Catheters, specifically, have a long history. They’ve been used since ancient times, with early examples dating back to around 3000 BCE. Over the centuries, they’ve transformed from simple tubes made of reeds or metal into medical devices designed for precision and patient comfort.

In this case study, we dive into how induction heating simulation can further improve catheters, making them safer and more reliable for patients and medical device manufacturers.

What’s the challenge?

When making catheters, internal stress can build up inside the material. This stress makes the catheter brittle, which is a big problem. It can cause the device to crack or break when pressure is applied during use. And we don’t want this to happen. This is especially important for the tip because it has to be sharp enough to pierce the skin but still flexible enough along the rest of the length not to break.

Typically, manufacturers use trial and error to solve this problem, creating lots of prototypes. It takes time and can get expensive fast. However, using modern simulation tools can speed things up and simplify the entire process. And this is exactly what we are going to do now.

RF induction heating simulation of a catheter tipping

RF induction heating is a smart way to control exactly how a catheter is heated, using electromagnetic fields. By carefully controlling the heat, we can reduce the internal stresses without weakening the catheter.

In our simulation, we fine-tuned exactly where and how much heat was applied by adjusting the induction coil’s length and position. We aimed most of the heat at the middle section, with just enough heat reaching the tip to keep it strong but not brittle.

Inside the simulation:

• Image 1: Shows the initial setup with the induction coil around the catheter. You can clearly see where the electromagnetic field is strongest—right around the catheter’s center. This helps achieve exactly the heating profile we need.

• Image 2: Here, the simulation shows how the temperature builds up along the catheter. You can see that it’s hotter in the middle and cooler at the tip, which is exactly what we want to maintain strength and flexibility.

Image 3: This wireframe view makes it easy to see the temperature distribution inside the catheter, highlighting how evenly and precisely the heat is controlled across its length.

Active Power

Looking at the Active Power graph, the heating starts steady and controlled. Around 30 seconds in, we gradually decrease the power to slowly cool the catheter. This slow cooling is important because it prevents the material from becoming brittle again.

Current Amplitude

The Current Amplitude graph follows a similar story. It stays stable initially, making sure that there is consistent heating. Once we’ve reached the ideal temperature, we slowly lower the current to match the cooling, maintaining the right properties throughout.

It is important to note that once we reach maximum temperature, we do not cool the material very fast, instead turning the inductor’s power down slowly.

What’s the result?

By using the simulation, we achieved exactly what we set out to do: a catheter tip that’s strong enough to puncture skin, with a body flexible enough to handle pressure without breaking.

Temperature details:

• Temperature-Distance graph: This graph shows the targeted peak temperature (about 223°C) in the middle of the catheter. Meanwhile, the tip stays cooler at around 144°C. This perfect balance keeps the tip strong and sharp.

• Temperature visualization: Another detailed look shows how gently and evenly the catheter cools down after reaching its peak heat. This controlled cooling process makes sure that the catheter stays flexible and durable.

With this simulation we wanted to demonstrate how engineers can quickly test and refine their designs, cutting down on costly trial-and-error methods. Manufacturers can make safer, better-performing catheters more quickly, saving time and resources. Simulation software is not a rocket science, it’s a simple yet powerful tool. And most importantly, the software is not only used by big corporations but it is accessible to companies with any size.

The post Catheter tipping case study: making better medical devices appeared first on CENOS : Simulation Software : Induction Heating : Radio Frequency : Wireless Charging.

To make the vice parts tough, a usual hardening process is necessary. Getting the heat treatment just right can be tricky. Usually, it takes a lot of testing, trial and error, and plenty of expensive prototypes to get things perfect.

But there’s also a simpler way. Using induction heating simulation, engineers can easily test and optimize how well a vice component will harden, all from their computer screen. Long before any prototypes are built. Engineers can improve the design and run as many tests needed until the perfect design is available.

Let’s see how this works.

Using FreeCAD with CENOS

Using simulation is simpler than you think. We have recently talked with Aleksander Sadowski. Turns out he’s a true FreeCAD enthusiast who designed a simple vice model, and he was particularly interested in using a simulation software to test that design and improve it. (He’s also doing FreeCAD training for engineers: alsado.de).

Why FreeCAD?

This is a widely known design tool that is fully compatible with CENOS. Just import the FreeCAD design to CENOS, set up the parameters, and run the simulation. So we combined drawing in FreeCAD and CENOS and got the results.

Here, we can see how the model was created in FreeCAD.

Making a vice that’s tough enough to withstand heavy use is tricky. The challenge is getting an even, reliable hardness on the surface. Traditionally, engineers had to guess and test repeatedly, wasting time and money on prototypes. It’s slow, costly, and there’s always a risk the final product won’t be as good as expected.

One of the biggest advantages of FreeCAD is its excellent compatibility with simulation software like CENOS. Engineers can easily import their FreeCAD models directly into CENOS, set simulation parameters, and quickly start running tests digitally without needing physical prototypes first.

Now that the design of a vice part is ready we can test it with simulation tool.

Running a simulation

Using induction heating simulation software, engineers can visualize exactly how heat will affect the vice component before making any physical parts. Here you can see couple of snapshots from the simulation software according to these FreeCAD designs we presented before.

The images show how the simulation reveals the hardness across the vice part. The colors show hardness levels measured in HV (Vickers Hardness). Bright reds and purples show areas with the highest hardness, which are perfect for surface durability. The blues are softer areas, meaning the heat didn’t reach deep into the metal, keeping the internal structure strong and flexible. Green and yellow colors show a gradual change between these extremes, confirming smooth transitions.

An additional feature in this simulation is that engineers can also include cooling. In the simulation animation below, you can see how cooling follows immediately after the induction coil moves over the part. This helps engineers control the hardening process precisely and prevents overheating or unwanted distortion.

What is the result?

Using simulation software, engineers find the best induction heating setup without building multiple prototypes. You can use the FreeCAD design tool to test and improve your designs using CENOS simulation software. FreeCAD is widely known and used amongst engineers, fully compatible with CENOS.

This digital-first approach speeds up the entire design process, reduces costs, and makes sure that you will produce consistent, high-quality vice parts every single time. Engineers can confidently move forward with production, knowing exactly what they’ll get. No surprises, no wasted effort.

The post How to use FreeCAD and CENOS simulation to improve surface hardening for vice components appeared first on CENOS : Simulation Software : Induction Heating : Radio Frequency : Wireless Charging.

The attached image materials in this case study show us visual insights into the evolving states of the solidification process under different conditions, with each snapshot demonstrating the changes between velocity, temperature gradients, and melt fraction.

What is the challenge?

Solidification in the presence of electromagnetic stirring is transient and requires precise simulation of hydrodynamics and electromagnetics. To achieve accurate results, it demands the following basics to be covered:

Transient complexity: Fully transient simulations for both hydrodynamics and electromagnetics are time-intensive and produce enormous amounts of data.

Steady-state assumptions: Steady-state simulations, while faster, can introduce inaccuracies if results are not carefully interpreted, as the steady state does not inherently reflect physical stability in solidification.

Iteration-dependent comparisons: With solidification evolving over iterations, comparison across simulations becomes unreliable without consistent parameters.

Simulation time: Transient simulations can take weeks, creating inefficiencies for engineers needing rapid results for real-time decision-making.

