Prediction of Standard Time of the Sewing Process using a Support Vector Machine with Particle Swarm Optimization

• Autorzy: Shao Yibing , Ji Xiaofen , Zheng Menglin , Chen Caiya

Identyfikatory

DOI

10.2478/aut-2021-0037

• Języki publikacji: EN

Abstrakty

EN

Standard time is a key indicator to measure the production efficiency of the sewing department, and it plays a vital role in the production forecast for the apparel industry. In this article, the grey correlation analysis was adopted to identify seven sources as the main influencing factors for determination of the standard time in the sewing process, which are sewing length, stitch density, bending stiffness, fabric weight, production quantity, drape coefficient, and length of service. A novel forecasting model based on support-vector machine (SVM) with particle swarm optimization (PSO) is then proposed to predict the standard time of the sewing process. On the ground of real data from a clothing company, the proposed forecasting model is verified by evaluating the performance with the squared correlation coefficient (R2) and mean square error (MSE). Using the PSO-SVM method, the R2 and MSE are found to be 0.917 and 0.0211, respectively. In conclusion, the high accuracy of the PSO-SVM method presented in this experiment states that the proposed model is a reliable forecasting tool for determination of standard time and can achieve good predicted results in the sewing process.

Słowa kluczowe

EN

standard time; support vector machine; particle swarm optimization; grey correlation analysis

PL

czas standardowy; maszyna wektorów nośnych; optymalizacja rojem cząstek

• Wydawca: Lodz University of Technology, Faculty of Material Technologies and Textile Design
• Czasopismo: Autex Research Journal
• Rocznik: 2022
• Tom: Vol. 22, no. 3
• Strony: 290--297
• Opis fizyczny: Bibliogr. 30 poz.

Twórcy

autor

Shao Yibing

• School of International Education, Zhejiang Sci-Tech University, Hangzhou, China
• Clothing Engineering Research Center of Zhejiang Province, Hangzhou, China

autor

Ji Xiaofen

• School of International Education, Zhejiang Sci-Tech University, Hangzhou, China

autor

Zheng Menglin

• Jnby Co., Ltd., Hangzhou, Zhejiang, China

autor

Chen Caiya

• Excellent Fashion Co., Ltd., Hangzhou, Zhejiang, China

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).