A machine learning optical system to ensure that human assembly technicians use the specified bolt tightening sequence in assembly line manufacturing
In large number of applications, the mechanical fasteners that are used to assemble the parts of a system must be tightened in a specific sequence to achieve the desired distribution of the load across the population of bolts. Failure to follow the sequence results in an undesired load distribution; this phenomenon is known as bolt crosstalk. Assembly personnel often fail to follow this sequence for a variety of reasons, resulting in over- or under-torqueing of bolts in the final assembly, which can lead to undesired system performance. There is currently no system or device that can ensure that a human operator follows a specified bolt tightening sequence while using a hand-held tool and thereby avoid bolt crosstalk. In this research, a system that constrains the operator to follow the specified tightening sequence was developed and tested. It utilizes a small tool-mounted camera to generate images of the bolt pattern and the relative location of the tool, and a machine learning algorithm to alert the operator if the tool is being brought to the wrong position. The developed software can detect all the bolt positions accurately by using a unique feature associated with them. The average of probabilities of detecting a position in different lightening conditions is more than 85%.