Cyber-Physical Quality Systems in Manufacturing
Dr. Atul
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Abstract
Digital Twin-based Cyber-Physical Quality System (DT-CPQS) concept involves automated quality checking, simulation, and prediction of manufacturing operations to improve production efficiency and flexibility as part of Industrie4.0 initiatives. DT-CPQS will provide the basis for the manufacturing process to march towards an autonomous quality platform for zero defect manufacturing in the future. Analysing sensor data from the CNC machine and vision monitoring system it was concluded that there was enough signal data to detect quality issues in a part being machined in advance using statistical/mathematical models (Smart PLS) and using machine learning algorithms. This allows the operator to take corrective actions before the resultant part ends in a quality failure and reduces the inspection time. The proposed approach forms the basis in expanding this concept to a large machine shop wherein by monitoring various parameters of the machines and state variables of the tools we can detect quality issues and develop an automated quality system using machine learning techniques.
Key takeaways
- However, some of the projects/work/papers worth mentioning are: (a) In-process machine vision monitoring of tool wear for Cyber-Physical Production Systems In this paper (Rmulo,2020) the author has proposed an in-process machine vision monitoring of tool wear integrated into a production system based on the CPS wherein through a four-phased approach (b) Measurements of Tool Wear Parameters Using Machine Vision System Here they have used digital cameras to capture tool wear images and using image processing techniques identify the tool wear zone to take appropriate action.
- Accordingly, we used the Principal This step involves recording the various machining, cutting, tool, work piece machine parameters, environmental conditions and vibration data of all equipment's/tool while they are in good condition and their effect on the inspection parameters mentioned above This step involves create the training set (reference) based on both the good and bad conditions of the above parameters and their effects of these on the quality and then use the appropriate Machine Learning Techniques Identify anomaly condition based on reference and work out the amount of deviation (score) which has the least effect on the inspection parameters This step involves recording the various machining, cutting, tool, work piece, machine parameters, environmental conditions and vibration data of all equipment's/tools while they are in bad condition and their effect on inspection parameters like dimensions tolerances, surface finish etc .
- Objective: Use the sensor signals from the CNC machine to detect dimensional accuracy and surface finish due to wearing out of a cutting tool using machine learning techniques so that the CNC operator can detect in real-time that the cutting tool is worn out and take corrective action before the resultant parts end in a quality failure.
- Hypothesis: Digital Twin based Cyber-Physical Quality System for tool wear can predict the failure of the tool with 95% accuracy in real-time.
- Digital Twin based Cyber-Physical Quality System for tool wear can predict the failure of the tool with 95% accuracy in real-time.
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