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The idea of the ‘dark factory’ has gained new attention as advances in robotics and AI accelerate. Stories range from fully automated automotive plants that operate around the clock and lights-out facilities in China, to experiments with humanoid robots on production lines, often framed as early signs of factories that no longer require people on the shop floor.

The application of AI in quality receives mixed responses: to some, it solves many problems; to others, it feels like Pandora’s box.

Force testing rarely draws attention until it fails. When components crack, seals leak, or devices do not activate as expected, engineers often trace the problem back to how force was applied, measured, or interpreted. 

Quality managers and engineers, like many other professionals, are often judged by what goes wrong. A product failure, a recall, or a customer complaint draws negative press, while processes that run smoothly can be taken for granted.

In many laboratories, confidence in physical testing is based on the assumption that results are consistent and that, if a method works once, it can be repeated. However, shifts in operator technique, environmental conditions, or instrument calibration can undermine that confidence. 

Priyank Patel is a quality expert in the med-tech space, focusing on surgical robots. He's a senior member of ASQ with ASQ certification.

Instead of waiting until the end of production to confirm size, quality teams increasingly collect dimensional data during machining and finishing operations.

Global supply chains are operating in one of the most turbulent environments in decades.

The audit is moving along smoothly. Procedures are available, records are organized, and questions are answered without hesitation. 

A process was, on an irregular basis, failing to meet the mark, perhaps 5% of the time. The process engineer observed the process in action and took a sample of thirty parts for analysis.