Emulating Engineering Expertise with AI: Crashworthiness Optimization at BMW
Technology Category
- Analytics & Modeling - Machine Learning
Applicable Industries
- Automotive
- Education
Applicable Functions
- Human Resources
- Product Research & Development
Use Cases
- Predictive Maintenance
- Time Sensitive Networking
Services
- Data Science Services
About The Customer
The BMW Group is the world’s leading manufacturer of premium automobiles and motorcycles, with 31 production and assembly facilities in 15 countries and a global sales network. The company is also a provider of premium financial and mobility services. As an innovation leader, BMW sets trends in production technology and sustainability with an intelligent material mix, a technological shift towards digitalization, and resource-efficient production. The company also ensures competitiveness through flexibility and continuous optimization of value chains.
The Challenge
BMW, a leading manufacturer of premium automobiles and motorcycles, faced a significant challenge in ensuring passenger safety during crash events. The complexity of orchestrating the chain of events during a crash, including the timing of discrete events such as bolts breaking or parts coming into contact, was a daunting task. The key performance indicators, including energy absorption, peak force level before failure, local displacements, and weight, were either too complex or resulted in over-constrained optimization problems. This made it difficult to confidently validate crashworthiness within the fast-paced product development process. The challenge was to find a solution that could simplify this process, reduce the number of design iterations required, and ensure the highest level of safety for passengers.
The Solution
BMW turned to Altair’s integrated Machine Learning solutions within HyperWorks to generate optimization constraints that mimic engineering expertise. The solution involved the use of clustering, an unsupervised machine learning algorithm, to help engineers understand how crash kinematics affect key performance indicators. Favourable crash kinematics were then enforced during the optimization process through the use of a classifier that emulates engineering decision making. This approach simplified the formulation of the optimization problem and reduced the number of design iterations required to develop complex automotive crash structures. The machine learning-powered workflow augmented and extended BMW’s existing engineering expertise, allowing them to more efficiently allocate computing and human resources to high-value simulation, analysis, and validation efforts.
Operational Impact
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