The challenge is to balance accuracy with computational efficiency to find a method that delivers meaningful insights in a shorter timeframe.

What is the solution?

A hybrid simulation approach was devised, combining steady-state and transient methods to balance speed and accuracy:

Steady-state initialization: Simulations begin with a steady-state solution to establish baseline hydrodynamic conditions in hours rather than days.

Transient refinement: These results serve as the initial condition for transient hydrodynamic simulations, allowing the system to stabilize further.

Selective transient electromagnetics: Transient electromagnetics are activated only when necessary, minimizing the computational load while capturing critical dynamics.

Iteration control: Steady-state simulations were artificially prolonged to advance solidification, enabling consistent comparisons across cases.

This tiered approach reduces the time needed for transient simulations while maintaining accuracy, with the steady-state results providing a practical foundation for further analysis.

Let’s look at the simulation visuals and explain what can we see.

The two images provided illustrate the solidification process in a crucible influenced by electromagnetic stirring (EMS). Below is a detailed analysis based on the key parameters visualized.

Steady-state simulation with stirrer coil voltage 2V

From steady-state results, it looks like more intensive stirring leads to better solidification. However, simulation with 2V finished at iteration 8481, but simulation with 10V was continued from the previous calculation with few restarts and the final result is displayed at iteration 21165.

Melt fraction

• The melt fraction ranges from 0 (fully solid) to 1 (fully liquid).
  • In the first image, the melt fraction near the electromagnetic stirring area is close to 1, indicating a mostly liquid state.
  • In the second image, the melt fraction shows a larger zone transitioning towards solidification, particularly near the edges of the crucible, where the value drops below 0.2.
• The melt fraction gradient reflects the efficiency of EMS in keeping the material near the stirring region in a liquid state, while peripheral areas begin to solidify as temperature gradients intensify.

Velocity magnitude

• Velocity magnitude ranges from 0 m/s to a maximum of approximately 4.22 m/s in the second image.
  • In the first image, velocities are relatively low, with the highest value near 0.35 m/s, indicating limited stirring and flow dynamics.
  • In the second image, velocities are significantly higher, especially near the electromagnetic stirring region, where they exceed 3 m/s, showcasing enhanced stirring effects.
• The stronger electromagnetic fields generate a more intensive stirring force, which in turn leads to increased velocity. We can see this in the second image, where mixing is improved. This improves mixing but could also contribute to higher thermal gradients.

Temperature distribution

• Temperature varies from 1152°C to 1767°C.
  • In the first image, the temperature is more uniformly distributed, with most of the melt above 1500°C, ensuring a homogeneous liquid state in the central region.
  • In the second image, the temperature gradient becomes more pronounced. The areas near the walls are cooler (below 1300°C), while the central stirring region remains above 1600°C.
• The temperature gradient corresponds to the transition from liquid to solid. The enhanced stirring in the second image creates a more localized high-temperature zone in the center while allowing cooler regions to solidify faster.

Overall dynamics

First image: Represents an initial, relatively uniform state with moderate stirring and limited solidification. The process is in the early stages, with melt fraction and temperature uniformity reflecting the dominance of the liquid phase.

Although steady-state solution has converged, we have to keep in mind that we are modelling a transient process. It means that, although each additional iteration gives very small contribution to the solidification front, overall after thousands of iterations, the changes can be quite noticeable.

Second image: The second image shows simulation, which has been continued from the previous calculation results. In this simulation we have increased coil voltage from 2V to 10V, thus strengthening the electromagnetic stirring intensity.

Here we can see that the increased mixing has disrupted the solidification and melted the already sold regions near the coils, where the electromagnetic mixing is the strongest. On the other hand, additional iterations have advanced the growth of the solid region further down the channel, where the electromagnetic mixing is no longer forcing the flow.

The following two simulation videos provide details about the solidification process under different conditions. Both simulations use electromagnetic stirring to influence the melt’s hydrodynamics, but they differ in duration and parameter focus.

First video (duration: 80 seconds): This video shows a harmonic transient simulation. It visualizes how stirring gradually impacts the melt over an extended period. The simulation highlights the interaction between electromagnetic forces and the melt dynamics, showcasing strong, oscillatory stirring patterns. The transitions are smooth and demonstrate how the melt reacts to varying electromagnetic fields.

Second video (duration: 10 seconds): The simulation focuses on transient conditions with fixed temperatures. It captures a more immediate and localized response to electromagnetic forces, emphasizing the interaction in a confined time window. The flow patterns are sharper, and the temperature gradients stabilize faster due to the fixed thermal boundary conditions.

What is the result?

The optimized workflow improves simulation efficiency while preserving the accuracy of results.

Time savings: Steady-state simulations reduced setup time from weeks to a few hours, with transient refinement completing in days instead of weeks.

Consistent results: Using steady-state results as an initialization point ensures that transient simulations converge more quickly and reliably.

Clear visual insights: The attached images illustrate the evolving states of the simulation. For example:

• Early steady-state results show uniform melt distribution.
• Advanced transient stages reveal the detailed velocity field, temperature distribution, and solidification patterns.

Improved decision-making: Engineers can confidently analyze solidification dynamics without waiting for prolonged simulations, enabling faster iteration on design and process optimization.

Conclusion and future direction

In the steady-state simulations, a harmonic time-averaged approach was applied to electromagnetic stirring (EMS) while solving the hydrodynamics in a steady-state framework. This method allowed for faster computation, achieving results within hours instead of the days or weeks required for fully transient simulations.

However, since solidification evolves over iterations in steady-state calculations, comparisons between different cases were only meaningful when performed at similar iteration levels to avoid misleading conclusions.

Additionally, while the steady-state results provide valuable insights into flow structures and temperature distributions, they do not inherently reflect a fully stabilized physical state, as solidification is a transient process by nature. To address this, the steady-state simulations were extended artificially to observe further solidification development.

The implications of this approach are significant: it enables engineers to quickly evaluate flow patterns, temperature gradients, and stirring efficiency before investing in computationally expensive transient simulations.

Moreover, using steady-state results as initial conditions for transient calculations accelerates convergence and enhances simulation accuracy, making it a practical and efficient strategy for optimizing electromagnetic stirring processes.

Credit: Illustrative image of continuous casting for this case study post is from “Fine Metal” article.

The post Optimizing solidification simulation in electromagnetic stirring applications: case study appeared first on CENOS : Simulation Software : Induction Heating : Radio Frequency : Wireless Charging.

Traditional charging methods are slow, costly, and labor-intensive, often requiring manual battery swapping and centralized charging hubs. If you drive an electric bike or a scooter, you’ve probably seen how empty batteries are collected and charged in some warehouses where batteries are plugged for charging. This type of traditional way is becoming outdated. Engineers and manufacturers need a solution that keeps up with the rapid growth of micro-mobility fleets.

The solution is wireless charging. Imagine how all the electric transportation is charged while they are parked in a designated area, or even charged while using it. But it’s not only about the right solution in the lab. The solution also has to fit the city environment and be easily built.

In this particula case study we wanted to explore the challenges of wireless charging (WCH) for micro-mobility vehicles and how simulation software provides the answer. We’ve had recently a talk with Tiler company CTO Joris Koudijs, and we were really inspired to write this case study and make a simulation of our own.

