Vision Based Quality Control and Maintenance in High Volume Production by Use of Zero Defect Strategies

Abstract

Quality inspection in high volume manufacturing often depends on manual decision-making processes and predictive condition based maintenance. Predictive maintenance as a part of defect detection related to indicators and environmental impact in production development. Use of Zero Defect (ZD) methods or Cyber Physical Systems (CPS) will give great value to reduction of scrap rates, fault and defects. Scrap rates is often a huge problem due to different conditions in the production system, and will often cause impact on the delivery to costumer. To provide a self-correcting system by use of machine control data and augmented reality (AR) solutions, input to continuous maintenance tasks on the shop floor can be assisted. CPS, vision based inspection or AR solutions in the production environment provides the opportunity to detect fault in order to prescribe a possible maintenance solutions. The vision inspection as decision-making system may give feedback on detected problems, like contrast of parts, change in geometry, material or smutch on product or on the palette in production. This makes it possible to reduce interoperability and to make self-adjustments in production lines by use of CPS as a system for collaborating computational entities connected to services.