Magnetic Materials in Engineering Design

For PCB motors, NdFeB permanent magnets are the first-choice rotor material, forming an axially fluxed structure with PCB stators to deliver high torque density, compact size, and high efficiency. Their design matching directly determines the motor’s performance ceiling. Core Working Logic Stator windings on PCB substrates generate rotating magnetic fields; NdFeB rotor magnets interact with these fields to output torque. High magnetic energy product of NdFeB enables miniaturization while maintaining strong torque output. Key Design Matching Points • Grade Selection: Use N35–N52 for standard applications; N45SH/N52SH for 120–150°C environments; UH/HH grades for ≥180°C scenarios to avoid irreversible demagnetization. • Shape & Arrangement: Wedge/arc shapes optimize field distribution; Halbach arrays boost air-gap flux density by ~30% and reduce cogging torque. Segmented, staggered, radially magnetized arrangements minimize eddy current losses. • Pole & Air-Gap Design: 4–16 pole pairs balance speed and torque; air gap of 0.3–1.0 mm balances flux leakage and torque density (no core allows flexible tuning). • Thermal & Reliability: NdFeB’s Curie temp (~310°C) requires thermal matching with PCB windings; apply surface coating (e.g., electrophoretic deposition) for corrosion resistance and insulation. Design Collaboration Tips 1. Match NdFeB’s remanence with PCB winding turns and copper fill rate to maximize torque and efficiency. 2. Combine thin NdFeB magnets with coreless stators for ultra-flat profiles, reducing volume by 30%–50%. 3. Integrate drive circuits/sensors on PCB substrates, ensuring magnet mounting compatibility to simplify system design. Typical Application Combinations • UAVs/precision instruments: N52 thin ring magnets + 6–8 layer PCB windings (lightweight, high torque density). • Industrial robots: N48SH wedge magnets + Halbach arrays + 10+ layer windings (high precision, thermal stability). • Medical devices: Segmented NdFeB + low-cogging PCB stators (low noise, smooth operation). Video from #MotorMaterial

Unlock the full potential of your BLDC motors by optimizing a key player: the magnetic circuit. 🧲 Ready to learn how? Follow me. In my previous posts, we talked about the basics of BLDC motors and their design basics. Today, let's talk about the crucial aspect of BLDC motor design: optimizing the magnetic circuit, to greatly enhance motor efficiency and performance. The magnetic circuit is the pathway through which magnetic flux flows within the motor. Optimizing this circuit directly impacts the motor's efficiency, power output, and overall performance. Magnetic Circuit Components? 🎯 Stator: The stationary part that produces a rotating magnetic field. 🎯 Rotor: The rotating part with permanent magnets that follows the stator’s magnetic field. 🎯 Air Gap: The small space between the stator and rotor, is critical for magnetic field interaction. Steps to Optimize the Magnetic Circuit 1️⃣ Material Selection 🧲 Use high-quality magnetic materials with low core losses for the stator and rotor. Materials like silicon steel are commonly used due to their excellent magnetic properties. 🧲 For the rotor, choose strong permanent magnets like neodymium magnets to ensure a robust magnetic field. 2️⃣ Designing the Stator and Rotor 🧲 Stator: Ensure the stator has the right number of slots and windings to generate a strong and consistent magnetic field. 🧲 Rotor: Arrange the magnets on the rotor in a way that maximizes the interaction with the stator’s magnetic field. Proper alignment is key. 3️⃣ Minimizing the Air Gap 🧲 Keep the air gap between the stator and rotor as small as possible. A smaller air gap leads to a stronger magnetic interaction and better motor performance. However, make sure the air gap is uniform to avoid imbalances that can lead to inefficiencies and vibrations. 4️⃣ Optimizing the Magnetic Path 🧲 Design the magnetic circuit to minimize losses. This involves reducing the length of the magnetic path and avoiding sharp corners where magnetic flux can get lost. 🧲 Use simulation tools to model the magnetic circuit and identify areas where losses occur. Adjust the design accordingly. 5️⃣ Cooling Considerations 🧲 Proper cooling of the stator and rotor is essential to maintain magnetic properties and prevent overheating. Incorporate cooling systems or materials that can dissipate heat effectively. Utilize tools like ANSYS or COMSOL for simulating and analyzing the magnetic circuit for design optimization. Perform finite element analysis (FEA) to visualize the magnetic flux distribution and identify areas for improvement. Optimizing the magnetic circuit in your BLDC motor design can lead to significant gains in efficiency and performance. By carefully selecting materials, designing the stator and rotor, minimizing the air gap, and optimizing the magnetic path, we can create a highly efficient and powerful motor. 🌐 Keen on tech, EVs, motor and AI innovations? Let's connect! #motordesign #technology