You’ll learn how companies like Tiler are using simulation to overcome alignment issues, optimize charging efficiency, and reduce costs.

If you’re an engineer or manufacturer looking to innovate your wireless charging solutions, this case study offers practical insights, examples, and possible results to help you stay ahead in the evolving urban transport market.

What is the challenge?

Urban transportation is undergoing a silent revolution. But it’s not that silent anymore, because you can see it happening around you. More people are choosing electric bicycles, scooters, and other micro-mobility solutions to navigate cities efficiently, hoping to skip those endless traffic jams and non-existent parking spaces.

But there’s a hitch – battery recharging is inefficient and labor-intensive. There has to be another solution. And this is what many companies are designing right now. A better way to charge batteries with less hassle.

This photo demonstrates how batteries are charged with a charging cable.

Just to draw you a picture, how does it work right now. Currently, workers manually collect depleted batteries, transport them to warehouses for recharging, and return them to vehicles. This process is costly, time-consuming, and unsustainable for growing fleets.

Companies like Tiler, a Dutch pioneer in urban wireless charging, recognize that the future lies in seamless Wireless Charging (WCH) solutions integrated directly into parking infrastructure.

This is one example how electric bicycle is charged in a designated post.

However, designing effective wireless chargers for micro-mobility comes with different kinds of challenges, and there are many of those. Here are few:

• Alignment issues: Precise coil alignment between the charger and vehicle is quite important. Even slight misalignments reduces efficiency.
• Design complexity: Space constraints in e-bikes and scooters limit coil placement options.
• Efficiency optimization: Ensuring fast, consistent charging without energy loss is technically demanding.

Engineers must design, test, and optimize these systems rapidly and cost-effectively. Traditional prototyping methods simply can’t keep up with the speed of innovation required today.

What is the solution?

This is where simulation software changes the business.

Simulation allows engineers to:

• Visualize and test coil alignment: Misalignment issues can be identified and corrected virtually, ensuring efficient charging even when vehicles are not perfectly docked.
• Optimize coil placement: Engineers can simulate different placements within limited spaces and choose designs that maximize charging efficiency.
• Save time and costs: Instead of weeks of physical prototyping, simulation provides answers in seconds.

Imagine you park the scooter to a designated post, and it will recharge while standing.

Companies like Tiler have successfully applied simulation to their wireless charging designs. Their goal is to embed chargers seamlessly into parking areas – imagine a charging plate on the ground where scooters or bikes rest, or a charger integrated into the vehicle stand itself.

Here’s the feedback from Tiler CTO Joris Koudijs (https://www.tilercharge.com):

Tiler explored multiple simulation tools, but found CENOS to be the most user friendly by far. It is really quick to setup multiple studies and iterate the design quickly. We also simulated the designs we have in production already and found very good correlation.

Simulation supported Tiler to improve their designs efficiently, addressing misalignment challenges and ensuring their charging solutions perform flawlessly in real-world conditions. There is still a lot of work to do, and using simulation software can help simulate different iterations of various ideas.

Tiler has managed to overcome the misalignment challenges with their coil design. They achieved an average K of 0.5 and Q of 700+ over a displacement of 3x the size of the receiver.

Tiler uses the vehicle kickstand as the receiver and a pavement tile as the transmitter. This puts an additional requirement for high misalignment performance.

Let’s look at the following images of the Tiler wireless charging solution.

A simplified simulation overview

Let’s look at the simplified wireless charging simulation using CENOS : WCH simulation software, demonstrating a rather simple interaction between a charger coil (transmitter) and a receiver coil (on the vehicle). It is a very close idea of how any charging spot could be solved for electric bicycles, scooters or other in an urban environment.

Simulation control setup

This setup controls the general simulation parameters for wireless charging.

Key parameters and their significance

Symmetry:

The full 3D model option accounts for all possible orientations and complexities, providing the most accurate simulation for real-world charging scenarios. Engineers can analyze the entire charging interaction without assumptions about symmetry.

Time:

Transient analysis helps engineers understand how the electromagnetic field evolves over time, crucial for evaluating dynamic charging conditions.

• Frequency: 12,000 Hz is typical for wireless power transfer systems, balancing efficiency and practicality.
• End time: 4 seconds
• Calculation time step: 0.25 seconds

Adaptive time steps allow for fine-tuning computational accuracy without excessive simulation times.

• Computation algorithm: The default algorithm optimizes performance for general use cases. Engineers can modify this for specialized simulations if needed.
• LTspice circuit simulation (unchecked): This option allows integrating circuit-level simulations. Leaving it unchecked simplifies the setup for purely electromagnetic field analysis.

The simulation shows the electromagnetic field distribution during the charging process, illustrating key design elements and potential challenges for wireless charging in urban environments.

Receiver coil setup

This image focuses on configuring the receiver coil for the simulation.

Key parameters and their significance

Material:

• Copper is the standard material for receiver coils due to its high electrical conductivity, which minimizes energy losses during wireless power transfer.

Electromagnetic domain properties:

• Number of turns (NtN_tNt​): 20. More turns increase the coil’s inductance, improving energy transfer efficiency but potentially increasing size and resistance.
• Number of strands (NsN_sNs​): 132. Multi-strand wires reduce the skin effect, which is critical at higher frequencies, ensuring more efficient current distribution.
• Strand diameter (ddd): 0.08 mm. Thin strands (0.08 mm) help mitigate losses due to skin effect, enhancing efficiency for high-frequency applications.
• Strand configuration: Circular strands.

Circuit options:

• Open circuit (checked): The simulation models the receiver coil as an open circuit to evaluate the coil’s electromagnetic behavior without load constraints.

There are a lot of benefits of using simulation software for product development. Engineers can validate design choices such as coil geometry, materials, and operating frequencies before prototyping, saving time and resources.

By adjusting parameters like frequency, number of turns, and strand diameter, engineers can strike a balance between efficiency, size, and cost.

What can we learn from the simulation?

Electromagnetic field distribution

• The image visualizes the electromagnetic field emitted by the charger coil. The red areas indicate high-intensity zones where the magnetic coupling is strongest, while the blue areas show diminishing intensity. Engineers can analyze these field distributions to make sure that there is efficient energy transfer from the charger to the receiver coil.

Coil alignment impact

• The visual highlights the importance of coil alignment for efficient charging. Even minor misalignments can result in energy losses or reduced charging efficiency. By simulating various alignment scenarios, engineers can design systems that maintain effective charging despite imperfect positioning.

Charging placement flexibility

This setup can be adapted for different charging locations, such as:

• Ground-level charging pads for scooters and bikes.
• Wall-mounted chargers for compact parking solutions.
• Charging posts integrated into urban infrastructure.

Material and design optimization

• The simulation allows product developers to test different materials and geometric configurations to achieve maximum efficiency and minimal energy loss before physical prototyping.

What engineers, product developers, and manufacturers can learn?

Optimizing coil design

• The different coil structures shown in these simulations demonstrate the importance of selecting the right coil geometry for specific use cases. Engineers can simulate and compare designs to determine which structure best suits their product’s application.

Designing for misalignment tolerance

• The broader field distribution in the second simulation suggests a design that can tolerate slight misalignments. This is crucial in real-world urban environments where perfect docking alignment is not always possible.

Performance across air gaps

• By analyzing simulations with varying air gaps, engineers can optimize the distance between charger and receiver coils to balance performance and practicality.

