| Title: | Enhancing manufacturing excellence with Lean Six Sigma and zero defects based on Industry 4.0 |
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| Authors: | ID Ly Duc, M. (Author)
ID Hlavaty, L. (Author)
ID Bilik, P. (Author)
ID Martinek, R. (Author) |
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| Files: |  APEM18-1_032-048.pdf (1,47 MB)
MD5: D14B6488D0E5C3AB8F09A245C1117121
 https://apem-journal.org/Archives/2023/Abstract-APEM18-1_032-048.html
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | Improving quality, enhancing productivity, redesigning machining tools, eliminating waste in production, and shortening lead time are all objectives aimed at improving customer satisfaction and increasing profitability for manufacturing companies. This study combines lean manufacturing and six sigma techniques to form a technique called Lean Six Sigma (LSS) by using the DMAIC (Define-Measure-Analysis-Improve-Control) model. This study proposes to use statistical test models to analyze real data collected directly from the operator. The study proposes to use the Taguchi optimization technique to determine the optimal conditions for oil dipping tanks of molybdenum materials. In addition, the study also proposes a computer vision technique to recognize objects using color recognition techniques running on the LABVIEW software platform. This study builds a digital numerical control (DNC) model operating on digital signal processing techniques, linking the data of each process together. The results reduced the rate of defective parts in the whole processing stage from 6.5 % to zero defects, the whole processing line production capacity increased by 7.9 %, and the profit of the whole production line was USD 35762 per year. As a valuable external outcome, the conclusion of the LSS project fostered a spirit of continuous improvement. The utilization of research results from the research environment in the actual production setting is significantly enhanced for the operator. The LSS model is deployed with specific tasks and targets for each member of the LSS project team, and the processing conditions for each specific stage are optimized, such as the oil dipping process and hole grinding process. Industry 4.0 techniques, including computer vision, digital numerical control, and commercial software such as LabVIEW and MINITAB, are optimized for use, simplifying machining operations. Some proposed directions for future research are also presented in detail. For example, studying the improvement of the quality of the 220 V power supply through harmonic mitigation in processing factories is an intriguing area of investigation. Additionally, exploring data security for big data in the context of Industry 4.0 would be a valuable study to enhance customer satisfaction with big data technology in the future. |
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| Keywords: | lean six sigma, industry 4.0, manufacturing, smart manufacturing, zero defect manufacturing, DMAIC, Define-Measure-Analysis-Improve-Control, computer vision |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 17.11.2022 |
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| Article acceptance date: | 06.04.2023 |
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| Publication date: | 29.04.2023 |
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| Publisher: | Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering |
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| Year of publishing: | 2023 |
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| Number of pages: | str. 32-48 |
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| Numbering: | Vol. 18, no. 1 |
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| PID: | 20.500.12556/DKUM-96992  |
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| UDC: | 658.5 |
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| ISSN on article: | 1854-6250 |
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| COBISS.SI-ID: | 267957507 |
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| DOI: | 10.14743/apem2023.1.455 |
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| Copyright: | Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
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| Publication date in DKUM: | 10.02.2026 |
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| Views: | 148 |
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| Downloads: | 2 |
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| Metadata: |    |
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| Categories: | Misc.
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