Master’s Thesis

Demonstrate a new method of fabric control for the sewing operation based the position of individual threads and actuation derived from current sewing dogs.  Identify which fabric mechanical properties are essential to offline programming of the system.

The importance of automating the garment manufacturing process has been understood since the early 1980s.  However, in spite of millions of dollars spent on research, three decades later, the industry is still far from achieving a fully autonomous process.  Previous work on fabric control in automated sewing focused on the control of only a single sheet of fabric using an industrial manipulator with an overhead vision system.  These methods did not meet the accuracy and robustness requirements of the sewing process with respect to fabric position and fabric tension.

To address these issues, a new method for fabric control in automated sewing is described.  It uses the current feed mechanism on sewing machines, feed dogs, but modifies them to be servo-controlled.  These servo controlled actuators, servo dogs, individually control two sheets of fabric before the fabric reaches the needle and during the sewing process.  The servo dogs actuate the fabric 180o out of phase with the sewing needle, providing incremental control of the fabric when the needle is out of the fabric.

To achieve this type of control successfully for automated sewing, the servo dogs have been designed for short displacement, high acceleration motions using a cable drive system powered by voice coil motors.  Feedback of fabric position has been determined to be necessary and is to be provided by a thread-tracking vision system.


Book, W.J., R. Winck, J. Huggins, S. Dickerson, S. Jayaraman, T. Collins, R. Prado, “Automated Garment Manufacturing System using Novel Sensing and Actuation,” 2010 International Symposium on Flexible Automation, July 2010.

Winck, R., S. Dickerson, W. Book, J. Huggins, “A Novel Approach to Fabric Control for Automated Sewing,” IEEE/ASME International Conference on Advanced Intelligent Mechatronics, July 2009. *Best Student Paper Award