Reducing physical prototypes

• Simulation provides a cost-effective way to refine designs before physical prototyping. By visualizing electromagnetic interactions, developers can make informed decisions on coil placement, structure, and alignment.

What is the result?

The shift to wireless charging for micro-mobility isn’t just convenient – it’s transformative. For engineers and manufacturers, streamlined WCH means:

• Reduced operational costs: No more manual battery swaps.
• Scalable solutions: As urban fleets expand, wireless charging scales effortlessly.
• Better user experience: Riders simply park their vehicles, and charging happens automatically.

For this particular simulation case study, let’s analyze the graphs that simulation software provides. This graph provides four key graphs from a wireless charging simulation using CENOS Wireless Charging (CENOS : WCH) simulation software. The graphs illustrate critical parameters over a 4-second simulation period:

1. Q Factor (top left graph)

• The Q Factor (Quality Factor) indicates how efficiently the coils can store energy versus losing it to resistance.
• A high Q Factor for the receiver coil means it efficiently stores and transfers energy with minimal loss.
• The low Q Factor for the transmitter could suggest potential inefficiencies in energy delivery or high resistive losses.

Takeaway for engineers:

• Focus on optimizing the transmitter coil design to reduce resistive losses and improve the Q Factor.
• Make sure the receiver coil maintains its high Q Factor by using materials with low resistivity and optimizing geometry.

2. Mutual inductance (top right graph)

• Mutual inductance starts at around 7 µH and steadily decreases, reaching a low of ~1 µH at the 3-second mark before slightly recovering.
• Mutual inductance measures the efficiency of magnetic coupling between the transmitter and receiver coils.
• The decreasing mutual inductance suggests that the coils are moving out of alignment during the simulation, which reduces energy transfer efficiency.

Takeaway for engineers:

• Ensure precise alignment between the coils in real-world designs to maintain high mutual inductance.
• Design the system to tolerate misalignment by optimizing coil shape and placement.

3. Stray loss (bottom left graph)

• Stray loss increases initially, peaking at ~45 mW for the “All” category (total system) around the 2-second mark and then decreases.
• The Ferrite RX (receiver ferrite) and Ferrite TX (transmitter ferrite) contribute less to stray losses compared to the total.

Takeaway for Engineers:

• Minimize stray losses by optimizing the ferrite materials and ensuring tighter magnetic field containment.
• Address misalignment issues to reduce leakage fields during charging.

4. Coupling coefficient (bottom right graph)

• The coupling coefficient starts at ~0.45 and drops significantly to ~0.05 by the 3-second mark before slightly recovering.
• The coupling coefficient measures the efficiency of magnetic linkage between the coils (0 = no coupling, 1 = perfect coupling).
• The sharp decline indicates a loss of alignment or increased distance between the coils, reducing energy transfer efficiency.

Takeaway for engineers:

• Ensure designs maintain a high coupling coefficient by keeping coils in close proximity and well-aligned.
• Use designs that can tolerate misalignment to avoid drastic drops in efficiency.

Cities are evolving. Efficient wireless charging solutions for e-bikes, scooters, and other micro-mobility vehicles are key to supporting greener, smarter urban transport.

Companies like Tiler are at the forefront of this shift, and simulation is their secret weapon. Engineers who use simulation in their development process will solve today’s challenges easier and stay ahead in the race to shape the cities of tomorrow.

The post Using simulation software to improve micro mobility mechanics appeared first on CENOS : Simulation Software : Induction Heating : Radio Frequency : Wireless Charging.

However, designing such systems is no simple task. Engineers face challenges in efficiency, alignment, and thermal management, all of which demand innovative solutions. Although, we might get an impression that everything is evolving fast and with little effort, in reality these solutions take massive effort and time.

In this particular case study we will look into the complexities of wireless charging for industrial robots, with a special focus on large-scale vacuum cleaners. This particular case study is built around the industrial size vacuum cleaners to demonstrate the benefits of using simulation software.

By using simulation software, engineers and manufacturers can uncover solutions to different challenges, revolutionizing their approach to industrial design. Simulating gives an undeniable opportunity to test and improve the ideas that are still in the design phase, in the drawings, allow engineers to test before building an actual prototype of the product.

In the following case study, we’ll explore the critical challenges in designing wireless charging systems for industrial robots, examine how simulation tools address these obstacles, and showcase the transformative results of this approach.

Whether you’re an engineer seeking ideas into optimizing inductive coil designs or a manufacturer aiming to cut development costs, this case study offers actionable takeaways. By the end, you’ll have a clear understanding of how simulation simplifies the design process and enhances performance and reliability, making it an indispensable tool for the future of wireless charging.

What is the challenge?

In an era of advancing industrial automation, wireless charging for industrial robots, particularly large-scale devices like industrial vacuum cleaners (which this case study is focused), is no longer a futuristic concept but a demand of the today’s consumer.

Of course, innovation always bring different challenges.

And here are the few:

• Power transmission efficiency: Wireless charging systems for industrial robots (including large scale vacuum cleaners) require high efficiency to transfer substantial energy over varying distances. Any drop in efficiency can result in prolonged charging times, reducing operational productivity.
• Magnetic field interference: Due to their size and operation, industrial vacuum cleaners generate electromagnetic interference, making it challenging to establish a consistent and stable wireless energy transfer.
• Design complexity: Engineers must design inductive coils and charging systems capable of maintaining optimal performance despite variations in alignment, temperature, and operating conditions.
• Thermal management: Heat dissipation is critical during wireless power transmission to prevent system failures or degradation of components over time.

What is the solution?

Simulation software provides engineers and manufacturers a tool to overcome these challenges.

Now, let’s look into this specific simulation use case for the industrial vacuum cleaner. For this case study we used CENOS wireless charging simulation software (C : WCH). Let’s analyze the images to understand the role of simulation in designing wireless charging systems for industrial vacuum cleaners.

• Image 1

This image shows the magnetic field distribution around the inductive coils during charging. Engineers can observe areas of high and low magnetic flux density, identifying potential inefficiencies in coil alignment. By leveraging simulation, engineers can adjust coil design to optimize power transfer efficiency and reduce electromagnetic interference.

• Image 2

Here, we see a detailed visualization of electromagnetic field lines interacting with the vacuum cleaner’s charging pad. This helps engineers make sure that the system achieves consistent alignment and stability during power transfer. Simulations can highlight misalignment issues, allowing for design corrections before physical prototyping.

• Image 3

The following simulation image demonstrates the external geometry of the vacuum cleaner along with the charging system’s electromagnetic interactions. By examining these visualizations, engineers can refine the placement and orientation of charging components, ensuring thermal management and maximizing energy efficiency.

• Image 4

The final image focuses on the charging system’s vertical magnetic flux distribution. Engineers can use this data to balance power delivery across the entire system, avoiding hotspots or uneven energy distribution that could lead to inefficiencies or damage.

Through these simulations, manufacturers can save significant time and resources. Virtual testing allows for the systematic improvement of charging systems without the expense of repeated physical prototypes.

What is the result?

