AI and automation need people and quality data to succeed
By Illya Verpraet2020-12-23T16:22:00
Experts from BMW, Ford, Symbio Robotics and Cosmo Tech discussed how artificial intelligence (AI) and automation in automotive production need fast ramp-up times and adoption by employees to succeed. High-quality data and use of simulation are also key.
Any system in a modern production plant needs to be completely robust, which means that it can take a while to get set up. According to Matthias Schindler, head of artificial intelligence innovation for BMW production system, setting up an AI system in itself isn’t too complex, and for a basic use case, can be done in a matter of hours. However, laying the groundwork – getting the funding and completing a proof of concept – took years. Getting an AI system to talk to legacy systems can be equally time consuming.
BMW’s first serious foray into AI for production was a system that can recognise whether the correct model designation badge such as 330i, 530e, X5, etc. has been placed on the right car. Schindler says that the project was started in 2018, with the system now being in use at the Dingolfing plant and being rolled out at other locations. “Today it would probably take us some hours to set up the AI, but what is much more important and takes much longer is what we call it system integration,” he said.
Ensuring that the camera is triggered to take photos at the right time, rather than film video, linking the AI’s perception to the order bank and the quality control systems is what takes time. “You have to have a connection and as we have legacy systems, the interfaces can be challenging and pretty time consuming to connect,” he added.
Paula Carsí de la Concepción, manufacturing engineering supervisor and technology specialist in emerging technologies at Ford’s Valencia engine plant, concurred that it is not the neural networks, which are the fundamental mechanisms behind AI, that are time consuming to set up. Instead, in Ford’s case, it was accounting for all the variability and slowly refining the concept.
She oversaw a project that uses microphones to determine whether an electrical connector has been connected properly, based on the sound it makes as the two terminals lock together. The breakthrough for the project was the realisation that ultrasonic microphones would filter out background noise that made normal microphones unusable.