Demand Forecasting for Textile Products Using Machine Learning Methods

Medina, Héctor; Peña, Mario; Siguenza-Guzmán, Lorena; Guamán, Rodrigo Medardo Abad

doi:10.1007/978-3-031-03884-6_23

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…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 ]. Although the algorithm was applied to textile manufacturing issues and demonstrated strong computational efficiency, its training mechanism is unidirectional and lacks interaction with the solution space.…”

Section: Related Workmentioning

confidence: 99%

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%

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

Chang,

Lin,

Hung

2024

Sensors

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“…proposed a Support Vector Machine (SVM) based SW-supported model for predicting desert locust presence in Somalia, Ethiopia and Kenya using the data from 2000 to 2020 Yasir et al (2022). investigated the significance of endogenous and exogenous indicators of demand forecasting for the textile industry using ML models such as linear regression (LR), support vector regression (SVR) and LSTM Medina et al (2022). proposed an ML model including regression, SVR and KNN in order to predict demand forecasting in the textile industry.…”

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confidence: 99%

Teksti̇l Endüstri̇si̇nde Deri̇n Öğrenme Kullanarak Aşiri Elektri̇k Tüketi̇mi̇ni̇n Önlenmesi̇ne Yöneli̇k Bi̇r Vaka Çalişmasi

YURDOĞLU,

GÜLEÇ

2023

Mühendislik Bilimleri Ve Tasarım Dergisi

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Resources are the most critical input in the manufacturing industry therefore, resource consumption is an essential issue to be minimized. On the other hand, consumption depends on several parameters thus, it is difficult to estimate. Recently, Machine Learning (ML) and Deep Learning (DL) are powerful Artificial Intelligence (AI) subdomains for future prediction in any area. In this paper, a DL-supported electricity prediction method is designed for the textile industry as a case study in order to prevent resource over-consumption while the machines are in the standby state. This method provides dynamic consumption thresholds of electricity consumption by sliding window technique based Long-Short Term Memory (LSTM) model that helps the machines to interrupt manufacturing in their decision. These calculated thresholds are also compared with the results of Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU) as the other DL methods and Automated Regressive Integrated Moving Average (ARIMA) as a traditional method and then the results have been analyzed how close they are to real-time electricity consumption data at standby. According to the results, the LSTM model successfully predicts electricity consumption levels, sends an interrupt signal to Programmable Logic Controller (PLC) unit when the consumption levels reach the threshold and therefore prevents resource over-consumption.

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Demand Forecasting for Textile Products Using Machine Learning Methods

Cited by 2 publications

References 24 publications

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

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

Teksti̇l Endüstri̇si̇nde Deri̇n Öğrenme Kullanarak Aşiri Elektri̇k Tüketi̇mi̇ni̇n Önlenmesi̇ne Yöneli̇k Bi̇r Vaka Çalişmasi

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