Could you please give me some comments on my abstract of a paper as below. Thank you very much for your kind support!
"Defects in textile manufacturing process cause a significant waste of resources and affect the quality of product. This is challenging manufacturers to maintain growth and competitiveness in Industry 4.0. To solve the problem, models for predicting defects should be developed to assist shop floor operators. However, there is a lack of studies and models for solving the problem. Focusing on realistic needs, this study aims to develop online defect prognostic models for textile manufacturing. In particular, data from the manufacturing processes are collected in time series. Then, control charts are used to transform the collected data into region data of product. Based on these data, back-propagation neural networks are designed for predicting defects at each stage. In addition, an experiment was designed to validate the proposed approach. The results have shown the robustness and efficiency of the proposed model. The model can implemented in practice to predict defects in advance that assists operators taking correct actions to prevent defect products and reduce waste."
need your support
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hntodn
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alexanderfinn
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Re: need your support
It is good and the small imperfections would probably not be noticed. however, as you have asked, I have put some changes in capitals here:
"Defects in textile manufacturing process cause a significant waste of resources and affect the quality of product. This is challenging manufacturers to maintain growth and competitiveness in Industry 4.0. To solve the problem, models for predicting defects should be developed to assist shop floor operators. However, there is a lack of studies and models TO SOLVE the problem. Focusing on realistic needs, this study aims to develop online defect prognostic models for textile manufacturing. In particular, data from the manufacturing processes IS collected in time series. FOLLOWING THIS, control charts are used to transform the collected data into regionAL data of THE product. Based on THIS data, back-propagation neural networks are designed TO PREDICT defects at each stage. In addition, an experiment IS designed to validate the proposed approach. The results SHOW the robustness and efficiency of the proposed model. The model can implemented in practice to predict defects in advance WHICH WILL ASSIST operators IN taking APPROPRIATE action to prevent defectIVE products and TO reduce waste."
"Defects in textile manufacturing process cause a significant waste of resources and affect the quality of product. This is challenging manufacturers to maintain growth and competitiveness in Industry 4.0. To solve the problem, models for predicting defects should be developed to assist shop floor operators. However, there is a lack of studies and models TO SOLVE the problem. Focusing on realistic needs, this study aims to develop online defect prognostic models for textile manufacturing. In particular, data from the manufacturing processes IS collected in time series. FOLLOWING THIS, control charts are used to transform the collected data into regionAL data of THE product. Based on THIS data, back-propagation neural networks are designed TO PREDICT defects at each stage. In addition, an experiment IS designed to validate the proposed approach. The results SHOW the robustness and efficiency of the proposed model. The model can implemented in practice to predict defects in advance WHICH WILL ASSIST operators IN taking APPROPRIATE action to prevent defectIVE products and TO reduce waste."