How Important AI is in Textile Industry?
The introduction of AI is helping the textile manufacturers to improve efficiency and enhance capabilities of employees extracting huge business insights from historical and real-time data.
FREMONT, CA: Though artificial intelligence (AI) is transforming several industries, its contribution to the field of the textile industry is relatively new. Conventionally, the textile industry has been associated with low-fixed capital investment, variable production volumes, high competitiveness, high demand on product quality, and a wide range of designs.
The introduction of AI is helping the textile manufacturers to improve efficiency and enhance the capabilities of employees extracting tremendous business insights from historical and real-time data. Though the journey of AI in the field of textiles is at its preliminary stage, here are three potential areas that will benefit considerably from the technology…
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The value of textile products is dramatically affected in case of defects. AI techniques such as an artificial neural network (ANN) are highly effective incorporation for defect identification in the fabric. A leading Indian foam manufacturer, Valiance designed “Intelligent Detect Identification” platform which is a similar platform where images of normal and abnormal foams were handed to determine the characteristics of the components of foam that meet the specifications and those that don’t.
The essence of inspection in textiles cannot be undermined, especially when defects can result in price reduction by as much as 60 percent. In-line inspection is a slow process due to the slow roll of the fabric from the weaving machine. Further, fabric pattern may involve multiple aspects such as knitting, weaving, finishing, braiding, and printing. Manual inspection can be tiring, as well as result in human errors. Instead, the manufacturer can install AI-driven camera-based inspection system at their workstations. The platform studies the weaving pattern and identifies the deviation from the normal.
Product appearance is often tagged with its value. The color of a product is often argued upon by the customers as being “too light” or “too dark,” which may affect the ultimate sale of the product. Traditionally, color tolerance was accomplished using “instrumental tolerancing systems” that encountered numerous false positives owing to visual inspections. The issue can be countered with the help of AI enabled platform that considers historical data of visual inspection results while creating the tolerances.
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