Watch the demo of our solution for more efficient troubleshooting in Automotive Production, recorded during the Google Cloud Learning Expedition at SPREAD HQ. Louis Weber demonstrates how we can visualize a VIN vehicle model in 3D, which allows workers to diagnose, locate, and rework error causes, saving time and effort, as well as increasing the output of the Production line.
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- Why the troubleshooting process in the automotive industry is so exhaustive and time-consuming
- How to reduce error search time with knowledge graphs
- A demo of the localization of the malfunctioning ECU inside the VIN-specific vehicle model in 3D
- How an intelligence network of the vehicle can help engineers narrow the scope of solutions through the suggestion of probable root causes
You’ll also find commentary from featured experts:
Even if a car is assembled perfectly, quality tests can still reveal hidden software or electrical failures. Workers rely on 1800-page-long PDFs to identify malfunctioning control units (ECUs) by their symptoms and their diagnostic trouble codes (DTCs). With the increasing complexity of vehicle architectures and the rising number of software modules, versions, and variants, how can we efficiently troubleshoot errors and keep pace with manufacturing targets?
Watch the demo of our solution for more efficient troubleshooting in Automotive Production, recorded during the Google Cloud Learning Expedition at SPREAD HQ. Louis Weber demonstrates how we can visualize a VIN vehicle model in 3D, which allows workers to diagnose, locate, and rework error causes, saving time and effort, as well as increasing the output of the Production line.
Louis Weber is an Account Executive at SPREAD and responsible for strengthening customer and partner relationships. Before joining the company, he worked in software quality and business development at IAV and studied Industrial Engineering at the Technische Universität Berlin.
Even if a car is assembled perfectly, quality tests can still reveal hidden software or electrical failures. Workers rely on 1800-page-long PDFs to identify malfunctioning control units (ECUs) by their symptoms and their diagnostic trouble codes (DTCs). With the increasing complexity of vehicle architectures and the rising number of software modules, versions, and variants, how can we efficiently troubleshoot errors and keep pace with manufacturing targets?
Watch the demo of our solution for more efficient troubleshooting in Automotive Production, recorded during the Google Cloud Learning Expedition at SPREAD HQ. Louis Weber demonstrates how we can visualize a VIN vehicle model in 3D, which allows workers to diagnose, locate, and rework error causes, saving time and effort, as well as increasing the output of the Production line.
Louis Weber is an Account Executive at SPREAD and responsible for strengthening customer and partner relationships. Before joining the company, he worked in software quality and business development at IAV and studied Industrial Engineering at the Technische Universität Berlin.