A function block based approach for increasing adaptability of assembly planning and control

Today's market turbulences cause frequent changes in manufacturing environments. Products diversity, small batch sizes and short life cycles have increased production uncertainties and created a highly dynamic shop floor environment. One essential requirement of such an environment is an adaptive planning and control system that is sufficiently agile to respond to the variety of production requirements and enable easy system reconfiguration at run-time. When developing a product, assembly is a key area that impacts the manufacturing system's responsiveness to the changes. In this research, a framework and a new methodology are introduced to increase the adaptability and autonomy of job-shop assembly process planning and control using function blocks (FBs). A function block is a reusable functional module with an explicit event-driven model, and provides for data flow and finite state automata based control. Event-driven and FB-enabled decision-making is unique in adaptive assembly planning and control. It is explained through an example of a two-robot assembly work cell, where the result of the adaptive planning is wrapped in FBs for execution. The proposed approach has been implemented and simulated using Matlab Simulink in the case study. The simulation demonstrates how this approach would increase the adaptability and responsiveness to changes that may occur regularly in dynamic job-shop assembly operations.