Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0

Abstract

Rapid and accurate industrial inspection to ensure the highest quality standards at a competitive price is one of the biggest challenges in the manufacturing industry. This paper shows an application of how a Deep Learning soft sensor application can be combined with a high-resolution optical quality control camera to increase the accuracy and reduce the cost of an industrial visual inspection process in the Printing Industry 4.0. During the process of producing gravure cylinders, mistakes like holes in the printing cylinder are inevitable. In order to improve the defect detection performance and reduce quality inspection costs by process automation, this paper proposes a deep neural network (DNN) soft sensor that compares the scanned surface to the used engraving file and performs an automatic quality control process by learning features through exposure to training data. The DNN sensor developed achieved a fully automated classification accuracy rate of 98.4%. Further research aims to use these results to three ends. Firstly, to predict the amount of errors a cylinder has, to further support the human operation by showing the error probability to the operator, and finally to decide autonomously about product quality without human involvement.

Smart Soft Sensor Design with Hierarchical Sampling Strategy of Ensemble Gaussian Process Regression for Fermentation Processes

“…where L(X Ξ) is the likelihood function of X. Given an initial parameter set Ξ (1) , EM algorithm in this way can produce a sequence of GMM parameters Ξ (1) , Ξ (2) , · · · , Ξ (s) , · · · by performing E step and M step successively, where s denotes the iteration times. The E step and M step iterate until they converge, which can be successfully carried out, as follows [21]: E step: Calculate the posterior probability of ith training data point with kth component C k in the sth iteration:…”

“…In recent years, artificial intelligence (AI) and machine learning (ML) have contributed to the great advancement of the Industry 4.0 [1,2]. It aims to ensure the high-quality control of production-based industries in the increasingly complex environment, such as the increased process automation, more efficient data analysis, lower human effort, safer working environment, and so on.…”

Towards Customer-Centric Additive Manufacturing: Making Human-Centered 3D Design Tools through a Handheld-Based Multi-Touch User Interface

“…In recent years extensive research on UIs has been conducted pursuing the design and development of new HMIs in I4.0 [28]. UIs are understood as a core component to facilitate machinery support in order to increase the efficiency, and, in general, enhance the capacities of human personnel when performing tasks such as planning [29] and product design [30], quality control [31], or maintenance [24], being commonly used, for instance, to transmit instructions to operators, provide navigation services [32], or to enable operators to control industrial robots [33]. Hence, generally speaking, it is possible to state that UI-specific works present in the related literature have been mainly aimed at improving and expanding the operators' working environment.…”

Quantum Strategic Organizational Design: Alignment in Industry 4.0 Complex-Networked Cyber-Physical Lean Management Systems

“… Lean Management. Lean management systems in a cyber-physical environment of Industry 4.0 are described as socio-technical structures that are designed to consistently reduce the variability of value creating processes and, therefore, increase their effectiveness and profitability [ 9 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ]. Variability in this context is understood as any deviation from the desired process state.…”