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

Seçkin, Mine; Seçkin, Ahmet Çağdaş; Coşkun, Aysun

doi:10.1177/1558925019883462

Cited by 23 publications

(8 citation statements)

References 38 publications

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“…With technological advances, it is tried to facilitate information control at the optimum level in the companies. Various artificial intelligence applications are available in textile enterprises (Shamey ve Hussain, 2003;Karthikeyan ve Sztandera, 2010;Seçkin, Seçkin ve Coşkun, 2019). Data processing in the computer environment has given the information using a great opportunity to diversify the information.…”

Section: Discussionmentioning

confidence: 99%

Audits in a Textile Company and Advantages

Seçkin¹,

Koc²,

Seçkin³

2021

acin

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Textile companies must develop strategies to survive in today's competitive and volatile world and flexibly adapt these strategies to today's conditions. In this competitive environment, audits conducted by an internal or external auditor, especially at the request of the customer, enable companies to differentiate themselves from other companies. Audits performed by companies can be classified in various ways. In this study, audit types are classified into internal and external audits depending on the type of auditor. If the audit is conducted by a person working in a company with a contract of pay, it is an internal audit, if not, it is an external audit. The principles and benefits of the audit to the company are explained in the study. In this study, a simulation was prepared to investigate the effect of audit frequency, internal interaction and number of employees in the company on the improvement of the company. In the prepared simulation, relevant changes were processed according to internal parameters on daily basis. As a result, it was found that with sufficient recovery of all parameters within the company, the highest improvement success is achieved when the audit frequency is conducted at least every 3 months. Future studies, information on how the developing information technologies and artificial intelligence provide convenience and perspective for audits are presented.

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Section: Discussionmentioning

confidence: 99%

Audits in a Textile Company and Advantages

Seçkin¹,

Koc²,

Seçkin³

2021

acin

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“…Researches claim that thresholding can detect 90% of defects in a plain textile. Until the 1960s, AI was an algorithm-based system (Seckin et al , 2019; Artificial Intelligence in the Textile Industry-Current and Future Application, 2019). Fabrication began using computers and algorithms after the 1980s (at least in R&D sections).…”

Section: Artificial Intelligence In Textiles Industriesmentioning

confidence: 99%

Artificial intelligence (AI) in textile industry operational modernization

Sikka

Sarkar

Garg

2022

RJTA

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Purpose With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing. Design/methodology/approach The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration. Findings AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances. Originality/value This research conducts a thorough analysis of artificial neural network applications in the textile sector.

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“…These elements play a vital role in increasing performance and productivity. There are many academic studies, especially simulation [20], quality control [21][22], image perception [22], data mining [23,24], decision making in apparel manufacturing [25][26][27][28], advancement of textile manufacturing processes [29], production and demand forecasting in textile industry [30][31]. In the literature review, the use of data mining in the textile industry and the studies on production defects were examined.…”

Section: Literature Reviewmentioning

confidence: 99%

Fabric and Production Defect Detection in the Apparel Industry Using Data Mining Algorithms

Ersöz

ZAHOOR

Ersöz

2021

International Journal of 3D Printing Technologies and Digital Industry

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Nowadays, technology plays a crucial role in fabric production in the textile industry. The demand for high-quality products and rapidly changing economic conditions increase the significance of readymade clothing manufacturers to produce the right quality product. In addition, in order to minimize production errors, to improve and maintain process performance, it is important to identify the sources of variability during manufacturing. The defective fabric is the main reason which is causing harm to the textile business. Therefore, the proper identification of manufacturing defects leads to a successful business. When it comes to extracting meaningful insights from data and knowledge discovery, data mining has proven its significance in various fields such as business, health, finance, and education. As in all other sectors, data mining is widely used in the textile sector too. In this study, it was aimed to determine the main causes of the error with defective production data of a company that produces clothing by using data mining methods. Decision Tree, Naive Bayes, Random Forest, and Gradient Boosted Trees Algorithms were used in the research. Accuracy rate and Cohen's kappa statistics were taken for comparison algorithms in the study. While determining the main reasons for defective products, the factors of which type of products the company produces for its customers, the sizes of the defective products, types of defects, and explanations were taken into consideration. The most common mistakes in sewing production and the main source of the error were evaluated. According to the results, suggestions were made for the company to take various measures.

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

Cited by 23 publications

References 38 publications

Audits in a Textile Company and Advantages

Audits in a Textile Company and Advantages

Artificial intelligence (AI) in textile industry operational modernization

Fabric and Production Defect Detection in the Apparel Industry Using Data Mining Algorithms

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