Using EdgeX Foundry to use sensor data to automate surface quality inspection for a manufacturing plant that produces Piston Rods used in different applications like Utility, Mining, Construction and Earth Moving.
Written by LF Edge members from Wipro. For more information about Wipro, visit their website.
In the manufacturing plant, the production of piston rods goes through multiple stages like induction hardening, friction welding, threading & polishing. Each of these stages have a quality checkpoint to detect defects in early stages and to ensure that the rods produced are in line with design specifications & function properly. After it goes through rigorous multi stage process, it reaches final station where surface level quality inspection happens. At this stage, the quality inspection happens manually, requiring highly skilled & experienced human inspector to look for different types of defects like scratches, material defects & handling defects on the surface of rods. Based on the final quality inspection results, it goes either to packaging & shipment area or to rework or scrap area.
Quality check at every stage is extremely critical to prevent quality problems down the line leading to recalls & reputational damages. The problem with manual inspections – they are costly, time consuming and heavily dependent on human intelligence & judgement. “Automated surface quality inspection” is an image analytics solution based on AI / ML that helps overcome these challenges. The solution identifies and classifies defects based on image analytics to enable quality assessment and provides real-time visibility to Key Performance Indicators through dashboards to monitor plant performance.
EdgeX architectural tenets, production grade readily available edge software stack, visibility of long term support with bi-annual release roadmap and user friendly licensing for commercial deployments made us adopt EdgeX as the base software stack.
The readily available IoT gateway functionalities helped us focus more on building business application specific components than the core software stack needed for an edge gateway. This helped us in rapid development of proof of concept for the use case we envisioned.
Once the rod reaches inspection station, the gateway triggers the camera to take surface pictures. The image captured is fed into the inference engine running on gateway, which looks for the presence of different types of surface defects. The inference output is fed into the business application hosted on cloud to provide actionable insights with rich visual aids.
The analysis of historical data for similar pattern of defects, correlating data from OT systems with inference output for a given time period and providing feedback to OT systems for potential corrective actions are possible future enhancements.
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