FEATURE:MANUFACTURING
“By applying machine vision, automakers and their suppliers can have confidence that their products will perform as expected out on the road”
> Lambert agrees that the incompatibility
of new and existing systems, particularly in software, can present hurdles: “Machine vision is a key technology solution in the industry, but traditional, rules-based machine vision struggles to handle more complex use cases and comes with longstanding problems around hardware and software compatibility, financial costs, procurement times, maintenance, interoperability and training.” Rajagopalan points out that if a
manufacturer has had a previous negative experience with the integration of a machine vision system, this can create uncertainty about using this technology: “Previous bad experiences in deploying vision systems prevents customers from betting big on these systems. “Lack of AI, optics and automation skills
in the industry creates a fear for adoption of such systems by the frontline workers.” Machine vision suppliers therefore strive
to develop easy-to-integrate solutions, as Chawarski says: “The goal is to make set-up and integration of their products as easy as possible so that the people working in the auto industry can quickly and successfully deploy solutions”.
Case studies: ChainWatch and Aurora Design Assistant Reflecting on a successful integration experience, Rünzi recounts how a manufacturer benefited from implementing SICK’s ChainWatch solution, which continuously monitors individual chain links in tow chain systems 24/7, ensuring there are no cracks or fractures and that everything is running smoothly: “Compared to a manual inspection, the ChainWatch System is able to detect cracks of chains on conveying systems of more than 300 micrometres during operation. Potential damage to chain links can be detected and repaired proactively long before it becomes a hazard. The successful implementation of the solution led to increased equipment uptime and simultaneously reduced inspection costs, resulting in significant performance gains”. Lambert shares a case study in which Mosaic incorporated Zebra’s Aurora
SICK Foreign Object Detection System
Design Assistant solution: “Mosaic is an industrial automation company with offices in Germany, Italy, Serbia and Brazil. It requires maximum quality assurance in the production of brake discs, which are made through sand-casting, a metal casting process characterised by the use of sand as the mould material. Following the casting process, the discs are cleaned inside a large drum. Sometimes, however, this process is not optimal and sand residues remain: the Mosaic system checks for the presence of traces of the sintering process and sand residues on the disc surface, which can damage the machinery or reduce its life cycle, eliminating defective units from the production line. With the Zebra Aurora Design Assistant solution, Mosaic is able to carry out quality control for over 200 different models of automotive brake discs. Inspections are repeatable 24 hours a day, and the machines operate faster than a human operator could”.
Augmented accuracy and sophistication Looking ahead, Rünzi predicts that growing use of AI will continue to enhance accuracy: “Machine vision systems will increasingly leverage AI and deep learning for more accurate defect detection.” Rajagopalan believes that existing machine vision challenges will become increasingly surmountable in line with rapidly advancing AI: “Advances in AI will make it easier to solve machine vision challenges previously not addressed. This will only further the interest and adoption of machine vision in automotive manufacturing”. He offers his insight on Indian markets, where growing awareness of AI is reaping benefits: “With increased focus in manufacturing in India, we will see the demand for machine vision increase in the next few years. For several decades, Indian manufacturers have depended on machine vision suppliers from the US and Europe to fulfil their needs. We see several
12 IMAGING AND MACHINE VISION EUROPE AUGUST/SEPTEMBER 2024
Checking heat-conducting paste on battery cells
homegrown machine vision companies from India catering to both Indian and overseas manufacturers in the near future”. Lambert sees a need for cloud-based
platforms to help manage this growth in AI use in a safe and sustainable way: “I think AI, specifically deep learning, will grow in use and sophistication. And for that to happen, we need to see greater use of cloud-based platforms to help overcome data silos and challenges and increase cross-site collaboration.”
An innovative future Automotive manufacturing reaching its fullest potential and delivering on unwavering quality and efficiency depends on manufacturers embracing machine vision; this is a two-way relationship, with the need for suppliers to provide systems that are easy to integrate. As integration challenges are tackled, machine vision systems and tools will continue to revolutionise automotive manufacturing, facilitating agile production lines in which accuracy, consistency and quality are of the highest standard. The benefits that machine vision can offer the industry will only continue to expand, driving forward innovation. i
SICK Sensor Intelligence
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