A comparative analysis of constraint and connectivity graph techniques for assembly sequence generation in robotic assembly cells

Abstract

The paper describes a comparative study on the assortment of appropriate assembly-generating techniques for robotic assembly cells. Several techniques have been developed and tested for the creation of viable sequences. Most of these techniques employ computational techniques to generate assembly sequences. Nevertheless, these techniques rely on a large number of assumptions and use insignificant data during the development of assembly sequences. In the present study, two techniques, viz., the connectivity graph technique and the constraint technique are selected, since they are very general, expedient, and easy to implement. Further, selected techniques can easily be automated to increase the productivity of robotics assembly cell. Each technique was applied to four products with different complexities to check their suitability in the context of robotic assembly cell. In the connectivity graph technique, the disassembly of components begins with the sink node in the connectivity graph and ends when no sink node is available. The constraint technique chooses two constraints: the G-constraint and the C-constraint. The process of disassembling the components starts with a component that does not have any G-constraint and satisfies the C-constraint. The G-constraint checks for geometric interference, and the C-constraint checks for connectivity relationships among the components. Both techniques are automated using C++ programming to obtain rapid and efficient results. The automation of assembly techniques is expected to lead to a revolutionary transformation in the assembly sector. The connectivity graph technique is found to be suitable for the robotic assembly cells as described in this article.