Combining System Modeling & Data to Optimize Heavy Equipment Performance

Knowledge Studio delivers explainable artificial intelligence (AI) and automates machine learning tasks to enable people to make fully informed decisions based on massive amounts of data. The software displays all the details of a model’s configuration so it is easy to understand how it generates predictions. Analysts who may not be familiar with modeling or AI processes can quickly uncover insights to help solve complicated problems. Data scientists can fine tune model parameters and develop highly sophisticated models using a drag-and-drop interface with no coding required.

Altair’s Data Analytics Assessment Service helps answer the tough questions: • What data do I have? • Can it be leveraged for analytics? • What other data do I need and how do I get it? • What ML technology can be used with my available data? • How do I get started?

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.

Panopticon provides business users, analysts, traders, and engineers with the monitoring and analysis tools they need to conduct successful, profitable operations while maintaining a close eye on anomalies, trends, clusters, and outliers. They can make insightful, fully informed decisions based on massive amounts of fast-changing data. Panopticon is enterprise-class software you can deploy in the cloud (public or private) or on-premises. It connects directly to virtually any data source, including big data sources, SQL and NoSQL databases, flat files, and real-time message queues. Users can develop stream processing applications and design sophisticated visual user interfaces without writing a single line of code. This technical document provides a summary of the capabilities, implementation options, applications, and benefits of using Panopticon to support your operational requirements.

The Battery Production Group at the Institute for Machine Tools and Industrial Management at the Technical University of Munich (TUM) researches the production of innovative battery cells.

The core of the work is process development and the optimization of processes within battery production – from mixing, coating and calendering of the electrode materials to the formation of the final battery cells. All battery production steps are carried out in-house using the TUM’s electrode and battery production line.

TUM uses Altair EDEM software to simulate the calendering process for lithium-ion batteries.

As of today, the “classical” V diagram is very well known among more and more engineers. Nonetheless its usage – even partly – is far away from the potential that it offers. One reason might be, that its benefits are not really obvious for the end-users. With this presentation, we will bring a new view by “closing the old V” and transferring it to a “closed ∇ (Nabla) cycle”. The focus of this contribution is on the opportunities to significantly increase the effectiveness of the approach of model-based development (MBD) by re-using engineering efforts in multiple ways.

Reducing aircraft design and development time is critical for all aircraft manufacturers, from urban air mobility and electric aircraft startups to military to commercial OEMs. In order to fully understand and optimize the complex systems of systems required in modern aircraft, aerospace engineers leverage a simulation method called Model-based Systems Engineering (MBSE). MBSE allows the evaluation of various types of vehicle systems to determine which best meet the mission requirements.

High accuracy sensors and encoders are integral components of an e-motor drive, greatly impacting the quality and efficiency on the system. A purpose-driven simulation approach is needed to account for all the physical interdependencies within these complex multi-domain systems.

Accurately predicting future consumer behavior allows credit risk analysts, financial marketing analysts, and fraud detection teams to better deploy strategies to capitalize on opportunities while deploying strategies that act as preventative measures against disruptive forces to their business models.

The world of financial services is not short on data, however, there is a shortage of tools that can handle everything the banks need to look at. From the very beginning, the Panopticon development team recognized the absolute need to work with what the industry is now calling “Big Data”

Regulatory change, competition, market fragmentation, and other factors are driving buy-side and sell-side firms to implement analytics systems to help them improve trading quality and reduce the cost of compliance. This two-part white paper explores best practices for approaching and planning such implementations.

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.

XLDyn® allows the product engineer to develop and track requirements associated with different verification methods, so the current project status is always available. In addition, XLDyn® has fully integrated system level Design of Experiments (DoE) that provides valuable design guidance to select the best set of parameters or parts. Even test data can be included in the DoE.

Nowadays, it is more and more difficult to design electro-technique devices without having a look at thermal stress. In more and more applications (more electric vehicles, more electric aircrafts, …) designers need to reduce weight, cost, increase efficiency, and keep the same security factor. One possibility is to increase current for the same device, needing to check how to draw away the heat. This is why the classical approximations need to be cross checked with complementary analysis. These new tools have to be rapid and accurate in order to run parametric and even optimization analysis. There is also a need for fast model in order to check robustness versus driving cycles. The goal in this article is to review rapidly the different methods available, depending on the accuracy required and the solving speed. The method includes equivalent thermal circuits, Finite elements methods and CFD analysis.

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.