Plastic clips, such as those used to attach interior trim, must be delivered to automotive suppliers with virtually no defects. Until now, quality control in injection molding production has often relied on manual visual inspections or random checks. However, manual inspections are slow, expensive, and prone to errors.
Quality control of plastic clips in injection molding production was previously carried out manually and on a random basis. Human inspectors could overlook the smallest defects such as cracks or molding errors – especially when tired or under time pressure. Only a fraction of the parts were inspected. Defective clips sometimes reached the customer unnoticed, resulting in sorting costs and complaints. Manual inspection also limited scalability as production volumes increased.
With the introduction of an AI-supported visual inspection system, fully automated 100% inspection directly after injection molding became possible for the first time. Defective parts are reliably detected and ejected. The data flows directly into process monitoring—for greater transparency, quality, and productivity.
AI-based visual inspection system Limited inspection rate: Only individual parts are checked at random. Human inspectors tire, and defect detection varies greatly.
Seamless inspection: Every part is reliably checked for relevant defects immediately after the molding process – including features that are difficult to detect.
High scrap and rework: Defective parts reach the delivery stage and have to be re-sorted or returned by the customer.
Cost savings: Scrap and rework are reduced, and customer complaints are eliminated. Two inspection positions could be reassigned.
Scaling problems: Manual inspection limits throughput and causes bottlenecks at higher production volumes.
Process optimization: Error trends are detected early, tool wear is made visible, and production is stabilized – at a consistent cycle rate.
Find out in detail how our customer increased its product quality and saved inspection costs at the same time by using an AI-based visual quality inspection.
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