Artificial intelligence monitoring of direct strip casting

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Identifying defective products on a production line is a labour-intensive and costly process, resulting in long product hold times and/or lost time and effort. Quality-related costs are a significant economic and environmental burden: even a slight variation in production processes or materials (many invisible to the human eye) can result in an entirely defective production run. Conventional machine vision-based surface inspection methods work well for surface inspection when defects have strong edges/contrast, but they are greatly affected by noise and lighting conditions. This proposal will address detection of defects in a novel aluminum strip-casting facility. To better handle noise and lighting issues, rather than conventional image processing, this proposal will develop state-of-the-art machine learning approaches, coupled with industrial-ready visual capture technologies. Initially focused on postmortem assessment of material, the proposal will also work to develop an on-line defect detection methodology. With suitable development, the final goal will be not just detect defects but help guide optimisation of the strip casting process for given aluminum alloy. The proposal brings together two small but innovative industrial contributors focused on applied uses of machine learning and novel aluminum casting technologies, respectively. While the machine learning approach will be applied here to industrial strip-casting of aluminum, we consider this a development test-bed for procedures and processes that are directly transferable to a range of continuous production industries. To enable application across a wide range of industries, the approach developed will therefore be modular and flexible. Our goal is to address a significant knowledge gap with the potential to benefit a wide range of Canadian industries.