Manufacturing Inspection Challenges and Opportunities for AI
Whether entities are manufacturing cars, semiconductor chips, smartphones, or food and beverages, production quality and yield are two of the industry’s top performance metrics. Poor production quality control results in significant operational and financial costs in the form of reworked parts, scrap generated, reduced yield, increased work in process inventory, post-sale recalls, warranty claims, and repairs.
Manufacturing processes typically include one or more steps where the product is visually inspected for defects. Typically, visual inspection is a highly manual process that can be time consuming and prone to errors. Over the years, rule-based visual inspection machines have also emerged.
However, each approach has drawbacks:
- Manual inspection is subject to operator perception and experience, which impacts consistency.
- Traditional inspection machinery needs to be programmed, is not flexible, and cannot adapt to product changes.
- Existing machine vision-based inspection can only detect a handful of defects at a time.
The manufacturing industry is no stranger to innovation, from the days of mass production, to lean manufacturing, to six sigma and, more recently, to enterprise resource planning. Artificial intelligence (AI) promises to bring even more innovation to the forefront. On paper, there are multiple benefits of using AI:
- Reduced cognitive load for operators, less defects slipped
- No programming required, adapts to product changes
- Detect hundreds of areas of interests on a product in seconds