Highly pure rare-earth elements are NOT essential for the production of Nd-Fe-B (neodymium-iron-boron) magnets. Nd-Fe-B magnets can be made from mixtures of rare-earth elements (REE) rather than highly pure metals of the individual REE. This possibility is often overlooked, but it could lead to much cheaper and more sustainable Nd-Fe-B magnets. REE mixtures can be separated into individual REE with a purity of more than 99.9% by solvent extraction (SX). For full separation of a mixture of all REE, more than 1000 SX stages are required. This makes REE separation an expensive and tedious process. While highly pure REE oxides are required for luminescent materials such as lamp phosphors or laser crystals, this is not the case for REE permanent magnets that are essential for electric vehicles, wind turbines, and military applications. We do not need 99.9% pure neodymium for high-quality Nd-Fe-B magnets. Nd₂Fe₁₄B (the main phase in Nd-Fe-B magnets) and its praseodymium analogue Pr₂Fe₁₄B, as well as mixtures of Nd₂Fe₁₄B and Pr₂Fe₁₄B, can be used for Nd-Fe-B magnets without experiencing any significant deterioration in magnetic properties. Therefore, the neodymium does not need to be more than 95% pure. It is even possible to prepare strong permanent magnets from mischmetal, a mixture of non-separated rare earths, using the naturally occurring ratio of these elements found in their ores. Only the samarium must be removed. Dysprosium and terbium are often added to Nd-Fe-B magnets to help maintain the magnetic properties of the magnets at higher temperatures. This makes the magnets more reliable and efficient in high-temperature environments, such as in electric vehicle motors. It is very difficult to separate these elements, but they do not need to be separated for use in magnets. They can be added to the magnet alloy as a dysprosium-terbium mixture. I was not aware of this myself until we analyzed different magnets used in cars at SOLVOMET R&I Centre. To illustrate this, have a look at the following chemical composition of a REE magnet from a power steering motor of a car: Neodymium: 20.9 wt% Dysprosium: 3.8 wt% Praseodymium: 4.2 wt% Terbium: 0.6 wt% Gadolinium: 0.2 wt% (Total REE content: 29.7 wt%) This raises the question: Why are we spending so much effort on the over-purification of REE for permanent magnet production? This is something to reflect upon. SIM2 KU Leuven #RareEarths #Lanthanides #Magnets #Metallurgy #NdFeBMagnets #RareEarthElements #PermanentMagnets #Sustainability #CriticalRawMaterials Credit picture: Shutterstock

ENCODING REPROGRAMMABLE RESPONCES INTO MAGNETO-MECHANICAL METAMATERIALS VIA TOPOLOGY OPTIMIZATION Hard-magnetic soft materials, created by embedding high-coercivity magnetic particles (such as neodymium iron boron alloy) into a soft matrix like rubber, have gained significant attention due to their enhanced programmability. Their geometry and magnetization distribution can be tailored, enabling controlled shape transformations under external magnetic fields. When exposed to such fields, the pre-magnetized hard-magnetic particles generate torques that deform the surrounding soft matrix, aligning the material with the applied field. Beyond shape morphing, the programmability of these materials extends to enabling tunable properties by leveraging nonlinear interactions between magnetic fields and other stimuli, such as mechanical loading. Researchers have demonstrated the feasibility of achieving switchable properties, including varying auxetic behavior (Poisson’s ratio), tunable buckling, and a reprogrammable force-displacement response. To achieve programmable and tunable properties, hard-magnetic soft materials provide an expanded design space encompassing both structural geometry and magnetization patterns. Exploring this design space has led to optimization-guided and machine learning-driven methodologies for computational design. Topology optimization, a generative approach, offers the ability to develop free-form designs by systematically optimizing geometries and magnetization patterns to meet user-defined objectives while adhering to functional constraints. Although topology optimization has been employed in a few studies for designing shape-morphing and actuation-capable hard-magnetic soft materials, this review present a multi-objective topology optimization framework for encoding reprogrammable properties into magneto-mechanical metamaterials and metastructures. These structures feature optimized geometries and embedded magnetization, enabling properties that can be seamlessly reprogrammed by switching external stimulus fields on and off. The framework relies on a design space parameterization scheme, allowing simultaneous optimization of both geometrical configurations and remnant magnetization patterns. The resulting designs exhibit distinct programmed behaviors under mechanical load alone and under combined magneto-mechanical conditions, where magnetic fields act as a “switch” to modify responses. To validate the effectiveness of these optimized designs with switchable properties, a representative design was fabricated and subjected to experimental testing. This optimization-driven computational approach offers a systematic and automated means of discovering programmable magneto-mechanical metamaterials and structures, whose properties can be dynamically adapted using external magnetic fields. # https://lnkd.in/epN6Gyb6