Applications of Artificial Neural Network in Textile Engineering

An Artificial Neural Network (ANN) is an information processing model that is inspired by the way biological nervous systems, such as the brain, process information. ANN is configured through a learning process for a specific application, such as pattern recognition or data classification. ANN is one of the hopes available to textile and apparel industry to integrate the elements such as production, cost, quality, information, statistical process control, just-in-time (JIT) manufacturing computer integrated manufacturing etc. Application of ANN in textile engineering is becoming popular since 1990. Gradually it was proven that they can address successfully complex engineering problems.

Artificial Neural Network (ANN) in Textile

The implementation of the ANNs presumes an initial phase of features extraction, which will be used later to feed the ANN. It includes the processing of the given data or measurements, typically in the form of a signal, an image etc. It has allowed covering up wide application areas in all five categories to ANN in textile field. A very brief overview of such uses of the ANNs in the basic sectors of textile engineering, viz; fiber, yarn, fabric and garment are given here.

Applications of Artificial Neural Network (ANN) in Textile Engineering:

Application ANN in all branches of textile engineering are described below.

Application of artificial neural network in fiber sector is given below:

  1. An ANN is used for the prediction of copolymer composition very correctly, as a function of reaction conditions and conversions.
  2. Classification of the animal fibers is one of the most typical problems. It has been resolved successfully using ANNs.
  3. ANN is used in cotton grading more perfectly.
  4. ANNs have supported to identify the production control parameters and the prediction of the properties of the melt spun fibers in the case of synthetic fibers.
  5. ANNs have been used in conjunction with NIR` spectroscopy for the identification of the textile fibers.

ANN Application in yarn sector is summarized below:

  1. ANN can help in imparting the better control on yarn quality during carding process.
  2. ANN is used in auto leveling in the draw frame for imparting desired linear density control.
  3. ANN used in optimization of the top roller diameter as well as the study of the spinning balloon in the main spinning phase is important for controlling yarn quality.
  4. Application of ANN, warp breakage rate reduce during the weaving.
  5. ANNs have been used for the prediction of hairiness of worsted wool yarns.
  6. The spinning of the staple fibers for the production of the yarns is a multistage procedure including many parameters, which influence the characteristics of the end product, viz; the spun yarn. ANN is the excellent method for predictors. The cost minimization of cotton fiber is also ensured by using classical linear programming approach in combination with ANN.
  7. ANNs have been used for the prediction of hairiness of worsted wool yarns and of cotton yarns. In a same way, ANNs have been used for the prediction of the evenness of ring spun worsted yarns and cotton yarns or the evenness of blended rotor yarns.
  8. ANN also used in splicing of two yarn ends more perfectly.
  9. The image processing technology is interfaced with neural networks to extract the defects in yarn packages and thereby used to classify the quality grades of the yarn packages.
  10. ANN is useful in defining relationship between process variables and molecular structure for synthetic yarns.
  11. ANNs have also been used for the appearance analysis of false twist textured yarn packages, for the prediction of yarn shrinkage or for the modelling of the relaxation behaviour of yarns.

Application of ANN in fabric sector is summarized below:

  1. ANN is used to inspect fabric for the detection of faults.
  2. Fabrics can be engineered either by weaving, knitting or bonding. Neural networks are successfully implemented in all three to optimise the input parameters.
  3. The detection and recognition of the patterns on a fabric is of the same complex category of problems and thus resolved by the implementation of ANNs.
  4. ANN is used to the prediction of the fabric drape.
  5. By the application of ANNs fabric handle become increase.
  6. ANNs in combination with fuzzy logic have been used in the case of the prediction of the sensory properties of the fabrics.
  7. ANNs have been used for the prediction of the tensile strength and for the initial stress-strain curve of the fabrics.
  8. Shear stiffness and compression properties of the worsted fabrics has been successfully modelled.
  9. Prediction of bursting strength using ANNs for woven and knitted fabrics has been achieved with satisfactory results.
  10. Permeability of the woven fabrics has been modelled using ANNs as well as, the impact permeability has been studied and the quality of the neural models has been assessed.
  11. Pilling propensity of the fabrics has been predicted and the pilling of the fabrics has been evaluated.
  12. The presence of fuzz fibers has been modelled by ANN.
  13. ANN is used in evaluating wrinkled fabrics with image analysis.
  14. Prediction of the spirality of the relaxed knitted fabrics as well as knit global quality and subjective assessment of the knit fabrics have been implemented using ANNs.
  15. Prediction of the thermal resistance and the thermal conductivity of the textile fabrics have been realized with the help of ANNs.
  16. Moisture and heat transfer in knitted fabrics has been studied with ANN modelling successfully.
  17. Engineering of fabrics used in safety and protection applications is supported by ANNs.
  18. Prediction of the fabrics end use is also possible via ANN method.
  19. Optimization of the application of a repellent coating has also been approached by the ANN model.
  20. Colour measurement, evaluation, comparison and prediction are major actions in the dyeing and finishing field of the textile process. It is done by ANNs.

Application of ANNs in apparel:

A large variety of ANN approaches for decision-making problems in the apparel industry have been proposed in the last two decades, three major leading areas are summarized below:

  1. Resolving prediction problem: RGI involves various predictions during production process, like; fabric manufacturing performance, sewing thread consumption, fashion sensory comfort, cutting time, apparel sales, etc.
  2. Resolving classification problems: It involves classification at various levels, just for an example; fabric online classification, fabric defect classification, fabric end-use classification and seam feature classification etc.
  3. Model identification problems.
  4. The thread consumption is predicted via an ANN model.
  5. The seam puckering is evaluated and the sewing thread is optimized through ANN models, respectively.
  6. The prediction of the sewing performance is also possible using ANNs.
  7. Prediction of the performance of the fabrics in garment manufacturing and fit garment design has been realized based on ANN systems.

ANN application in nonwoven are given below:

  1. The non-woven fabrics undergo a process of inspection in order to ensure quality of the delivered material. A visual inspection system has been based on wavelet texture analysis and robust Bayesian ANNs.
  2. A neuro-fractal approach has been used for the recognition and classification of non-woven web images.
  3. Many quality issues are inspected via ANN methods, like structure properties relations of the non-woven fabrics, construction of a quality prediction system, modelling of the compression properties of needle-punched nonwoven fabrics, the simulation of the drawing of spun bonding nonwoven process and also the objective evaluation of the pilling on nonwoven fabrics.

Application of Artificial Neural Network (ANN) creates new opportunities in the field of textile engineering. The artificial neural network is increasingly used as a powerful tool in different sectors of textile engineering for solving many problems. The results obtained by these intelligent devices are much more precise and reliable than the normal method of measurement or inspection. Textile industries in developed counties have started exploiting these techniques to their advantage. Most of the textile processes & the related quality assessments are non-linear in nature & hence, artificial neural networks find application in textile technology.


  1. Engineering Cotton Yarns with Artificial Neural Networking (ANN) by Sweety A. Agrawal and Tasnim N. Shaikh
  2. Information Systems for the Fashion and Apparel Industry by Tsan-Ming Jason Choi
  3. International Journal of Current Microbiology and Applied Sciences Volume 7 Number 04 (2018); Applications of Artificial Neural Network in Textiles by Neha Chauhan, Nirmal Yadav and Nisha Arya

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