Abstract

Although textile production is heavily automation-based, it is viewed as a virgin area with regard to Industry 4.0. When the developments are integrated into the textile sector, efficiency is expected to increase. When data mining and machine learning studies are examined in textile sector, it is seen that there is a lack of data sharing related to production process in enterprises because of commercial concerns and confidentiality. In this study, a method is presented about how to simulate a production process and how to make regression from the time series data with machine learning. The simulation has been prepared for the annual production plan, and the corresponding faults based on the information received from textile glove enterprise and production data have been obtained. Data set has been applied to various machine learning methods within the scope of supervised learning to compare the learning performances. The errors that occur in the production process have been created using random parameters in the simulation. In order to verify the hypothesis that the errors may be forecast, various machine learning algorithms have been trained using data set in the form of time series. The variable showing the number of faulty products could be forecast very successfully. When forecasting the faulty product parameter, the random forest algorithm has demonstrated the highest success. As these error values have given high accuracy even in a simulation that works with uniformly distributed random parameters, highly accurate forecasts can be made in real-life applications as well.

Highlights

  • Considered as having started with the introduction of cyberphysical systems, Industry 4.0 has begun to change the way industrial organizations operate

  • This study aims to provide a method to forecast the possible amount of faulty products and the production period based on the production planning information that is created using the information obtained during the ordering phase

  • For the purpose of the study, since data cannot be obtained from businesses because of commercial concerns and confidentiality, the data related to the production process have been created by way of simulation based on production parameters of a glove manufacturer

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Summary

Introduction

Considered as having started with the introduction of cyberphysical systems, Industry 4.0 has begun to change the way industrial organizations operate. Technologies such as artificial intelligence (AI), additive manufacturing, Internet of things, augmented reality, smart robotic systems, big data, and cloud computing support the efficiency and speed of many industrial processes such as production, planning, R&D, quality management, supply, order, and so on. Looking at where each sector is with regard to the application of Industry 4.0 technologies or the transformation process of businesses, it can be seen in various studies that defense, health, automotive, and white goods industries have progressed to more advanced levels than textile, leather, food, and furniture.[1,2,3] textile production is heavily automation-based, it is viewed as a virgin area. When the developments in informatics are integrated into the textile sector, efficiency is expected to increase by way of higher productivity, lower costs, shorter processes, and fewer faults.[4]

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