Optimizing Medical Stents with Machine Learning

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.

Heavy mobile machines consist mostly of production equipment working almost twenty hours a day, year on year, in diverse harsh environments, undergoing extreme loads and overloads. Especially diggers and loaders such as hydraulic excavators, wheel loaders, and backhoes, cater to multiple applications with use cases such as digging, trenching, loading, lifting, breaking, and ripping. Many times, these machines undergo non-standard uses where the machine is subjected to unplanned forces and moments as in the case of self-loading on a trailer, or a bucket hitting a dump truck body. This paper highlights the workflow process and simulation-driven methods to integrate multi-physics with Altair’s industry-leading solutions. The latest generation of Altair simulation tools can capture a wider range of vehicle systems and environmental interactions.

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

Simulation-driven design changed heavy equipment product development forever, enabling engineers to reduce design iterations and prototype testing. Increasing scientific computing power expanded the opportunity to apply analysis, making large design studies possible within the timing constraints of a program. Now engineering data science is transforming product development again. Augmented simulation features inside Altair® HyperWorks® are accelerating the design decision process with machine learning (ML). The power of ML-based AI-powered design combined with physics-based simulation-driven design leveraging the latest in high-performance computing is just being realized.

Learn how Altair Radioss™ Performs with AMD EPYC™ 7003 Series Processors

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. 

Engineering.com audience survey of nonlinear simulation practices

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.

A company specializing in 3D printing relies on simulation to make tools for injection molding that are less expensive, lighter and better than those created with traditional methods.

In recent times there is a high demand for lightweight automotive components which will reduce oil consumption and emissions. The components that are under non-linear load conditions would need optimization techniques that would yield a design which satisfies all performance targets and at the same time maintains the process efficiency with respect to time and cost. The use of CAE tools such as Altair’s OptiStruct and HyperWorks allows engineers to explore various design solutions starting from concept level to matured design that meets multiple requirements simultaneously with due consideration of manufacturing methods that allows engineers to arrive at an optimal design and process.

To design and build an aerial ladder for a firetruck, the engineer needs to accurately determine the working loads the ladder will encounter. Some of these can be easy to interpret such as the weight of the firefighter in the basket at the end of the ladder, or the weight of the water being supplied to the nozzle. Other loads can be a little harder to quantify, such as how wind affects the ladder. There are several different ways to determine this effect, and two of those will be explored in this paper: the standard equation (ASCE 7-10), and CFD.

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.

The task is to define optimal composite material and laminate property data, using HyperMesh in combination with Anaglyph’s composites design and analysis software tools.

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.

This white paper demonstrates how to find the optimal locations to place strain gauges on a casted bracket in order to accurately measure loads.

Altair RADIOSS* and Intel® Xeon® Scalable processors combine to provide an advanced solution stack for improved crashworthiness, safety, and manufacturability of automotive structural designs.

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.

Additive manufacturing coupled with topology optimization allows the design-and-analysis and manufacturing iterations to be reduced significantly, or even eliminated. To ensure that the part will perform as simulated, a mid-stage validation is conducted on a standardized part before creating the final products.

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.