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Vision Spectra Preview - Winter 2023

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Here is your first look at the editorial content for the upcoming Winter issue of Vision Spectra.

How AI Computer Vision Succeeds in Quality Inspection where Traditional Methods have Failed

Detecting Dents and Damage in Aluminum Cans. This article explores a new use case demonstrating the effectiveness of AI computer vision in addressing a challenging application within the manufacturing industry: the detection of dents and damage in aluminum cans – especially when those cans are covered in water droplets after they go through a washing station. Traditional computer vision inspection methods have long struggled to provide accurate and efficient solutions for this task. We will explore the limitations of these conventional approaches and highlight the potential for AI-driven solutions to open up new possibilities for inspection tasks in various industries. With AI-based computer vision, we showcase the successful identification of dents and damage in aluminum cans, an important issue for quality control in the beverage industry. This article also provides insights into the process of training and deploying AI models within a factory context, offering guidance for those looking to harness the potential of AI in their inspection processes.

1) Introduction – Overview of the problem and challenges faced today
2) Limitations of traditional inspection methods
3) Embracing AI for improved quality inspection – how AI can address these limitations
4) Description of successful application in this use case – aluminum cans, detection of dents and damage – how it works
5) Training and deploying the AI system into a factory
6) How this can be used for other manufacturing inspection tasks

Key Technologies: computer vision, inspection, AI

How Machine Vision Improves Battery Production

The use of machine vision in battery production holds numerous advantages for manufacturers. As part of end-to-end digitization and integrated process optimization, the technology offers very significant leverage. It enables a reduction in the reject rate and makes a measurable contribution to sustainability through the associated, more efficient use of valuable raw materials. At the same time, it also allows output to be increased because as the "eye of production", machine vision enables continuously automated and seamlessly traceable production processes. Machine vision in electrode production In electrode manufacturing, copper or aluminum foil is coated on both sides with an active material. Machine vision is used to inspect the surface, dimensional accuracy, and alignment of the coated electrode webs to ensure their quality. In addition to pure quality control, the results of the machine vision software, sometimes together with other data points, enable end-to-end process optimization. This means that it is possible to detect at an early stage if, for example, the coating is being applied unevenly, etc. Preventing errors in cell assembly Electrode production is followed by cell assembly. Machine vision software is also used here to inspect the individual work steps. This allows weld seams to be inspected, although there can be a high variation in possible defect features due to the process, which is why AI approaches are well suited. In addition, thanks to machine vision, barcodes and data matrix codes can be read reliably even in the presence of movement and changing light conditions.

Key Technologies: machine vision

AI-Powered Cobot Inspection

The topic is "Collaborative inspection solution with AI" targeting the automotive industry. We want to talk about robotic and vision solution that helps to inspect bigger parts (for example car doors and internal panels) with a long reach. The article will talk about industry challenges and why manufacturers need such applications to help boost production. We will also talk about connecting it to an all-in-one control platform to achieve centralized data as well as perform traceability.

Key Technologies: AI, cobots, inspection

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