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.
Highlights
Self-optimization is possible with the advent of Cyber-Physical Systems (CPS) which composes of networks of software /hardware components (Wright, 2013) that control physical processes
We intend to implement a Cyber-Physical Quality System (CPQS) that can predict the quality of a part being manufactured using an unsupervised machine learning algorithm based on the correlation of the data from various machines on the shop floor and in-process machine vision monitoring systems (Miranda, 2017)
Phases a) Phase 1: This phase involves defining the lifecycle of the CPQS - requirement analysis, finalize the development process in terms of system specification b) Phase 2: During this phase, we look at dynamic ways to represent the model in Smart PLS
Summary
Self-optimization is possible with the advent of Cyber-Physical Systems (CPS) which composes of networks of software /hardware components (Wright, 2013) that control physical processes This complex manufacturing system that monitors and triggers actions based on decentralized decisions in real-time for the physical processes is called Cyber-Physical Production Systems (CPPS) (Lu et al, 2020). We intend to implement a Cyber-Physical Quality System (CPQS) that can predict the quality of a part being manufactured using an unsupervised machine learning algorithm based on the correlation of the data from various machines on the shop floor and in-process machine vision monitoring systems (Miranda, 2017) This would lead to a reconfigurable quality inspection mechanism wherein depending on the condition of the machine /tools the quality process for the product can be changed on the fly thereby reducing the cycle time required to manufacture the components thereby increasing productivity and reducing the cost of quality. An example is the Reference Architectural Model Industrie 4.0 (RAMI 4.0) (Henkel, 2016)
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