Catalysts of change: How robots, machine vision and AI are changing the automotive manufacturing landscape

This month, we are discussing the technological and cultural shift in the automotive manufacturing environment caused by the advent of robotics and machine vision, coupled with the development of AI vision systems. We also drill down on the drivers of change and how augmented reality will shape the future of manufacturing technology for automotive production.

Robots in Manufacturing
Robots in manufacturing have been used for over sixty years, with the first commercial robots used in mass production in the 1960s – these were leviathans with large, unwieldy pneumatic arms, but they paved the way for what was to come in the 1970s. At this time, it was estimated that the USA had 200 such robots [1] used in manufacturing; by 1980, this was 4,000 – and now there are estimated to be more than 3 million robots in operation [2]. During this time the machine vision industry has grown to provide the “eyes” for the robot. Machine vision uses camera sensor technology to capture images from the environment for analysis to confirm either location (when it comes to robot feedback), quality assessment (from presence verification through to gauging checks) or simply for photo capture for warranty protection.

The automotive industry is a large user of mass-production robots and vision systems. This is primarily due to the overall size of the end unit (i.e. a built car), along with vision systems for confirmation of quality due to the acceptable parts per million failure rate (which is extremely low!). Robots allow repetitive and precise assembly tasks to be completed accurately every time, reducing the need for manual labour and providing a faster speed for manufacturing.

Automating with industrial robots is one of the most effective ways to reduce automotive manufacturing expenses. Factory robots help reduce labour, material, and utility expenses. Robotic automation reduces human involvement in manufacturing, lowering wages, benefits, and worker injury claims.

AI Machine Vision Systems
Deep learning in the context of industrial machine vision teaches robots and machines to do what comes naturally to humans, i.e. to learn by example. New multi-layered “bio-inspired” deep neural networks allow the latest machine vision solutions to mimic the human brain activity in learning a task, thus allowing vision systems to recognise images, perceive trends and understand subtle changes in images that represent defects. [3]

Machine vision performs well at quantitatively measuring a highly structured scene with a consistent camera resolution, optics and lighting. Deep learning can handle defect variations that require an understanding of the tolerable deviations from the control medium, for example, where there are changes in texture, lighting, shading or distortion in the image. Deep-learning vision systems can be used in surface inspection, object recognition, component detection and part identification. AI deep learning helps in situations where traditional machine vision may struggle, such as parts with varying size, shape, contrast and brightness due to production and process constraints.

Augmented Reality in Production Environments
In industrial manufacturing, machine vision is primarily concerned with quality control, which is the automatic visual identification of a component, product, or subassembly to ensure that it is proper. This can refer to measurement, the presence of an object, reading a code, or verifying a print. Combining augmented, mixed reality with automated machine vision operations creates a platform for increased efficiency. There is a shift towards utilising AI machine vision systems (where applicable!) to improve the reliability of some quality control checks in vision systems. Then, combine that assessment with the operator.

Consider an operator or assembly worker sitting in front of a workstation wearing wearable technology like the HoloLens or Apple Vision Pro (an augmented reality headset). An operator could be putting together a sophisticated unit with several pieces. They can see the tangible objects around them, including components and assemblies. They can still interact with digital content, such as a shared document that updates in real time to the cloud or assembly instructions. That is essentially the promise of mixed reality.

The mixed-reality device uses animated prompts, 3D projections, and instructions to guide the operator through the process. A machine vision camera is situated above the operator, providing a view of the scene below. As each step is completed and a part is assembled, the vision system automatically inspects the product. Pass and fail criteria can be automatically projected into the operator’s field of sight in the mixed reality environment, allowing them to continue building while knowing the part has been inspected. In the event of a rejected part, the operator receives a new sequence “beamed” into their projection, containing instructions on how to proceed with the failed assembly and where to place it. When integrated with machine vision inspection, mixed reality has become a standard aspect of the production process. Data, statistics, and important quality information from the machine vision system are shown in real time in the operator’s field of view.

Robots, AI Vision Systems and Augmented Reality
Let’s fast forward a few years and see what the future looks like. Well, it’s a combination of all these technologies as they continue to develop and mature. So AI vision systems mounted on cobots help with the manual assembly, while the operator wears an augmented reality headset to direct and guide the process for an unskilled worker. Workers are tracked while all tasks are being quality assessed and confirmed, while data is stored for traceability and warranty protection.

In direct automotive manufacturing, vision systems will become easier to use as large AI deep-learning datasets become more available for specific quality control tasks. These datasets will continue to evolve as more automotive and tier one and two suppliers use AI vision to monitor their production, allowing quicker deployment and high-accuracy quality control assessment across the factory floor.

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