To reduce the difficulty of the sewing process and promote the automation process of fabric sewing, a soft finger-assisted feeding method is proposed to investigate the effect of sewing process parameters on the quality of automatic sewing.
Taking cotton woven fabrics as an example, the causes of sewing deviation are firstly investigated from three aspects: fabric properties, sewing speed and sewing edge position. By simulating the sewing action of human hands, the method of reducing sewing deviation by using soft fingers to press and feed the fabric is proposed. Then, four sewing process factors, namely, robot arm end pressure, sewing machine speed, sewing needle gauge and stitch density, were selected, and three levels were set for each factor to design orthogonal sewing experiments. The sewing deviation of 1# sample under different sewing processes was measured, and the optimal parameter matching for automatic sewing of this specimen was derived.
The findings demonstrate that, while sewing cloth automatically, the sewing deviation is significantly influenced by the robotic arm's end pressure, sewing speed, and stitch density, whereas the sewing deviation is not significantly impacted by the needle number.
The findings offer fundamental information for the development of an automated sewing procedure using soft fingers, which has theoretical and real-world application value to speed up the intelligent modernization and transformation of the apparel industry.
