An approach to automate the synthesis of sheet metal parts
In this research, an approach is developed to automate the design for sheet metal parts that are not only novel and manufacturable but also satisfies multiple objective functions such as material cost. Unlike commercial software tools such as Pro/SHEETMETAL which aids the user in finalizing and determining the sequence of manufacturing operations for a specified component, our approach starts with spatial constraints in order to create the component geometries and helps the designer design. While there is an enormous set of parts that can feasibly be generated with sheet metal, it is difficult to define this space systematically. To solve this problem, we currently have 108 design rules that have been developed for five basic sheet metal operations: slitting, notching, shearing, punching and bending. The technique revealed here represents candidate solutions as a graph of nodes and arcs where each node is a rectangular patch of sheet metal, and modifications are progressively made to the sheet to maintain the parts manufacturability. They are presented in the form of Standard Tessellation Language files (.stl) that can be transferred into available modeling software for further analysis. The overall purpose of this research is to provide creative designs to the designer granting him/her a new perspective and to check all the solutions for manufacturability in the early stage of design process. The abovementioned automation approach uses a new topological optimization technique to solve graph based engineering design problems by decoupling parameters and topology changes. This technique namely Topological and Parametric Tune and Prune (TP²) is the first topology optimization method that has been developed specifically for domains representable by a graph grammar schema. The method is stochastic and incorporates distinct phases for modifying the topologies and modifying parameters stored within topologies. Thus far, with the problems that been tested, (TP²) had proven better than genetic algorithm in terms of the quality of solutions and time taken to acquire them.