FEATURE Automated warehousing
using sampling rather than inspecting every part individually, it is easy to see how a rarer defect would evade capture. In safety- critical industries though, every part will be inspected, and, given the shortcomings of manual inspection, many companies have invested in technology to improve their odds of detection.
Investing in AI
AI-based quality inspection systems are vastly superior in detecting defects, but most of the existing technology has limitations. Firstly, the AI system relies on hardware such as imaging and other sensors. Relatively subtle changes in environmental conditions, like lighting for example, can create problems. at catching simple defects. Whether an AI can spot a defective part before it leaves the plant depends on the sophistication of the algorithm and the application. Basic rule-based challenges that are deterministic and the environment is repetitive in nature, but most modern production processes – particularly in safety-critical industries like aerospace, automotive and med-tech – require deep learning algorithms. For training these AI models, the process is often lengthy and time consuming. The end result might be a model that is good at detecting common defects. However, we run up against the problem referred to by AI technologists a ‘‘class imbalance’’, where common defects routinely show up in the training, but rare defects don’t. Not surprisingly, the end result is a model that fails to catch the outliers.
Continuing to avoid capture Due to errors routinely made by manual inspectors and the shortcomings of existing AI, components with minor defects leave the plant. single components, but in assembly there’s another layer. When multiple components are assembled together, defects that went undetected at an earlier stage of production can often continue to avoid detection. Imagine a simple single gear. A minor defect is not spotted on the surface, as sheet inspection of metal surfaces is notoriously challenging. This gear then becomes part of a
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transmission, which in turn becomes part of a car.
Functional testing of the product may expose a defect in some instances, but in many cases the defective component, buried inside the assembled product, will continue to escape attention at this stage. The defect can now pass through multiple stages of the production process undetected, as it is no longer visible, even with the most thorough inspection process and the most sophisticated AI. The point at which the defect comes to the attention of the manufacturer is when the product fails within its warranty period or, worst still, is shown to violate regulatory requirements. The longer the defects evade detection, the bigger the cost to the manufacturer, as it then has to recall the entire product rather than simply addressing the defective part in isolation.
The next step in AI As already observed, although AI has assisted in improving defect detection, existing systems still have their limitations. Thankfully, that is now beginning to change. The hardware that inspection systems rely on to more companies. At the same time, we are overcoming the challenges of training AI models for inspection, by automating the process of model training which makes the process faster and more accurate. It is the quality manager that transfers his knowledge in the model creation process and, with augmented AI, the quality manger can easily continue to update the model on the production line, preparing for the most challenging inspection tasks.
Supply Chain Logistics
Supply Chain Logistics
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More than 20,000 sensors. Factory Automation. Process Automation. Hazardous Areas. Harsh Environments. Measurement & Inspection. LED Lighting. Vision Systems. Machine Safety. Vehicle Detection. Flow, Temperature Pressure & Vibration.
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Automation | May 2024 21
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