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.
Highlights
Countries aspiring to lead these technological changes and remain in industrial leadership positions have strategically positioned themselves for the new type of cyber–physical infrastructure that will emerge from the Industrial Internet of Things (IIoT) and data science
The goal of this paper is to present a soft sensor deep neural network (DNN) that performs a classification of images from high-resolution cameras towards a fully computer vision Optical Quality Control (OQC) of the printing cylinder of a global leading player in the Printing Industry 4.0
Due to the automation by means of the soft DNN sensor, the costs associated with OQC could be drastically reduced
Summary
Countries aspiring to lead these technological changes and remain in industrial leadership positions have strategically positioned themselves for the new type of cyber–physical infrastructure that will emerge from the Industrial Internet of Things (IIoT) and data science. The United States launched the Manufacturing Leadership Coalition (SMLC) [2] in 2011. Other notable examples include “China Manufacturing 2025” [3] that seeks to elevate advanced manufacturing technology, or Japanese’s “Society 5.0” [4] with a holistic focus on the safety and well-being of humans through cyber–physical systems. The Japanese manufacturer has consistently gained a competitive edge towards its competition by providing its value stream elements with the ability not to pass defects to the step in the manufacturing process [5].
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