By using simulation software to design wireless charging systems for industrial robots and in this case vacuum cleaners, engineers can see several benefits. And here are the few to point out:

• Enhanced efficiency: Optimal coil design and alignment reduce energy losses, ensuring fast and reliable charging.
• Reduced development costs: Virtual prototyping minimizes the need for physical prototypes, cutting costs and speeding up the development timeline.
• Improved reliability: Simulations identify potential issues, such as electromagnetic interference or thermal overload, enabling engineers to address them early in the design process.
• Scalability: Simulation insights pave the way for the broader adoption of wireless charging in other industrial applications, from forklifts to automated guided vehicles.

In conclusion, the integration of simulation software in the design process for wireless charging systems transforms challenges into opportunities. For engineers and manufacturers tackling the complex demands of industrial-sized devices like in this particular case the vacuum cleaners, these tools are not just advantageous, but they are rather essential.

The post Wireless charging of industrial robots: case study appeared first on CENOS : Simulation Software : Induction Heating : Radio Frequency : Wireless Charging.

With production cycles lasting up to seven hours, companies face pressure to improve throughput while maintaining uncompromising quality. This is where induction heating technology enables faster and more efficient production processes.

This case study dives deep into the induction heating of press moulds, an important step in transitioning from autoclave processing to faster Out-of-Autoclave (OoA) methods. Conducted by Frank van Duin at Fokker Aerostructures, the study explores how simulation software like CENOS can help engineers design optimal moulds, reduce material waste, and cut production cycles.

Through real-world experimentation and comparative analysis with industry-leading tools like Abaqus, this study demonstrates the effectiveness of simulation and provides insights for engineers aiming to refine their processes and stay ahead in the competitive aerospace sector.

What is the challenge?

Induction heating of press moulds presents unique challenges, particularly in the aerospace industry where precision and efficiency are paramount. Traditional methods like autoclave processing are effective but highly time-consuming, taking up to 7 hours to process large thermoplastic parts such as skins and spars.

This inefficiency impacts production cycles and costs, especially for companies dealing with expensive and complex materials like thermoplastics, which must endure extreme temperatures, resist corrosion, and remain lightweight.

Key challenges are the following:

• Material costs: Aerospace-grade thermoplastics are expensive, and inefficient processes lead to significant wastage.
• Prototyping complexity: Designing the right mould often requires multiple prototypes, further increasing costs and time.
• Heating pattern optimization: Achieving uniform heating patterns to maintain material properties is critical but challenging without proper tools.
• Large-scale feasibility: Scaling processes from lab setups to full-scale production demands meticulous planning and testing.

What is the solution?

Simulation software, particularly platforms like CENOS, offers a transformative solution for overcoming these challenges. By enabling engineers to design and test moulds virtually, it eliminates much of the trial-and-error process, saving both time and resources.

Highlights of the solution:

• Accelerated production cycles: Simulations help optimize the induction heating process, reducing production time from 7 hours to just 30 minutes in controlled environments.
• Cost efficiency: Virtual prototyping minimizes material wastage and reduces the number of physical prototypes required.
• High precision: Tools like CENOS allow engineers to predict heating patterns with remarkable accuracy, ensuring the desired material properties are retained.
• Easy adaptability: With user-friendly templates and support for nonlinear material properties, CENOS caters to both new and experienced users. Its compatibility with open-source pre- and post-processors adds flexibility.
• Comparative validation: In experiments by Frank van Duin at Fokker Aerostructures, CENOS delivered results comparable to industry leader Abaqus, but at a fraction of the cost and with enhanced usability.

Simulation steps for induction heating of press moulds:

1. Define thermoplastic and mould material properties.
2. Estimate power requirements and design coil geometry.
3. Simulate and analyze heating patterns to identify the optimal configuration.
4. Manufacture mould and inductor based on optimized designs.

Let’s look at the following two types of press moulds and how the simulation visually highlights important aspects of the heating mechanism and electromagnetic interactions, providing engineers with actionable insights.

A. Pancake type of press mould

Heating pattern visualization:

The color gradient across the mould indicates the temperature distribution, with warmer colors (yellow/orange) representing higher temperatures and cooler colors (purple/black) showing areas of lower heat. This visualization helps engineers identify the uniformity of heat across the mould and pinpoint any areas that may require design adjustments for consistent heating.

Magnetic field lines:

The blue magnetic field lines around the coil demonstrate how the electromagnetic field interacts with the mould. These lines offer engineers insights into the efficiency and concentration of the magnetic field, helping them understand how energy is being transferred and whether the design achieves optimal electromagnetic coupling.

Coil configuration and geometry:

The 3D representation of the coil structure showcases the layout and positioning of the inductors. Engineers can evaluate whether the geometry aligns with the heating requirements and decide whether alternative designs (e.g., pancake or meander types) could provide better results based on specific mould shapes or material properties.

Electromagnetic induction efficiency:

The simulation highlights areas of potential inefficiency, such as uneven heating or electromagnetic leakage, which engineers can address through iterative design changes. By adjusting parameters like coil spacing, frequency, or material conductivity, engineers can optimize performance.

Material interaction and performance:

Engineers can assess how different materials (e.g., ferrite blocks or thermoplastics) interact with the electromagnetic field, providing critical data for selecting materials that enhance heating efficiency while minimizing energy losses.

B. Meander type of press mould

Coil design geometry:

The image illustrates a meander-style coil, which is ideal for larger or irregularly shaped moulds. Its linear structure allows for even coverage across the surface, ensuring that heat is distributed more uniformly compared to other designs like pancake coils.

Localized heating optimization:

The design’s flexibility enables engineers to incorporate ferrite blocks at strategic points where currents run in opposite directions. These blocks enhance magnetic field concentration in areas prone to uneven heating, minimizing inefficiencies caused by conflicting electromagnetic interactions.

Heating precision:

The meander design provides control over specific zones of the mould, making it particularly suitable for applications requiring differential heating or tailored thermal profiles. Engineers can fine-tune the coil layout to align with specific material or process requirements.

Design simplicity and scalability:

While the meander-type coil is relatively straightforward to manufacture, it is also scalable. This simplicity makes it a practical choice for industries like aerospace, where precision and repeatability are essential.

Energy efficiency challenges:

The possibility of ferrite block usage comes with trade-offs. While these blocks enhance localized heating, they may absorb some heat, reducing overall system efficiency. Engineers can use this insight to balance the benefits of localized heating against potential energy losses.

Adaptability for complex moulds:

The meander design’s adaptability allows for effective use in complex mould geometries that require varying thermal zones. This makes it a versatile option for applications demanding intricate and precise heating.

What is the result?

The impact of using simulation software like CENOS for induction heating of press moulds is profound, especially for engineers and manufacturers aiming for efficiency and precision.

Key outcomes:

• Time savings: Production cycles for thermoplastics have been cut from hours to minutes, enabling faster delivery and reduced downtime.
• Improved quality: Accurate simulations ensure uniform heating and cooling, preserving the mechanical properties of thermoplastic composites.
• Lower costs: The reduced need for physical prototypes and materials significantly decreases production expenses.
• Scalable solutions: The insights gained from simulations can be directly applied to large-scale production, addressing the challenges of scalability.

For example, two coil designs were tested:

• Pancake inductor design: Ideal for homogeneous heating, though manufacturing tolerances can impact performance.
• Meander inductor design: Suitable for complex moulds, with ferrite blocks enhancing heating in conflicting areas but potentially reducing efficiency over time.

Why is it important?

