A machine learning optical system to ensure that human assembly technicians use the specified bolt tightening sequence in assembly line manufacturing

Soni, Varun (M.S. in mechanical engineering)
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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%.