Applying Machine Learning Augmented Simulation to Heavy Equipment

Renishaw uses Altair signalAI to deliver advanced digital gauging with real-time melt-pool analytics. This AI-driven quality assurance process helps Renishaw identify manufacturing anomalies earlier, develop parts quicker, and realizes a stable production.

A key development goal of any machine-building project is to produce perfectly running, reliable machines that make high-quality products. By leveraging accurate virtual prototypes, seamless production can be ensured earlier in the development process to help assess and improve product profitability.

Radomes are used across multiple industries, including aerospace, defense, electronics, and telecommunications. When properly designed, the radome can actually enhance the performance of an antenna system. The proper selection of a radome for a given antenna can help improve the overall electromagnetic system performance by eliminating wind loading, allowing for all-weather operation, and providing shelter for installation and maintenance. Altair’s radome simulation solution helps to streamline the design of these complex components, ensuring performance while significantly reducing development time.

The advancements in the fields of AI and ML, combined with the increased availability of robust simulation, testing, and field data sets has made engineering data science a critical component of the modern product development lifecycle, but in order to extract maximum value from these exciting tools, companies need a plan to store, manage, and utilize their data efficiently. They need data discipline

How do industry leaders and today's young minds look at ethical AI? This article from Engineering.com poses some tough questions about the role AI will play in our future and how we can plan to deploy these powerful tools responsibly. The panel of industry leaders and up-and-coming engineers interviewed for this article include:

• James Scapa, chairman, founder and CEO of Altair
• Carsten Buchholz, capability lead of Structural Systems Design at Rolls-Royce
• Hod Lipson, a professor at Columbia University that researches Robotics, AI, Digital Design and Manufacturing
• John Estrada, a student that produced an AI model for drought stress assessments in plants
• Tienlan Sun, a student that produced an AI model to detect illnesses within the eyes

Medical stents are a lifeline for patients with cardiovascular illness and disease. Altair's solutions can speed up development time by satisfying the testing of variables virtually, allowing engineers to truly optimize the design and performance of medical stents.

Two- and three-wheeler vehicle manufacturers, whether they are existing OEMs, new EV start-ups, or suppliers serving this segment, all have the goal of shortening product development time and getting product to market faster. With Altair HyperWorks™, ride, durability, and vehicle dynamics simulations for two- and three-wheeled vehicles can now be seamlessly performed using an intuitive and easy to use GUI with built-in libraries for vehicle models, analyses, and predicting and optimizing vehicle behavior.

Simulation helps you validate at the end of a product design cycle, but deployed early and throughout a development process, it can actually allow you to explore more potential solutions, collaborate more effectively and optimize the design for cost, performance, weight, and other important criteria. This infographic provides a framework for developing and implementing your own simulation-driven process to help you produce more efficient e-motors and shorten development times. 

In this white paper, a suitable FE model of a joining process(clinching) is built using the metal forming simulator AFDEX and then the process is optimized using the multidisciplinary optimization software HyperStudy from Altair.

The benefit of design exploration and optimization is understood and accepted by engineers but the required intensive computational resources have been a challenge for their adoption into the design process. The HyperWorks Unlimited (HWUL) appliance provides an effective solution to these challenges as it seamlessly connects all the necessary tools together in the cloud. The aim of this study is to showcase the benefits of HWUL on an optimization driven design of a complex system. For this purpose an automotive seat design for crash loadcases is selected.

This paper describes the process of using Altair tools such as Flux for synchronous permanent magnet motor EM FEA analysis and HyperStudy to minimize the weight of the NdFeB magnets of a typical IPM motor for electric traction application such as the IPM motor of the Toyota Prius 2010.

Snap-fits are ubiquitous engineering features used to quickly and inexpensively assemble plastic parts. The geometric, material, and contact nonlinearities associated with snap-fit problems can present modeling challenges. Quasi-static solutions with explicit solvers are commonly used to analyze snapfits; however, OptiStruct’s nonlinear solver now possess the ability to solve these highly nonlinear problems implicitly. The first part of this study discusses an effective approach to using OptiStruct for the implicit finite element analysis of snap-fits. Once an accurate simulation model has been created, engineers typically make design changes in order to achieve desired insertion and retention forces. The second part of this study details how HyperMesh morphing and HyperStudy can be used to optimize the snap-fit design, resulting in desired insertion and retention forces while minimizing mass and ensuring structural integrity. The approach documented in this report can reduce the design time, material use, and failure rate of snap-fits used in industry.

In an electric machine, the torque is generated by electromagnetic forces which also create some parasitic vibrations of the stator. These vibrations excite the mechanical structure on which the motor is fixed and generate sound. When designing the electric machine, this aspect has to be taken into account from the start since it depends on the harmonic content of the currents that feed the machine, on the shapes of the rotor and stator, and on the interaction of the electric frequencies with the natural mechanical modes of the structure. To simulate this phenomenon, a coupling between electromagnetic calculations and vibration analysis has to be set-up. Some optimization procedure can also be added in order to reduce the noise. In what follows, it is shown how Altair HyperWorks suite; specifically FluxTM, OptiStruct®, HyperMesh® and HyperStudy® products have been successfully used to perform a multi-physics optimization for noise reduction in a fuel pump permanent magnet motor.

This step-by-step tutorial details how to use RAMDO with HyperStudy and OptiStruct.

In this paper, a workflow is presented that integrates the functionalities of a metal forming simulation software, AFDEX and a multidisciplinary optimization software, HyperStudy. Using this approach, the forming load of a gear driver used in an automotive transmission is minimized and two die design parameters are optimized.

The objective of this paper is to assess several optimization algorithms in HyperStudy for their effectiveness and efficiency. The following sections of this paper present an overview of the optimization algorithms frequently used in HyperStudy. This is followed by benchmarking of both single objective and multi-objective optimization problems, respectively.

To derive the natural frequencies and mode shapes of a given structure, the test Engineer has to decide on excitation positions that will efficiently excite all the modes of the structure in the frequency range of interest. Excitation positions are usually decided upon from experience or trial and error methods which can be time consuming and still not capture all of the modes in the selected frequency range. Using Altair HyperStudy and Radioss (bulk), Pre-test CAE analysis has been carried out to identify effective excitation positions before the commencement of modal testing, thereby significantly reducing pre-test lab time.

The use of CAE software tools as part of the design process for mechanical systems in the automotive industry is now commonplace. This paper highlights the use of Altair HyperWorks to assess and then optimize the performance of a McLaren Automotive front suspension system. The tools MotionView and MotionSolve are used to build the model and then carry out initial assessments of kinematics and compliance characteristics. Altair HyperStudy is then used to optimize the position of the geometric hard points and compliant bush rates in order to meet desired suspension targets. The application of this technology to front suspension design enables McLaren Automotive to dramatically reduce development time.

When applied to engineering, Machine Learning can be a powerful tool to aid in a range of applications, from faster finite-element (FE) model building to optimizing manufacturing processes and obtaining more accurate results from physics-based simulations. Although incorporating this collection of technology is relatively new in the field of engineering, Altair has made leaps forward in this space to provide users with the tools they need to make a difference.