TEXTILE DYEING PROCESS AND DYEING RECIPE PREDICTION USING ARTIFICAL INTELLIGENCE

Öz

In this study, a comprehensive analysis is presented on the determination and estimation of the sample color or target color taken from the customer in the laboratory department of textile dyeing companies. The importance of the textile industry in the world and Turkey is also mentioned. In the report published by Statista, it is seen that the textile industry has a share of 3.3% in Turkey and the world. In this work, a sample study was conducted in a textile finishing company and the processes were shared. First, the classical processes used to determine the target color are explained in detail. Then, it was mentioned how the data obtained with the spectrophotometer device is used in color estimation using machine learning methods and artificial neural networks According to the results of the examination, it is seen that the data obtained with the hyperspectral camera device is estimated by the long short-term memory (LSTM) model, since the spectrophotometer device is expensive and does not give accurate results recently. In addition, it has been observed that this model gives better results than the same model created from the data obtained with the spectrophotometer device

Anahtar Kelimeler

• Dyeing Recipe Prediction
• Artificial Intelligence
• Textile Dyehouse
• Color Space
• Spectrophotometer

TEKSTİL BOYAMA SÜRECİ VE YAPAY ZEKA KULLANARAK BOYAMA REÇETESİ TAHMİNİ

Öz

Bu çalışmada tekstil boyama firmalarının laboratuvar bölümünde müşteriden alınan numune renginin veya hedef rengin belirlenmesi ve tahmin edilmesi üzerine kapsamlı bir analiz sunulmaktadır. Tekstil sektörünün dünyadaki ve Türkiye’deki önemine de değinilmektedir. Statista’nın yayınladığı raporda tekstil sektörünün Türkiye’de ve dünyada %3,3’lük bir paya sahip olduğu görülmektedir. Bu çalışmada bir tekstil terbiye firmasında örnek bir çalışma yapılmış ve süreçler paylaşılmıştır. İlk olarak hedef rengi belirlemek için kullanılan klasik süreçler kapsamlı bir şekilde anlatılmıştır. Ardından spektrofotometre cihazı ile alınan verilerin makine öğrenmesi yöntemleri ve yapay sinir ağları kullanılarak renk tahmininde nasıl kullanıldığından bahsedilmiştir. Yapılan inceleme sonuçlarına göre son zamanlarda spektrofotometre cihazının pahalı olması ve kesin sonuç vermemesi sebebiyle hiperspektral kamera cihazı ile elde edilen verilerin uzun kısa-süreli bellek (Long Short-Term Memory-LSTM) modeli ile tahmin edildiği görülmektedir. Ayrıca bu modelin spektrofotometre cihazı ile elde edilen verilerden oluşturulan aynı modele göre daha iyi sonuçlar verdiği gözlemlenmiştir.

Anahtar Kelimeler

• Boyama Reçetesi Tahmini
• Yapay Zekâ
• Tekstil Boyahanesi
• Renk Uzayı
• Spektrofotometre

Kaynakça

1. Almodarresi, E. S. Y., Mokhtari, J., Almodarresi, S. M. T., Nouri, M., & Nateri, A. S. (2013). A scanner based neural network technique for color matching of dyed cotton with reactive dye. Fibers and Polymers, 14, 1196-1202.
2. Aloysius, N., & Geetha, M. (2017, April). A review on deep convolutional neural networks. In 2017 International Conference on Communication and Signal Processing (ICCSP) (pp. 0588-0592). IEEE.
3. Chaouch, S., Moussa, A., Ben Marzoug, I., & Ladhari, N. (2019). Colour recipe prediction using ant colony algorithm: principle of resolution and analysis of performances. Coloration Technology, 135(5), 349-360.
4. Chaouch, S., Moussa, A., Ben Marzoug, I., & Ladhari, N. (2020). Application of genetic algorithm to color recipe formulation using reactive and direct dyestuffs mixtures. Color Research & Application, 45(5), 896-910.
5. Chaouch, S., Moussa, A., Ben Marzoug, I., & Ladhari, N. (2022). Study of CI Reactive Yellow 145, CI Reactive Red 238 and CI Reactive Blue 235 dyestuffs in order to use them in color formulation. Part 3: Application of ant colony and genetic algorithms for color recipe prediction. The Journal of the Textile Institute, 1-12.
6. Chen, M., Tsang, H. S., Tsang, K. T., & Hao, T. (2021). An Hybrid Model CMR-Color of Automatic Color Matching Prediction for Textiles Dyeing and Printing. In Neural Computing for Advanced Applications: Second International Conference, Guangzhou, China, August 27-30, 2021, Proceedings 2 (pp. 603-618). Springer Singapore.
7. Chen, T. B., & Soo, V. W. (1996, June). A comparative study of recurrent neural network architectures on learning temporal sequences. In Proceedings of International Conference on Neural Networks (ICNN'96) (Vol. 4, pp. 1945-1950). IEEE.
8. CIE 1931 XYZ Color Space, https://acikders.ankara.edu.tr

