Intelligent Visual Quality Control System Based on Convolutional Neural Networks for Holonic Shop Floor Control of Industry 4.0 Manufacturing Systems

Abstract

The article presents research on industrial quality control system based on AI deep learning method. They are a part of larger project focusing on development of Holonic Shop Floor Control System for integration of machines, machine operators and manufacturing process monitoring with information fl ow in whole production process according to Industry 4.0 requirements. A system connecting together machine operators, machine control, process and machine monitoring with companywide IT systems is developed. It is an answer on manufacture of airplane industry requirements. The main aim of the system is full automation of information fl ow between a management level and manufacturing process level. Intelligent, fl exible quality control system allowing for active manufacturing optimization on the base of achieved results as well as a historical data collection for further Big Data analysis is the main aim of the current research. During research number of selected AI algorithms were tested for assessing their suitability for performing tasks identifi ed in real manufacturing environment. As a result of the conducted analyzes, Convolutional Neural Networks were selected for further study. Number of built Convolutional Neural Networks algorithms were tested using sets of data and photos from the production line. A further step of research will be focused on testing a system in real manufacturing process for able possible construct a fully functional quality control system based on the use of Convolutional Neural Networks.

A Prestudy of Machine Learning in Industrial Quality Control Pipelines

“…Several papers from the Related Work section are comparable to our solutions. For example, we use CNNs in detecting imperfections in oven faceplates, which similar application is used in [11]. Although the accuracy of their proposed network is higher, we showcase that there is underlying knowledge which can be used in pre-trained networks that brings forth faster training.…”

“…Efforts are made into automating quality control processes in order to be closer to Industry 4.0. In [11], they mention several manufacturing paradigms with the main focus being on Holonic Shop Floor Control Systems. To aid in bridging the flow of information between a management level and a manufacturing process level, several CNNs were trained in order to find flaws in cast products, namely impellers of submersible pumps.…”

“…Results [5] Using feature elimination and classification threshold search to find best models for classifying good or bad welds Logistic regression model -maximum probability of correct decision (MPCD): 1 [12] Testing several machine learning models to predict dimensional defects in cars Both XGBoost and Random Forest -ROC AUC: 0.97 [14] Training machine learning models to detect defective units during PCB manufacturing Gradient boosted tree -precision: 93.1%, recall: 89.9%, accuracy: 92.6% [1] Proper seal detection in thermoforming food packaging ResNet18 -accuracy: 95% [11] Convolutional neural networks in detecting faults in cast metal products Custom CNN -accuracy: 99.82% The small number of learning examples was insufficient for a hard real-life problem, as expected.…”

A product-driven system with an evolutionary algorithm to increase flexibility in planning a job shop

“…Industry 4.0 requires manufacturing systems to be flexible, dynamic, and able to react immediately to disruptions. These are critical aspects in the design stage, for which it is necessary to resort to modeling that integrates the advantages of traditional modeling [ 1 ]. Such models are the basis of the manufacturing process control system [ 2 ], and should consider the integration of historical data with data from the system in real-time.…”

Demonstrator of a Digital Twin for Education and Training Purposes as a Web Application

“…The application of virtual and augmented reality tools in DT design offers many possibilities. It allows a new approach to controlling industrial equipment and making measurements in a modified image of the world, for example, distance measurement for positioning an object in space, Lalik, Flaga [7] or automation of vision-based quality control -Oborski, Wysocki [8], Gaska et al [9]. DT technology provides new opportunities in the field of robot programming; the concept of hybrid robot programming was presented by Kuts et al [10].…”