Kernel Ridge Regression Based Modelling and Anomaly Detection for Temperature Control in Textile Dyeing Processes

Gorgül, Ahmed Ümit; Çom, Mustafa; Sultanoğlu, Sencer

doi:10.1109/icstcc59206.2023.10308428

Cited by 1 publication

(3 citation statements)

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“…Hoque et al applied the least absolute shrinkage selector operator (LASSO) to predict the bursting strength of single jersey cotton plain knitted fabrics [ 30 ], whereas Rabaca et al used logit ridge regression and LASSO to predict business failure [ 31 ]. In a study by Gorgül et al [ 32 ], a kernel ridge regression (KRR) model produced satisfactory anomaly detection results. Gorgül et al recommended the use of KRR for modeling and anomaly detection, specifically in the context of temperature control in textile dyeing processes [ 32 ].…”

Section: Related Workmentioning

confidence: 99%

“…In a study by Gorgül et al [ 32 ], a kernel ridge regression (KRR) model produced satisfactory anomaly detection results. Gorgül et al recommended the use of KRR for modeling and anomaly detection, specifically in the context of temperature control in textile dyeing processes [ 32 ]. Their primary objective was to quickly identify issues with temperature controls, address failures in dyeing machines, and improve the efficiency of dyeing processes [ 32 ].…”

Section: Related Workmentioning

confidence: 99%

“…Gorgül et al recommended the use of KRR for modeling and anomaly detection, specifically in the context of temperature control in textile dyeing processes [ 32 ]. Their primary objective was to quickly identify issues with temperature controls, address failures in dyeing machines, and improve the efficiency of dyeing processes [ 32 ]. Ridge regression was used to predict the demand for textile products [ 33 ].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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%