9. Elman, J. L. (1990). Finding structure in time. Cognitive science, 14(2), 179-211.
10. Grishanov, S. (2011). Structure and properties of textile materials. In Handbook of textile and industrial dyeing (pp. 28-63). Woodhead Publishing.
11. Golob, D., Osterman, D. P., & Zupan, J. (2008). Determination of pigment combinations for textile printing using artificial neural networks. Fibres & Textiles in Eastern Europe, 16(3), 68.
12. Haji, A., & Vadood, M. (2021). Environmentally benign dyeing of polyester fabric with madder: modelling by artificial neural network and fuzzy logic optimized by genetic algorithm. Fibers and Polymers, 22, 3351-3357.
13. Kandi, S. G. (2007). Color recipe prediction by genetic algorithm. Dyes and Pigments, 74(3), 677-683.
14. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
15. Ku, C. C., Chien, C. F., & Ma, K. T. (2020). Digital transformation to empower smart production for Industry 3.5 and an empirical study for textile dyeing. Computers & Industrial Engineering, 142, 106297.
16. Kumar, M., Husain, D., Upreti, N., & Gupta, D. (2010). Genetic algorithm: Review and application. Available at SSRN 3529843.
17. Li, F., Chen, C., & Mao, Z. (2022). A novel approach for recipe prediction of fabric dyeing based on feature‐weighted support vector regression and particle swarm optimisation. Coloration Technology, 138(5), 495-508.
18. Ministry of National Education. Tekstil Teknolojisi, Hedef Rengi Bulmak, 2011.
19. Moussa, A. (2021). Textile color formulation using linear programming based on Kubelka‐Munk and Duncan theories. Color Research & Application, 46(5), 1046-1056.
20. Nauck, D., & Kruse, R. (1997). A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy sets and Systems, 89(3), 277-288.
21. Noble, W. S. (2006). What is a support vector machine?. Nature Biotechnology, 24(12), 1565-1567.
22. Onar, N. (2011). Renk Recetesi Tahminlemesinde Yapay Sinir Aginin Kullanimi. Tekstil ve Muhendis, 18(81), 12-21.
23. Pinkus, A. (1999). Approximation theory of the MLP model in neural networks. Acta Numerica, 8, 143-195.
24. Qin, X., & Zhang, X. J. (2021). An industrial dyeing recipe recommendation system for textile fabrics based on data-mining and modular architecture design. IEEE Access, 9, 136105-136110.
25. Sagirlibas, M. V. (2009). Color recipe prediction with neural networks (Doctoral dissertation, DEÜ Fen Bilimleri Enstitusu).
26. Salazar-Vazquez, J., & Mendez-Vazquez, A. (2020). A plug-and-play Hyperspectral Imaging Sensor using low-cost equipment. HardwareX, 7, e00087.
27. Samanta, P. (2022). Basic Principles of Colour Measurement and Colour Matching of Textiles and Apparels. Colorimetry, 105.
28. Samanta, P. (2018). Fundamentals and Applications of Computer-Aided Colour Match Prediction (CCMP) System. Trends in Textile & Fash Design 2 (5)-2018. LTTFD. MS. ID. 000148. DOI: 10.32474/LTTFD. 2018.02. 000148.
29. Sennaroglu, B., Öner, E., & Senvar, Ö. (2014). Colour recipe prediction in dyeing acrylic fabrics with fluorescent dyes using artificial neural network/Stabilirea retetei de vopsire a materialelor acrilice cu coloranti fluorescenti, folosind o retea neurala artificiala/Colour recipe prediction in dyeing acrylic fabrics with fluorescent dyes, using artificial neural network. Industria Textila, 65(1), 22.
30. Senthilkumar, M. (2007). Modelling of CIELAB values in vinyl sulphone dye application using feed-forward neural networks. Dyes and Pigments, 75(2), 356-361.
31. Sikka, M. P., Sarkar, A., & Garg, S. (2022). Artificial intelligence (AI) in textile industry operational modernization. Research Journal of Textile and Apparel.
32. Textile Global Market Report, 2023.
33. Textiles and Clothing Industry in Turkey, Statistics & Facts, 2022.
34. Tu, Z., Yin, Y., & Qin, X. (2022). Towards Better Data Pre-Processing for Building Recipe Recommendation Systems from Industrial Fabric Dyeing Manufacturing Records: Categorization of Coloration Properties for a Dye Combination on Different Fabrics. In Design Studies and Intelligence Engineering (pp. 17-23). IOS Press.
35. Uyanik, S., & Celikel, D. C. (2019). Türk Tekstil Endüstrisi Genel Durumu. Teknik Bilimler Dergisi, 9(1), 32-41.
36. Westland, S. (1998). Artificial neural networks and colour recipe prediction. In Proceedings of the International Conference and Exhibition: Colour Science (pp. 225-233).
37. Yu, C., Cao, W., Liu, Y., Shi, K., & Ning, J. (2021). Evaluation of a novel computer dye recipe prediction method based on the pso-lssvm models and single reactive dye database. Chemometrics and Intelligent Laboratory Systems, 218, 104430.
38. Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural Computation, 31(7), 1235-1270.
39. Zhang, J., Zhang, X., Wu, J., & Xiao, C. (2021). Dyeing recipe prediction of cotton fabric based on hyperspectral colour measurement and an improved recurrent neural network. Coloration Technology, 137(2), 166-180.
40. Zhu, H. (2022). A Neural Network Model to Predict the Color of Dry Cotton Fabric from a Wet State. North Carolina State University.