Machine Vision Supported Quality Control Applications in Rotary Switch Production by Using Both Process FMEA and Design FMEA

Öz

Emerging in the past few decades, Industry 4.0 has wide effects over production lines with an increasing number of novel applications. These applications implement more than one of the tools of Industry 4.0. These tools include but are not limited to internet of things (IoT), big data, cloud computing, artificial intelligence, augmented reality, virtual reality, machine to machine communication (M2M), smart robot applications, etc. The aim of these efforts is mainly to acquire smarter manufacturing systems. With spreading Industry 4.0 methodologies, the role of sensors became more important to respond to new demands. One of the most important sensor types in this sense is the camera which now has wide variants in different forms. Applying machine vision algorithms via cameras grants optimization of many critical processes. In this perspective, the quality of both product and process should be handled as a key performance indicator that may be continuously enhanced for excellence. Machine vision algorithms may be adapted to check and manage quality in designated control points on the production lines. This study focuses on the control of the quality of rotary switches that are widely used in household appliances like ovens and washing machines. Rotary switches are critical components of an appliance since they direct the flow of electricity within the product. A failure in the functionality of this component directly causes the failure of the main product. Hence, the quality rate of rotary switches should be calculated in defective parts per million (dppm) units. An intense quality control procedure is required to achieve low dppm rates during production. As a real-life application, a camera system is integrated into the rotary switch production line on a selected point. Classification algorithms are developed on a cost-effective platform to perform visual quality checks of the rotary switches and qualify as “Ok” or “Defective”. The selected point ensures a high percent check of quality criteria while enabling repair of the defective parts with minor interventions. The aim of this control is to identify a defective rotary switch as soon as possible since most of the defects are irreversible once the rotary switch is totally produced or even some processes are completed. In this case, the entire product should be set apart for scrap. Another originality of our study is applying both Process Failure Mode and Effect Analysis (PFMEA) and Design Failure Mode and Effect Analysis (DFMEA) together. There is almost no referenced study in the literature. Benchmark comparisons are conducted upon completing the integration of the new system to the production line. As a result, enhancements in the quality, cost, and production speed parameters are achieved with a cost-effective smart system. Additional capabilities are added to the system, namely data analyzing online data feeding.

Anahtar Kelimeler

• Machine Vision
• Industry 4.0
• Quality Control
• FMEA
• Poka Yoke

Kaynakça

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