Two- and Three-Wheel Vehicle Simulation

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.

Heavy equipment manufacturers want to design products that are durable and perform at their peak under a variety of conditions. To accomplish this, Altair provides an integrated multi-disciplinary simulation environment to virtually test and optimize equipment performance and therefore, help reduce design and development costs. Using simulation-driven design, studying the full dynamics of a product or system is possible, from motion analysis to complete lifecycle durability testing.

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.

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.

Block construction is a modern shipbuilding method which involves the assembly of prefabricated modular sections. Cross-sections of the superstructure are pre-built in a shipyard, taken to the building dock, then hoisted into position and attached. Block splitting and lifting schemes are largely devised after the ship design phase is completed, relying on empirical data and expertise to avoid costly and potentially dangerous failures during build-up. Advances in computer-aided engineering (CAE), however, now make it possible to plan ship build-up in the principal design phase, giving designers greater insights into block assembly process outcomes and reducing downstream risk through simulation.

Today, most products are complex mechatronic combinations of advanced technologies, mixing electrical parts with controllers and embedded software. To efficiently manage innovative products, organizations are turning to a Model-Based Development approach for concept studies, control design, multi-domain system simulation and optimization. To meet this demand, Altair’s simulation and optimization suite aims to transform design and decision-making throughout product lifecycles with their multi-disciplinary software tools and consultancy services.

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.

Automotive radars are becoming standard equipment on vehicles. Their purpose is to adjust the distance between vehicles and/or alert the driver when dangerous situations arise. Several antenna architectures are used to cover the different safety functions in complex bumper/car chassis environment where the side effects become more and more significant on the radar performances. Hence, automotive radar integration process becomes a very important topic. Weak radar integration will generate gain loss, high side lobes levels and angular errors. Those degradations will impact the radar range, the main radar axis (BSE) and the radar detection quality (resolution, ambiguity, discrimination).

Development tools and methods, such as simulation, are increasingly important to face and address the pressure of innovation. As an example, for successful new design methods and to show how simulation tools are used, Altair developed a virtual demonstrator based on a cobot application. This complex machine interacts with a human operator as the ultimate smart manufacturing equipment - to show how challenges in modern product design can be overcome.

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.

When developing mobile machines, manufacturer focus is twofold: 1) Increasing a machine’s productivity and operator comfort 2) Improving its energy efficiency. To achieve these objectives, it is crucial to have an optimized system-of-systems and seamless interaction between subsystems. But how can manufacturers design components from varied disciplines like mechanics, electronics, and hydraulics to create a holistic overall system having optimal performance? The answer is digital transformation.