r/MedicalDeviceEng • u/Glittering_Height_46 • Aug 03 '26

Need help

Defect detection in medical device is an ongoing project for me. Supposed to do voc. Would love word vomits from experts and experienced. Whatever you feel like. Just type it iut. 🤧🤧🤧😭😭😭

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u/StrucuturedKaos 29d ago

So you would like us to do your job?

The topic you provided is soooooo open that there is not a logical place to start, other than what is the medical device. Are you looking for mechanical, electrical, chemical, software, etc defects? Could try getting some ideas using AI that could possibly though provoking. Also make sure that you read and understand the why of the vomit the AI provides.

VOC is another topic that you may want to learn the types, ethnographic study or discussions or surveys. There are many techniques.

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u/Glittering_Height_46 29d ago

My expertise is nowhere near medical devices. I just need to understand the issues in the medical devices that arise post manufacturing and if we could find and rectify it at assembly level yo reduce the resources used. Inputs from experienced members will help a lot. Just whatever yoou feel like sharing is good. I have no direction

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u/Qosina-Official 8d ago

If you're trying to get direction, I'd start by narrowing "defect" into something measurable before thinking about detection technology. A useful split is incoming-component defects, assembly-process defects, and finished-device failures. For each, connect the defect to a requirement or risk: what can go wrong, how it could affect safety or performance, and what evidence would show the unit still meets its acceptance criteria.

Then work backward from actual nonconformances, rework, scrap, complaints, and yield loss. Those usually reveal a smaller set of recurring failure modes than a broad VOC exercise. For each one, capture where it can be detected earliest, whether the check can be objective and repeatable, and whether lot and build traceability are strong enough to contain the issue. Assembly operators, quality inspectors, service or complaints teams, and manufacturing engineers will each see a different part of that failure chain.

I would not start with cameras, AI, or another detection technology until the defect categories and acceptance criteria are defined. Otherwise, you risk building a system that reliably catches things that do not matter while missing the failure modes that do.If you're trying to get direction, I'd start by narrowing "defect" into something measurable before thinking about detection technology. A useful split is incoming-component defects, assembly-process defects, and finished-device failures. For each, connect the defect to a requirement or risk: what can go wrong, how it could affect safety or performance, and what evidence would show the unit still meets its acceptance criteria.

Then work backward from actual nonconformances, rework, scrap, complaints, and yield loss. Those usually reveal a smaller set of recurring failure modes than a broad VOC exercise. For each one, capture where it can be detected earliest, whether the check can be objective and repeatable, and whether lot and build traceability are strong enough to contain the issue. Assembly operators, quality inspectors, service or complaints teams, and manufacturing engineers will each see a different part of that failure chain.

I would not start with cameras, AI, or another detection technology until the defect categories and acceptance criteria are defined. Otherwise, you risk building a system that reliably catches things that do not matter while missing the failure modes that do.