A Fuzzy AHP Perspective on Improving Yarn Winding Productivity in Textile Industry

Bathrinath, S.; Dhanasekar, M.; Koppiahraj, K.; Priyanka, Ramesh

doi:10.1007/978-981-99-3761-5_49

Cited by 1 publication

(2 citation statements)

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“…Textile manufacturing processes such as warping and sizing are essential for the manufacturing site. Exact production information and procedure control can enhance manufacturing efficiency [ 4 , 11 , 12 ]. Taking weaving as an example, it encompasses a variety of processes such as winding, warping, sizing, and drawing.…”

Section: Related Workmentioning

confidence: 99%

“…Drean et al (2022) provided practical calculations in weaving preparation to ensure consistent quality and prevent failure of events during subsequent processing [ 4 ]. Bathrinath et al used the fuzzy analytical hierarchy process to analyze problems in the yarn winding process and designed a corresponding strategy to improve productivity [ 11 ]. Recently, predictive maintenance has been applied in textile production management, including machines [ 13 , 14 ] and scheduling maintenance [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

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Cloud-Based Machine Learning Methods for Parameter Prediction in Textile Manufacturing

Chang,

Lin,

Hung

2024

Sensors

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In traditional textile manufacturing, downstream manufacturers use raw materials, such as Nylon and cotton yarns, to produce textile products. The manufacturing process involves warping, sizing, beaming, weaving, and inspection. Staff members typically use a trial-and-error approach to adjust the appropriate production parameters in the manufacturing process, which can be time consuming and a waste of resources. To enhance the efficiency and effectiveness of textile manufacturing economically, this study proposes a query-based learning method in regression analytics using existing manufacturing data. Query-based learning allows the model training to evolve its decision-making process through dynamic interactions with its solution space. In this study, predefined target parameters of quality factors were first used to validate the training results and create new training patterns. These new patterns were then imported into the solution space of the training model. In predicting product quality, the results show that the proposed query-based regression algorithm has a mean squared error of 0.0153, which is better than those of the original regression-related methods (Avg. mean squared error = 0.020). The trained model was deployed as an application programing interface (API) for cloud-based analytics and an extensive auto-notification service.

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Section: Related Workmentioning

confidence: 99%