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

Shao, Yang; Ji, Xiaofen; Zheng, Menglin; Chen, Caiya

doi:10.2478/aut-2021-0037

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…Shao et al [21] tried the use of smart technology in the management of the sewing process, standard time in the process was predicted and computed, a machine learning prediction model was created, and model parameters were adjusted. Six factors such as sewing length, stitch density, bending stiffness, fabric weight, production amount, drape coefficient, and length of service were identified by correlation analysis.…”

Section: Related Research Workmentioning

confidence: 99%

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Improving Quality, Productivity, and Cost Aspects of a Sewing Line of Apparel Industry Using TQM Approach

Joy,

Hawlader,

Rahman

et al. 2024

Mathematical Problems in Engineering

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Total quality management (TQM) is a methodical strategy to minimize defects through six processes including choosing the manufacturer, gathering information, identifying the issues, analyzing the current situation, and coming up with solutions, putting the plan into action, data collection, and result analysis. The study investigates how quality, productivity, and cost aspects of a sewing line can be improved by implementing (TQM) approach in an apparel manufacturing industry in Bangladesh. This research concentrates on reducing four primary types of faults in the sewing department of DMC Apparels Ltd., such as uncut thread, up-down, broken stitch, and joint stitch. Proper information on several stitching errors was provided by the operators and helpers and those are used to assess the study in the next step. Performing the Pareto analysis and histograms to identify the primary concerns are part of the analyzing process. Moreover, using the 10 TQM pillars, the implementation step entails establishing the TQM mindset in the sewing line of the apparel industry. Several data-gathering methods are utilized to analyze primary data throughout the study’s final phase. To analyze the acquired data, Microsoft Excel was employed. The percentage of defect per hundred unit was decreased by 1.51% after optimization, as the recommended solutions’ impact on the defect level in the section was found to be quite significant. Significant parameters such as efficiency, quality, and cost aspect were analyzed and showed positive changes after implementing the TQM approach. The new technical layout also allows for the saving of five employees and a 3,000-min labor expense reduction. Finally, the research was completed by analyzing the basic pitch time graph and calculating the overall equipment effectiveness.

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Section: Related Research Workmentioning

confidence: 99%

“…Next, a novel forecasting approach is put up to estimate the sewing process's standard time. By analyzing the performance using the mean-square error (MSE) and squared correlation coefficient, the suggested forecasting model is validated [21].…”

Section: Related Research Workmentioning

confidence: 99%

Improving Quality, Productivity, and Cost Aspects of a Sewing Line of Apparel Industry Using TQM Approach

Joy,

Hawlader,

Rahman

et al. 2024

Mathematical Problems in Engineering

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“…For example, the "air" node property is set to msg.payload.air to display the current air velocity. The output current is an important indicator for judging the state of the battery [11]. In this paper, whether the battery is in a state of peroxidation is judged by predicting the output current of the SOFCs.…”

Section: Monitoring Designmentioning

confidence: 99%

“…In addition, predicting abnormal measurement parameter data of fuel cells, such as charge/discharge current, charge/discharge voltage, humidification during operation, and gas pressure, can help identify possible faults in advance. Shao et al [11] used PSO to optimize the SVM algorithm for standard time-determined forecasting. The test results show that the square correlation coefficient is 0.917, and PSO-SVR can achieve a good prediction effect.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a low-code monitoring system for solid oxide fuel cells

Sun,

Guan,

Wang

2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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Solid oxide fuel cells (SOFCs) have the advantages of high conversion efficiency, low working noise, and environmental protection. Thus, they are widely used in large-scale power generation, combined heat power, peak load-regulating energy storage, and other fields. In the actual working process of SOFCs, the battery performance will decline sharply due to extreme conditions such as overtemperature and peroxide, and the service life will be terminated. Remote monitoring of fuel cells can detect problems in time and restore the battery's performance by restoring the battery, thereby extending its service life. The data generated by fuel cells during operation have a strong temporal correlation. To discover the future state of fuel cells in time and solve the problem that a large number of codes are needed for monitoring, this paper designs a remote monitoring system of fuel cells with low-code based on Node-RED and Ali Cloud. The output current of fuel cells is predicted by using the improved back propagation neural network based on Levenberg-Marguardt (LM-BPNN) algorithm. The test result of LM-BPNN shows that the test samples have a root mean square error of 0.172, a mean absolute percentage error of 3.85%, and a coefficient of determination of 0.998.

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“…Y. Shao et al showed a strong correlation between the following parameters in the sewing process to determine sewing time: fabric weight, stitch density, drape coefficient, bending stiffness, seam length, production quantity and operating time [16].…”

Section: Introductionmentioning

confidence: 99%

Use of MTM, RAV and ZAK Methods in Determining Working Methods and Time Norms in Technological Operations of Sewing Clothes

Kirin,

Hursa Šajatović,

Firšt Rogale

2024

Processes

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The paper presents the application of MTM, RAV and ZAK methods in the determination of working methods and time norms for the technological process of sewing. The technological operation of sewing the back sleeve seam on a men’s jacket is used for the study. The paper presents the designed workplace in a real production process. In addition, the appropriate working method and the time norm are determined using the MTM (Method Time Measurement), RAV (determination of sewing time for straight seams) and ZAK (determination of sewing time for curved seams) methods. The aim of the study was to combine the methods for determining different types of time (manual, machine-hand and machine) for performing technological operations and precisely calculate the time norms. For the described methods of determining the production time, computer programs were developed that speed up the calculation of the time norm. If the working method is defined in advance and the characteristics of the sewing machine are known, the time norm for each technological operation can be determined in advance. Based on these calculations, it is possible to determine the total time for the production of clothing in advance and thus to plan and schedule the production process and plan the delivery dates to customers. The results show that the technical equipment of the sewing machine and the working method have an influence on the structure of the technological sewing process. By choosing an appropriate working method, the workload and fatigue of the workers is reduced, as an appropriate number of auxiliary hand sub-operations are used. In this way, the appropriate time required to perform the technological operation is achieved.

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

Cited by 8 publications

References 11 publications

Improving Quality, Productivity, and Cost Aspects of a Sewing Line of Apparel Industry Using TQM Approach

Improving Quality, Productivity, and Cost Aspects of a Sewing Line of Apparel Industry Using TQM Approach

Implementation of a low-code monitoring system for solid oxide fuel cells

Use of MTM, RAV and ZAK Methods in Determining Working Methods and Time Norms in Technological Operations of Sewing Clothes

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