By using simulation in the induction heating process, engineers are equipped to tackle the challenges of modern aerospace manufacturing with confidence. This approach not only advances production efficiency but also reinforces the industry’s commitment to sustainability and innovation.

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It is more energy-efficient and inherently more environmentally friendly than most other heat sources for steel billets. Manufacturers aim to achieve high productivity, low cycle times, and repeatable quality—all while minimizing energy consumption and environmental impact.

In this case study, we’d like to demonstrate a simulation example on how to optimize a progressive induction heating system for a steel billet using CENOS, a 3D simulation software, which is focused specifically on induction heating and uses open source components and algorithms, making it affordable for small and medium companies.

What is the challenge?

CENOS simulation software is capable of simulating various types of induction heating for forging. It is possible to simulate both static heating and progressive heating where the billet is moved through the coil with constant velocity.

The coil design is also of no limitations – single coil and multi-coil are possible to simulate. Besides the coil, it is also possible to simulate any material and frequency. Common challenges include subsurface overheating, inefficient energy use, and inconsistent results. Engineers must navigate complex parameters like coil design, frequency selection, and material properties, making manual adjustments time-consuming and costly.

The challenge grows when comparing multiple system designs. For example, how do you determine whether a two-stage or three-stage heating system offers better performance without costly trial-and-error? This is where using simulation software offers a solution.

What is the solution?

CENOS, a 3D simulation software, is tailored to solve induction heating challenges by empowering engineers with powerful computational tools. Designed for ease of use—even for beginners—CENOS simplifies the complex physics of induction heating into a streamlined three-step process:

The simulation process consists of three steps:

• Choose the workpiece geometry (from built-in templates or create your own CAD file).
• Define induction heating parameters (frequency, voltage, time, etc.).
• Run 2D or 3D simulation of your choice.

In the end, results like temperature and magnetic field are displayed in 3D renderings, plots, and more.

Apparent power, induced heat, and inductance are logged into an Excel file.

3D Simulation example – comparison of two heating systems

Here, the progressive heating of the billet is simulated, with two systems under consideration – two-stage and three-stage systems.

• The target for the simulation was to reach 1200 ℃ ± 50 ℃.

To check both systems, the user had to create a setup for both of them, set physical parameters (material properties, frequency, current, etc.) and start the simulation.

After the simulation is done, the user has access to different output variables:

• Temperature distribution
• Current density and Joule heat distribution
• Magnetic field lines
• Total, reactive and apparent power
• Inductance of the coil
• Coil current, voltage
• etc.

In our example of billet heating is possible to compare both cases and the output.

It is visible how a three stage system can decrease the power consumption and increase the production rate for this specific case.

It is also possible to plot the distribution of temperature, Joule heat, magnetic field etc. A resulting temperature distribution in the billet across the radius is shown in Figure 1. As can be seen, better temperature homogeneity is obtained in the three stage system.

Figure 1. Temperature distribution along the billet radius at the outlet of the heating system

Figure 2. Temperature distribution in the long billet during scanning (progressive) induction heating.

Figure 2 shows how different systems lead to different temperature distribution. In two stage system, temperature required for forging is reached with shorter coils, thus also with smaller scanning speed. This leads to worse temperature uniformity and smaller production rates.

On the other hand, three stage heater gradually increases the temperature of the billet and the resulting temperature difference between core and surface is smaller.

CENOS users are free to change all the input parameters and assemble the system of any number of stages required for their process.

In case if the same system has to be used for scanning of shorter billets where end effects play a more significant role, it is possible to set up a simulation with moving billet. An example of temperature dynamics in such simulation are shown in GIF images below:

Two stage system

Three stage system

What is the result?

As demonstrated in the simulation example it is possible to compare two different systems and get results that make decisions easier thus saving valuable company resources. Simulation helps make better decisions for production set-up and planning.

The scope and variety of different simulations is unlimited, all depends on what problem user wants to solve:

• Heating system design to optimize induction heating performance, improve product quality, and avoid unpleasant surprises related to subsurface overheating
• The selection of power, frequency, and coil length in induction billet heating applications
• The selection of right forging temperatures for plain carbon and alloy steels to avoid possible damage by incipient melting or overheating.

This use case shows the potential of simulation software in improving the manufacturing industry. With tools like CENOS, engineers are equipped to overcome the complexities of induction heating, paving the way for innovation.

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However, rather than discarding damaged blades, modern renewal techniques allow engineers to repair them by reshaping the tips. The critical first step in this process is heating the damaged tip to a uniform temperature, enabling precise reshaping to restore its original form and functionality.

The simulation featured in this case study highlights the application of induction heating to achieve this goal. Unlike preheating for welding, the primary objective here is to ensure even temperature distribution across the blade tip for optimal reshaping.

By using simulation technology, engineers can overcome the inherent challenges of heating complex geometries and develop efficient, scalable solutions to renew turbine blades for continued use in aircraft engines.

This case study explores the challenges, solutions, and results of this innovative application.

What is the challenge?

Turbine blades are engineered for reliability, operating under high pressure and temperatures. However, during operation, small debris and particles can strike the blades, particularly their tips, causing deformation or damage. This damage disrupts airflow, reduces engine efficiency, and risks further operational issues. To renew the blade, its tip must be reshaped to its original form. This reshaping process begins with heating the damaged area to a uniform temperature.

The challenges in this process include:

• Achieving uniform temperature distribution: The blade’s intricate geometry and material properties make it difficult to ensure consistent heating, especially at the tip.
• Preventing material defects: Overheating or uneven heating can weaken the blade’s microstructure, leading to cracks or reduced durability during operation.
• Efficiency and speed: Traditional trial-and-error methods for heating optimization are slow and inefficient, delaying repair timelines.
• Scalability: Aircraft engines have hundreds of blades, requiring repeatable and reliable heating processes.

Without precise control, these challenges can result in poorly repaired blades, making them unsuitable for reuse.

What is the solution?

The solution lies in leveraging simulation-driven induction heating processes. Simulation software enables engineers to model and optimize the heating process virtually, accounting for the blade’s geometry, material properties, and thermal requirements.

Using simulation tools:

• Engineers can design optimized inductors: By virtually testing coil designs, engineers can ensure uniform temperature distribution across the blade tip, reducing hotspots or cold spots.
• Simulations provide precise thermal control: Advanced algorithms predict how electromagnetic fields interact with the blade, achieving consistent heating patterns.
• Time and cost are minimized: By simulating the entire process, engineers can eliminate costly physical prototypes and accelerate repair workflows.

Let’s take a look into the simulation, using CENOS Induction Heating simulation software.

In this quick animation, we actually demonstrate 10 design iterations. You can see the coil shape design changes and how it affects the heating process. With CENOS this is a quick and simple automated process. You can use virtually any CAD modeling software of your choice.

The colors indicate the areas of uniform heating across the blade tip, ensuring the material is evenly prepared for reshaping. The virtual design allowed for optimal inductor placement, reducing energy waste and avoiding overheating sensitive areas.

In the first iterations, you can see that the first coil design heated the trailing edge of the blade way too much. It may not be intuitive how much larger the coil should be, but simulation helps you to determine the exact size.

As you can see, the inductor around the trailing edge had to be made significantly larger to achieve uniform temperature. Due to more uniform heating, the heating time was also reduced from 125s to 50s thus saving the welder’s time.

