1. Fabric defect identification
Defects in fabric reduce the value of the textile products. Any defect in the fabric is passed into the final product, which can result in the rejection. That’s why fabric it is very essential to check the quality of the fabric before the manufacturing. Fabric inspection is manually checked by skilled workers using lighted tables with equipment. This process is slow and many times can allow faults to pass to the product.
In this case, the application of AI can perform this task at a faster rate, with much higher accuracy and without fatigue. Artificial intelligence can be used to predict the fabric properties before manufacturing with the help of the neuro-fuzzy or other system by using the yarn, and fabric’s constructional data.
2. Pattern inspection
Fabric pattern may have multiple aspects such like: weaving, knitting, braiding, finishing, and printing, etc. By replacing visual inspection with vision-based inspection could help manufacturers avoid human fatigue and errors in the detection of novelties and defects. AI techniques like ANN are applied for defect identification in fabric inspection of the textile industry. The fabric picture to be analysed is obtained from the image acquisition system and saved in relevant standard image format (.JPEG, .JPG, .PNG etc.). Different Multi-Layer back propagation algorithm is used to train and test this ANN system. The system learns the weaving pattern, yarn properties, colours and tolerable imperfections from these images.
3. Colour matching
Colour is an important aspect of textile products. The appearance of a textile product is perceived to be related to its quality. The colour of a product is judged to be acceptable/unsatisfactory, or it can be judged in more details to be: ‘too light ‘or, ‘too dark’, ‘too red’ or, ‘too green’. To solve this problem, AI can be developed that has ‘Pass/Fail’ feature to help improve the accuracy and efficiency.
4. Sewn seam
In sewn, seams and stitches are used to join two or more pieces of fabric together. The ease of seam formation and the performance of the seam are the important parameters are known as “sew-ability.” Fabric low-stress mechanical properties such as tensile, shear, bending, etc. may affect the sew-ability. Artificial intelligence system can be used to find the sew-ability of different fabrics during the production.
5. CAD systems:
One of the important steps in textile production is pattern making. In this process, basic patterns are made by the designers and subsequently digitized to computer. Various CAD software are used in the textile industry for pattern-making, digitizing, grading, and marker planning. The CAD software helps in achieving high productivity and improved quality of the product.
6. Production planning and control
Production planning and control (PPC) coordinates between various departments of production so that delivery dates can met and buyer orders are delivered on time. AI can be used to solve of the machine layout, operation assignment, sewing line balancing, etc. AI can help in achieving the main purpose of PPC.
7. Final inspection
The inspection of finished and semi-finished textile product during their production is essential to get fewer rejections. The final quality inspection of finished garments is mainly done by experienced people, which is very time-consuming and may be influenced by the physical and mental condition of the inspector. As a result, automated AI inspection is essential to achieve the efficiency and accurate results. Automated inspection can be performed by the use of AI and image processing for inspection of the quality of the product.
8. Supply Chain Management
Supply Chain Management in fashion includes the flow of fibres, yarns, fabrics, garments, trims, and accessories in between different production points or to retail. SCM integrates various business processes, activities, information, and resources for creating value for the buyers. Standard Supply Chain Management can manage the cost and business competitiveness