Development of an Improved Algorithm for workpiece Localization of Raw Material

Open Access
Ma, Yu
Graduate Program:
Industrial Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
July 20, 2015
Committee Members:
  • Dr Sanjay Joshi, Thesis Advisor
  • Workpiece localization
  • machining allowance
  • point cloud
One of the problems that manufacturing industries are faced with is the proper localization of workpieces within the raw blanks while maintaining sufficient machining allowaces. This is especially important in case where the raw material is of near net shape. The solution of this problem is defined as workpiece localization. The corresponding decision-making is especially critical when the size and shape of the blank are closely specified to the designed model to ensure sufficient material. In this paper, we present an improved workpiece localization algorithm for machining, which is achieved by two-step point cloud localization. Firstly, the two point clouds are created (one for the part and one for workpiece) and localized roughly by the Principal component analysis algorithm. Secondly, a more precise algorithm, i.e., least square-based algorithm, is used to search for the best translation and rotation of the workpiece within the blank. The algorithm allows an optimal setup of the part to ensure that no shortage of material occurs during machining. Through transformation, the algorithm determines whether or not the designed model is totally enclosed in the actual raw material to be machined. The two-step localization algorithm can reduce the computational time. The input to the new algorithm is simplified for a 2D workpiece localization process by using point clouds. A 2D example of plasma cutting of the blank and subsequent machining is used to test the algorithm. The results show the processing time is faster than other localization methods using the simplified inputs.