This also saves the machinist’s time. While this inductor is relatively simple, testing different geometries in the lab would be much, much more time consuming and expensive. Simulation also allows to quickly test other turbine blade geometries with the same inductor.

By simulation software, engineers can:

• Optimize inductor design: Quickly iterate and refine coil designs to achieve uniform heating.
• Predict temperature distribution: Accurately model heat flow within the blade, eliminating trial-and-error guesswork.
• Save time and resources: Complete optimization in hours instead of weeks, reducing costs and speeding up production cycles.

The simulation software ensures that every detail, from the blade’s geometry to its material properties, is factored into the design. This process not only improves efficiency but also minimizes the risk of defects during welding or other downstream processes.

Analysis of the simulation

Let’s look into some more details of this simulation.

Magnetic flux density in the induction heating process

Key insights:

• This image illustrates the magnetic flux lines generated during the induction heating process around the turbine blade.
• The intensity of the magnetic flux density is color-coded, showing areas of higher flux concentration near the inductor coils.
• Engineers can identify how the electromagnetic fields interact with the blade, pinpointing regions of effective heat transfer.

Value for engineers:

• Visualizing the magnetic flux density helps engineers optimize the placement and geometry of inductor coils for uniform heating.
• It provides insights into reducing energy losses by minimizing areas of excessive flux leakage or hotspots.

Heat distribution and current density visualization

Key insights:

• The image displays the thermal distribution on the turbine blade’s surface, with a gradient indicating temperature changes from the base to the edges.
• Current density is overlaid, showing how electric currents flow through the material to generate heat.
• Areas of high temperature correspond to regions of higher current density, emphasizing the role of induction currents.

Value for engineers:

• Engineers can evaluate whether the heating is uniform across the blade’s complex geometry.
• It assists in determining the effectiveness of inductor design and material conductivity in achieving desired temperature profiles.

Visualization of current density and flow

Key insights:

• This image provides a more detailed view of the current density distribution within the turbine blade, overlaid with directional flow lines.
• The gradient highlights regions where eddy currents are most intense, which are crucial for localized heating.
• The direction of current flow helps engineers understand the interaction between the inductor’s magnetic field and the workpiece.

Value for engineers:

• This visualization allows engineers to refine inductor coil configurations to avoid areas of insufficient or excessive heating.
• It aids in troubleshooting issues such as uneven temperature distribution or energy inefficiency by identifying problematic areas in the design.

What is the result?

By using simulation-driven induction heating processes, manufacturers can renew turbine blades with exceptional precision and efficiency. The results speak for themselves:

• Uniform heating: Damaged blade tips are heated to consistent temperatures, ensuring the reshaping process restores the blade to its original condition.
• Extended blade lifespan: The risk of microstructural damage is minimized, maintaining the blade’s integrity and extending its operational life.
• Faster turnaround times: Simulation reduces repair time by enabling engineers to perfect the heating process virtually, saving weeks of trial-and-error adjustments.
• Cost savings: Reusing turbine blades instead of manufacturing replacements significantly reduces costs for aircraft operators.

This approach is a good option for industries where turbine blade reliability is non-negotiable, such as aerospace and power generation. By combining induction heating with simulation technology, engineers can confidently repair damaged components, keeping aircraft engines efficient and operational.

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There are various models on the market like FAG PowerTherm heaters by Schaeffler Group, Portable heaters by NSK Maintenance Tools, TIH series by SKF Maintenance Products, etc.

Yoke-style bearing heaters are designed with a yoke that is placed through the bearing bore. Many bearing heaters have different size yokes which can be changed to accommodate various bearing sizes. Some of them are purposed for extra-large bearings.

Hot plate style bearing heaters have a flat heating surface. Hot plates are typically used for heating smaller bearing sizes.

Cone-style bearing heaters are, as the name implies, cone-shaped, and have a stepped design to accommodate a range of bearing sizes. The coil heats the bearing, and the cone acts as a thermal insulator.

Traditional trial-and-error methods to optimize these heating devices are time-consuming, costly, and often fall short of achieving the desired results. This is where simulation software steps in, and offer engineers the tools to design smarter, safer, and more efficient induction heating systems.

This case study explores the challenges of ball-bearing induction heating, showcases how simulation software can offer a solution, and highlights the results of adopting a simulation-driven approach.

What is the challenge?

Ball-bearing induction heating is a complex process where bearings must be heated uniformly and efficiently to process their mounting or dismounting without causing damage to the components.

The typical challenges in the process are:

• Material constraints: Medium carbon steel bearings require specific heating parameters. Poorly selected materials or improper parameter settings can lead to uneven heating, risking structural integrity.
• Heat transfer inefficiency: Bad designs for induction coils can result in suboptimal heat transfer, leading to prolonged heating times or failure to reach the target temperature.
• Safety concerns: Overheating coils or lack of temperature control mechanisms can compromise device safety and efficiency.
• Design variability: The wide variety of bearing sizes and heater styles, including yoke-style, cone-style, and hot plates, necessitates adaptable solutions for engineers and manufacturers.

These challenges underline the need for precise modeling and optimization of the induction heating process to guarantee reliability, efficiency, and safety.

What is the solution?

Simulation software provides engineers and manufacturers with a powerful tool to model, analyze, and optimize ball-bearing induction heating processes.

Let’s look at the simulation with the following parameters:

• Bearing material: medium carbon steel
• Recommended heating temperature: 110 °C
• Current: 8 A
• Frequency: 24 kHz
• Heating time: 3 min
• Insulator material: Glasschaum-Granulat Case A
• Coil type: Stranded coil

The right temperature heating recipe was discovered by the means of simulation.

By using simulation, you can address the challenges mentioned above with precision and efficiency:

• Accurate heating recipes: CENOS helps determine the optimal heating temperature (e.g., 110°C) and time (e.g., 3 minutes) for bearings, offering a uniform thermal expansion and preventing component damage.
• Material and geometry customization: Whether you’re working with yoke-style, cone-style, or hot plate heaters, CENOS allows you to model various coil geometries and materials, tailoring the design to your specific needs.
• Safety and performance validation: Through simulation, you can calculate coil temperature and current parameters, such as 8 A at 24 kHz, to avoid overheating and ensure device safety.
• Real-time insights: The software enables users to visualize the heat distribution across the bearing and coil. For example, our simulation of a cone-style heater revealed inefficiencies when suboptimal materials were used, providing actionable insights for improvement.

Now, on the following simulation, we can see the ball-bearing and induction heater coil overheating. You can see what happens if materials are chosen poorly. The heat from the induction coil barely reaches the bearing, the device is not optimized for normal function.

After changing some of the parameters, and choosing the best materials, you can see the end result of well-built, functional bearing heater, which you can use to improve actual real-life devices.

Overheating simulation

Analyzing this scenario, engineers can learn how adjusting parameters such as coil material, geometry, current, and frequency can improve heating efficiency. The simulation emphasizes the importance of material compatibility and precise design in achieving uniform heat distribution and preventing coil overheating.

These findings allow engineers to improve their designs and build high-performing, reliable devices for industrial use.

What is the result?

