Application of data mining technique in predicting worsted spun yarn quality

Mozafary, Vajihe; Payvandy, Pedram

doi:10.1080/00405000.2013.812552

Cited by 17 publications

(8 citation statements)

References 17 publications

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“…However, a decrease in crimp percentage and float length was observed. Mozafary et al proposed a combined approach, where they used K-means algorithm for data clustering and ANNs for defects detection i.e., yarn unevenness [ 31 ]. The feedforward ANN and Levenberg–Marquardt training function of back propagation were applied in this method and the effectiveness was demonstrated by a comparative analysis with standard ANN results.…”

Section: Classification Based On Textile Processesmentioning

confidence: 99%

Classification of Textile Polymer Composites: Recent Trends and Challenges

2021

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Polymer based textile composites have gained much attention in recent years and gradually transformed the growth of industries especially automobiles, construction, aerospace and composites. The inclusion of natural polymeric fibres as reinforcement in carbon fibre reinforced composites manufacturing delineates an economic way, enhances their surface, structural and mechanical properties by providing better bonding conditions. Almost all textile-based products are associated with quality, price and consumer’s satisfaction. Therefore, classification of textiles products and fibre reinforced polymer composites is a challenging task. This paper focuses on the classification of various problems in textile processes and fibre reinforced polymer composites by artificial neural networks, genetic algorithm and fuzzy logic. Moreover, their limitations associated with state-of-the-art processes and some relatively new and sequential classification methods are also proposed and discussed in detail in this paper.

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Section: Classification Based On Textile Processesmentioning

confidence: 99%

Classification of Textile Polymer Composites: Recent Trends and Challenges

2021

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“…Automation technologies can be utilized in several parts of this productive process. In particular, a large amount of data is created and stored, such as the properties of each yarn (e.g., color, thickness), the configuration of each machine used in the creation process (e.g., spinning, weaving) (Mozafary and Payvandy, 2014), and the results of the specific tests that the company executes. All these data can be processed by Data Mining (DM) and Machine Learning (ML) methods, allowing the discovery of valuable knowledge in order to improve the textile manufacturing process (Yildirim et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology

Ribeiro

Pilastri²,

Moura

et al. 2020

Proceedings of the 22nd International Conference on Enterprise Information Systems

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Textile and clothing is an important world industry that is currently being transformed by the adoption of the Industry 4.0 concept. In this paper, we use Data Mining (DM) technology and the CRoss-Industry Standard Process for DM (CRISP-DM) methodology to model the textile testing process, which assures that products are safe and comply with regulations and client needs. Real-world data were collected from a Portuguese textile company, which has the goal to reduce the number of attempts they take in order to produce a woven fabric. Thus, predicting the outcome of a given test is beneficial to the company because it can reduce the number of physical samples that are needed to be produced when designing new fabrics. In particular, we target two important textile regression tasks: the tear strength in warp and weft directions. To better focus on feature engineering and data transformations, we adopt an Automated Machine Learning (AutoML) during the modeling stage of the CRISP-DM. Several iterations of the CRISP-DM methodology were employed, using different data preprocessing procedures (e.g., removal of outliers). The best predictive models were achieved after 2 (for warp) and 3 (for weft) CRISP-DM iterations.

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“…This device is costly and therefore hardly available for small-scale companies. To reduce the quality measurement cost of businesses, Mozafary and Payvandy 12 have used artificial neural network method to predict the worsted yarn quality. Yildiz et al 13 have made breaking elongation and sewing strength modeling for yarns by using the artificial neural network method.…”

Section: Introductionmentioning

confidence: 99%

Production fault simulation and forecasting from time series data with machine learning in glove textile industry

Seçkin¹,

Seçkin

Coşkun

2019

Journal of Engineered Fibers and Fabrics

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Although textile production is heavily automation-based, it is viewed as a virgin area with regard to Industry 4.0. When the developments are integrated into the textile sector, efficiency is expected to increase. When data mining and machine learning studies are examined in textile sector, it is seen that there is a lack of data sharing related to production process in enterprises because of commercial concerns and confidentiality. In this study, a method is presented about how to simulate a production process and how to make regression from the time series data with machine learning. The simulation has been prepared for the annual production plan, and the corresponding faults based on the information received from textile glove enterprise and production data have been obtained. Data set has been applied to various machine learning methods within the scope of supervised learning to compare the learning performances. The errors that occur in the production process have been created using random parameters in the simulation. In order to verify the hypothesis that the errors may be forecast, various machine learning algorithms have been trained using data set in the form of time series. The variable showing the number of faulty products could be forecast very successfully. When forecasting the faulty product parameter, the random forest algorithm has demonstrated the highest success. As these error values have given high accuracy even in a simulation that works with uniformly distributed random parameters, highly accurate forecasts can be made in real-life applications as well.

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Application of data mining technique in predicting worsted spun yarn quality

Cited by 17 publications

References 17 publications

Classification of Textile Polymer Composites: Recent Trends and Challenges

Classification of Textile Polymer Composites: Recent Trends and Challenges

Predicting the Tear Strength of Woven Fabrics Via Automated Machine Learning: An Application of the CRISP-DM Methodology

Production fault simulation and forecasting from time series data with machine learning in glove textile industry

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