Publication:
Enhancing manufacturing excellence with Lean Six Sigma and zero defects based on Industry 4.0
Enhancing manufacturing excellence with Lean Six Sigma and zero defects based on Industry 4.0
| dc.contributor.author | Ly Duc, M., Hlavaty, L, Bilik, P., Martinek, R. | |
| dc.date.accessioned | 2024-03-07T00:40:49Z | |
| dc.date.available | 2024-03-07T00:40:49Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Improving quality, enhancing productivity, redesigning machining tools, elim-inating waste in production, and shortening lead time are all objectives aimed at improving customer satisfaction and increasing profitability for manufac-turing 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 pro-poses 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 pro-duction 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 opera-tions. 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 intri-guing 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. | |
| dc.identifier.doi | https://doi.org/10.14743/apem2023.1.455 | |
| dc.identifier.uri | http://repository.vlu.edu.vn:443/handle/123456789/12842 | |
| dc.language.iso | en_US | |
| dc.relation.ispartof | Advances in Production Engineering & Management | |
| dc.relation.issn | 1854-6250 | |
| dc.subject | "Lean Six Sigma | |
| dc.subject | Industry 4.0 | |
| dc.subject | Manufacturing | |
| dc.subject | Smart manufacturing | |
| dc.subject | Zero defect manufacturing | |
| dc.subject | DMAIC (Define-Measure-Analysis-Improve-Control) | |
| dc.subject | Computer vision" | |
| dc.title | Enhancing manufacturing excellence with Lean Six Sigma and zero defects based on Industry 4.0 | |
| dc.type | Resource Types::text::journal::journal article | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | #PLACEHOLDER_PARENT_METADATA_VALUE# |
Files
Original bundle
1 - 1 of 1