Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades

2010-2011 > Academic research: refereed > Chapter in an edited book (author

An Extended Review on Fabric Defects and Its Detection Techniques

In Textile Industry, Quality of the Fabric is the main important factor. At the initial stage, it is very essential to identify and avoid the fabrics faults/defects and hence human perception consumes lot of time and cost to reveal the fabrics faults. Now-a-days Automated Inspection Systems are very useful to decrease the fault prediction time and gives best visualizing clarity- based on computer vision and image processing techniques. This paper made an extended review about the quality parameters in the fiber-to-fabric process, fabrics defects detection terminologies applied on major three clusters of fabric defects knitting, woven and sewing fabric defects. And this paper also explains about the statistical performance measures which are used to analyze the defect detection process. Also, comparison among the methods proposed in the field of fabric defect detection

Leveraging Unstructured Image Data for Product Quality Improvement

Recently, traditional quality assurance methods, which often require human expertise, have been accompanied by more automated methods that use machine learning technology. These methods offer manufacturers to reduce error rates and, consequently, to increase margins as well. In particular, predictive quality assurance (Pre QA) allows to minimize expenses by feeding back information from product returns and quality checks into the early product development. However, Pre QA requires detailed information about previous quality problems which is not always readily available in a structured form. In this paper, we therefore discuss the potential of leveraging initially unstructured information in the form of images, taken either during quality checks or by customers when returning a product, to the end of product quality improvement. We furthermore show how this might be realized in practice using the case of fashion manufacturing as an example

Texture classification of fabric defects using machine learning

In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others

Artificial Neural Networks and Their Applications in the Engineering of Fabrics

WOS: 00036372550000

STATISTICAL PROCESS CONTROL MODEL IN THE DESIGN AND THE DEVELOPMENT OF FABRICS

In this paper the aim was to create a basic model for total quality management in the organization of the textile production, with an emphasis on Statistical Process Control (SPC) in the design and development process that will completely satisfy the requirements, the needs and the desires of the buyers and all the participants in the business relations. The paper deals with a methodology of the process of fabric’s design and development, consisted of two macro stages: 1) development and 2) fabric’s production. The monitoring, the analysis and the verification of the process were done using statistical methods, techniques and tools, with which in the most explicit manner data processing, analysis and presentation are done. This is aimed at continuous process improvement. According to this, a few methods were presented: FMEA method, Pareto and Cause and Effect analysis and X - R control chart

STATISTICAL PROCESS CONTROL MODEL IN THE DESIGN AND THE DEVELOPMENT OF FABRICS

In this paper the aim was to create a basic model for total quality management in the organization of the textile production, with an emphasis on Statistical Process Control (SPC) in the design and development process that will completely satisfy the requirements, the needs and the desires of the buyers and all the participants in the business relations. The paper deals with a methodology of the process of fabric’s design and development, consisted of two macro stages: 1) development and 2) fabric’s production. The monitoring, the analysis and the verification of the process were done using statistical methods, techniques and tools, with which in the most explicit manner data processing, analysis and presentation are done. This is aimed at continuous process improvement. According to this, a few methods were presented: FMEA method, Pareto and Cause and Effect analysis and X - R control chart