• • • ENERGY EFFICIENCY • • •
How an Autonomous Machine Vision system increased accuracy and cut waste
BSH Home Appliances wanted to implement a quality assurance system to spot defects early on and reduce material waste, with the goal of cutting costs while benefiting the environment. The company found the perfect partner in Inspekto, the German-Israeli quality assurance specialist who invented Autonomous Machine Vision (AMV)
B
SH Home Appliances is one of the leading home appliance companies worldwide and the largest in Europe. The company is
committed to a progressive sustainability agenda and its entire manufacturing process is carbon neutral. The company consists of 40 facilities worldwide
and employs more than 62,000 people. Its commitment to sustainability is actualised in the production of energy-saving appliances, as well as in the reduction of the company’s environmental footprint in all areas of the value chain. Like Inspekto, BSH is a firm believer in
innovation through digitalization and 4.9% of the company’s spending is dedicated to research and development (R&D). As a result, the company established the BSH Startup Kitchen, an initiative that offers young companies the possibility to collaborate with BSH by offering their cutting-edge solutions to improve the company’s products and processes. The BSH Startup Kitchen tests promising new technologies and, following a successful pilot phase, offers the chance of a long- term business relationship. Lars Roessler, Venture Partner at BSH Startup
Kitchen, said: “We have recognised that startups are a valuable source of innovative technologies and solutions for many of our business segments..
“BSH can apply such innovations directly in our
product development and boost the productivity of our processes.” One area where BSH intended to improve its
existing processes was quality assurance. As the systems in place still allowed some defected items to slip through, BSH was looking for a QA method that withheld the company’s strict quality standards without being too complex and cumbersome to deploy. As the need arose for an accurate, reliable, but
user-friendly system, the BSH Startup Kitchen decided to approach Inspekto, the pioneer of Autonomous Machine Vision.
The challenge Automating QA allows manufacturers to save time and money. The cost of poor product quality is notorious — damage to a hard-earned reputation, erosion of customer trust, expensive recalls, material waste and reworking costs are just some consequences of releasing defective products. For this reason, QA is a crucial step in every
manufacturing process, regardless of industry size or sector. However, manual QA is not fit for the strict standards of Industry 4.0, since human inspectors may miss defects, especially when inspecting highly complex electrical items.
On the other hand, traditional machine vision
solutions are extremely expensive and complex to set up and maintain, making them unpractical for many manufacturers. BSH wanted to implement a reliable automated QA system, but was struggling to find a satisfying solution. “Even with multiple inspection check-ups,
mistakes still emerged, thus increasing scrap- related costs,” explained Dipjyoti Deb, venture partner at BSH Startup Kitchen. “BSH had experimented with automated inspection solutions in the past, but each proved unsatisfactory and costly.” BSH’s challenge was to increase the accuracy
and efficiency of batch inspection processes, in a way that was simple and did not require the design and installation of a complex, customised project. BSH project engineers Markus Maier and Stefan
Schauberger were responsible for reducing the detection time of component defects at one of BSH’s oven manufacturing plants in Traunreut,
Germany.They approached BSH Startup Kitchen with this problem, and a partnership with Inspekto was formed.
The solution Inspekto is the inventor of AMV, a new approach to industrial QA that mimics the entire human vision process while retaining the reliability and repeatability of industrial machine vision. Just as the human brain adapts our single
optical system — our eyes — to each scenario, AMV adapt a single electro-optical system to fit a wide range of use-cases. As a results, AMV systems are not tailor-made, case-specific solutions, but off-the-shelf products that come pre- trained for a wide variety of use cases, so that users can easily install and deploy them independently and in a very short time. The user does not need to specify the
parameters for image capturing, like the distance between the camera and the sample item, lighting, focus value, shutter speed and exposure time — all of this will be automatically calculated and dynamically adjusted by the AMV system using its artificial intelligence (AI) engines. Users only need to present the system with 20 to
30 good sample items, so that it can learn the characteristics of the items to be inspected and flag any deviation from the memorised standards. The user-friendliness and immediacy of AMV
systems are the result of Autonomous Machine Vision Artificial Intelligence (AMV-AI), a proprietary
30 ELECTRICAL ENGINEERING • JULY/AUGUST 2022
electricalengineeringmagazine.co.uk
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