Ball-bearing induction heating, when optimized through simulation, delivers number of advantages to engineers and manufacturers, for instance:

• Reduced heating time: Achieve precise heating in just 3 minutes, minimizing downtime and increasing productivity.
• Improved device longevity: Optimized parameters prevent overheating, extending the lifespan of induction heating equipment.
• Tailored solutions for any application: From small bearings on hot plates to extra-large bearings on yoke-style heaters, CENOS equips you to tackle diverse industrial needs.
• Cost savings: By designing efficient heating devices with the right materials and parameters, manufacturers can reduce energy consumption and maintenance costs.

Engineers who adopt simulation software can solve complex problems before building physical solutions, and that way lead innovation in their industries with fewer resources.

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In this particular case study we analyze how using simulation software can improve the process of induction scanning hardening of a shaft.

Unlike single-shot hardening, where heating and cooling are done in a row, induction scanning requires simultaneous heating and cooling cooing to follow the heating with the spray shower attached next to the inductor, which moves along the shaft. The dynamic nature of this process demands some multi-step control over factors like frequency, speed, and power.

Additionally, the cooling system, including the design of the spray shower, must be precisely engineered to achieve the desired hardness profile.

What is the challenge?

In traditional approaches, these parameters are tested through a make-and-test process. Engineers harden a prototype, then cut it open to study the hardness profile. If the profile doesn’t meet specifications, the material is scrapped. This approach is a costly, time-intensive process that can generate a significant amount of waste. The challenge, therefore, is to optimize each step and minimize scrap material without compromising on quality.

This video shows what the typical shaft hardening process looks like.

(Video credit: YouTube channel of Heat Treating Corp).

What is the solution?

CENOS Induction Heating simulation software eliminates the need for physical prototyping by allowing engineers to simulate the entire induction scanning hardening process. By importing CAD files of complex geometries, such as a splined shaft, engineers can set up and run simulations in less than two hours (depending on size and complexity of the part, as well as the computational power at user’s computer).

This includes processing the CAD geometry, setting up the symmetry boundary conditions for accurate replication, and adjusting parameters for optimal hardening.

Here’s how the simulation is set up for this particular case study

The software offers engineers the flexibility to simulate with varied parameters like power, frequency, and scanning speed, and do that multiple times within a single day. In one test case, a shaft with 18 splines was simplified by creating a half-shaft model, which significantly reduced calculation time without sacrificing accuracy.

The simulation even incorporates differential heat transfer rates for the heating and cooling sections, allowing precise control with coefficients such as 5 W/(m²·K) (which describes the natural convection process) for heating and 100 W/(m²·K) (which describes spray-showering with given parameters) for cooling.

What can engineers read from the simulation?

• Temperature gradient

The simulation shows a clear progression of temperature from the heated section (red) to the cooler areas (blue). This gradient indicates how effectively the heat is applied and dissipated.

• Cooling dynamics

The transition from red to blue near the cooling zone shows the efficiency of the cooling system (e.g., spray showers). Engineers can analyze the rate at which the material cools and ensure it aligns with the desired hardening process.

• Symmetry and uniformity

The symmetrical heat distribution around the shaft can be evaluated to ensure uniform hardening. Any anomalies in the heat profile could indicate design issues with the inductor or cooling system.

What kind of decisions can be made?

• Hardness profile prediction

The temperature distribution can help engineers predict the resulting hardness profile of the shaft. Regions heated to a specific range are expected to harden to a particular depth and hardness level.

• Optimization of parameters

Engineers can use this simulation to fine-tune parameters such as power, frequency, and scanning speed. For instance, ensuring the red zone reaches the desired temperature quickly while maintaining uniformity.

• Energy efficiency assessment

The efficiency of energy transfer from the inductor to the shaft can be analyzed. Engineers can identify areas where heat is lost or unevenly distributed, enabling improvements to the process.

• Cooling system effectiveness

The rapid shift in temperature near the cooling zone can be used to assess the cooling system’s performance. Engineers might analyze whether the spray shower or other cooling mechanisms are optimally positioned and effective.

• Material behavior

The simulation can show how different materials react to the induction heating and cooling process. This is particularly useful when selecting materials for specific applications.

• Process validation

Comparing simulation results to experimental data validates the process, reducing the need for costly physical prototyping.

The power of CENOS lies not only in saving time but also in enabling engineers to keep within available power supply limits by defining voltage and current sources for each setup. This control ultimately makes sure that each process stays efficient, reliable, and within budget.

The light-colored section of the shaft represents the hardened region, showing the area affected by the induction heating process. This visualization allows engineers to verify whether the hardening is uniform and meets the desired specifications.

Engineers can assess whether the hardened area matches the required depth and shape based on the operational parameters. This is critical for ensuring mechanical properties such as strength and wear resistance.

• Magnetic field lines

The blue lines depict the magnetic field generated by the induction coil. These lines demonstrate how the magnetic field interacts with the shaft, showing areas of higher field density where energy transfer is most concentrated.

• Field intensity distribution

The density of the magnetic field lines near the coil indicates regions of higher magnetic field intensity, which correlates directly with heat generation in the shaft. This information is vital for ensuring the hardening process is uniform.

• Inductor design

The orange-colored induction coil shows how the coil geometry affects the magnetic field distribution. Engineers can assess whether the coil design is optimal for uniform heating of the shaft.

• Field interaction with the shaft

The magnetic field’s penetration into the shaft material is visible. This determines the depth of heating and, consequently, the depth of hardening.

What is the result?

With CENOS simulation software, the induction scanning and hardening process of a shaft can be fine-tuned to perfection. And it can be done within hours rather than weeks. Engineers gain immediate insights into the hardness profile achieved at specific operating parameters.

For instance, using a two-turn inductor with a flux concentrator, one simulation produced a precise hardening profile after just 15 seconds of heating at 10 kHz, 6 kA, and 10 mm/s scanning speed. This level of control not only accelerates the prototyping phase but also ensures that manufacturers produce higher-quality components with minimal waste.

In this particular simulation we see the following results from these graphs:

• Active power

The graph shows total power stabilizing around 10.5 kW, with the workpiece consuming approximately 5 kW and the inductor around 5.5 kW. We see that each inductor (and we have 2) consumes just below 6 kW, and so does the workpiece, which makes resulting efficiency just around 33%, which is not very good. This shows efficient energy transfer to the workpiece, as total power closely aligns with the sum of individual components. Engineers can use these values to pinpoint where energy losses may occur.

• Inductance

Inductance fluctuates between 13 nH and 17 nH for different terminals during the process. Terminal 1 peaks at 17 nH, while Terminal 11 stabilizes near 13 nH after 1 second. These shifts show varying electromagnetic coupling, helping engineers assess coil design and material behavior during heating.

• Current amplitude

The current amplitude stabilizes near 8 kA for Terminal 1 and fluctuates slightly after 2 seconds, remaining within ±0.5 kA. This steady current ensures consistent heating. Engineers can monitor these values to maintain process stability and avoid overheating.

• Voltage amplitude

Voltage amplitude rises quickly to about 16 V for Terminal 1 and stabilizes around 14 V after 1 second. Terminal 11 follows a similar trend but stabilizes closer to 12 V. These consistent voltage levels confirm stable energy transfer and reliable power delivery for the process.

For engineers, this case shows how using a simulation software can transform the hardening process, reducing material waste, saving time, and improving process efficiency. As industries push for greater sustainability and faster time-to-market, a proper simulation software bring precision and power to their design and production workflows.

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