An Industry 4.0 example: real-time quality control for steel-based mass production using Machine Learning on non-invasive sensor data
Insufficient steel quality in mass production can cause extremely costly damage to tooling, production downtimes and low quality products. Automatic, fast and cheap strategies to estimate essential material properties for quality control, risk mitigation and the prediction of faults are highly desirable. In this work we analyse a high throughput production line of steel-based products. Currently, the material quality is checked using manual destructive testing, which is slow, wasteful and covers only a tiny fraction of the material. To achieve complete testing coverage our industrial collaborator developed a contactless, non-invasive, electromagnetic sensor to measure all material during production in real-time. Our contribution is three-fold: 1) We show in a controlled experiment that the sensor can distinguish steel with deliberately altered properties. 2) 48 steel coils were fully measured non-invasively and additional destructive tests were conducted on samples to serve as ground truth. A linear model is fitted to predict from the non-invasive measurements two key material properties (yield strength and tensile strength) that normally are obtained by destructive tests. The performance is evaluated in leave-one-coil-out cross-validation. 3) The resulting model is used to analyse the material properties and the relationship with logged product faults on real production data of ~108 km of processed material measured with the non-invasive sensor. The model achieves an excellent performance (F3-score of 0.95) predicting material running out of specifications for the tensile strength. The combination of model predictions and logged product faults shows that if a significant percentage of estimated yield stress values is out of specification, the risk of product faults is high. Our analysis demonstrates promising directions for real-time quality control, risk monitoring and fault detection.
Code (0)
등록된 구현이 없습니다.
Tasks
Fault DetectionSimilar Papers 제목 키워드 기반
ML Algorithm Synthesizing Domain Knowledge for Fungal Spores Concentration Prediction
The pulp and paper manufacturing industry requires precise quality control to ensure pure, contaminant-free end products suitable for various applications. Fungal spore concentration is a crucial metric that affects pape…
Time SeriesA novel method for object detection using deep learning and CAD models
Object Detection (OD) is an important computer vision problem for industry, which can be used for quality control in the production lines, among other applications. Recently, Deep Learning (DL) methods have enabled pract…
Objectobject-detectionObject DetectionReal-time MPC with Control Barrier Functions for Autonomous Driving using Safety Enhanced Collocation
The autonomous driving industry is continuously dealing with safety-critical scenarios, and nonlinear model predictive control (NMPC) is a powerful control strategy for handling such situations. However, standard safety …
Autonomous DrivingModel Predictive ControlMotionBricks: Scalable Real-Time Motions with Modular Latent Generative Model and Smart Primitives
Despite transformative advances in generative motion synthesis, real-time interactive motion control remains dominated by traditional techniques. In this work, we identify two key challenges in bridging research and prod…
Motion SynthesisA Comprehensive Framework for Automated Quality Control in the Automotive Industry
This paper presents a cutting-edge robotic inspection solution designed to automate quality control in automotive manufacturing. The system integrates a pair of collaborative robots, each equipped with a high-resolution …
Ensemble Learning