Suitability of Self-Organization for Different Types of Production

Krockert, Martin; Matthes, Marvin; Munkelt, Torsten

doi:10.1016/j.promfg.2021.07.020

Cited by 3 publications

(1 citation statement)

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“…Radical change in production systems organisation paradigm is forced also by rapid increasing of products complexity and new hard to manufacture materials development [2]. Growing sophistication of production process and connected with it technical risk is necessary [3].…”

Section: Introductionmentioning

confidence: 99%

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

Oborski¹,

Wysocki²

2022

Adv. Sci. Technol. Res. J.

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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.

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Section: Introductionmentioning

confidence: 99%