An innovative approach for automatic generation, verification and optimization of part programs in turning

Continuous innovation of products and optimization of manufacturing processes are of fundamental importance for preserving competitiveness. In the last decades, several approaches based on analytic models for optimization of basic machining operations such as cylindrical turning and face milling have been developed. However, the analytic approaches may not be adequate for real industrial applications, since they are based on average cutting parameters and thus they are not capable of taking into account the effect of complex geometries and instantaneous cutting conditions. In this paper, an innovative integrated system for automatic generation of optimized part programs in turning based on realistic machining simulation is proposed. The system components are described in detail and the machining simulator is validated by comparison with the results of real cutting tests. Then, the optimization approach is applied to a simple case study. The results show that the behavior of the cost function is rather complex, even for simple workpieces. Moreover, the simulator can detect unfeasible combinations of cutting parameters and thus reduce inline part program refinement and optimization. The optimal combination of cutting parameters determined by the new system was competitive with the solutions derived from tool specifications or proposed by a machining expert

Compensation of geometrical errors of CAM/CNC machined parts by means of 3D workpiece model adaptation

In modern industry conditions, it is very important to develop methodologies for reducing costs and achieving the maximum quality of machined parts, especially considering the dimensional accuracy of workpieces. Geometrical and dimensional inaccuracies are due to several factors, such as workpiece and tool deformation during machining, thermal distortions, tool wear, and machine tool inaccuracy. There are two main approaches used to improve the accuracy of workpieces: mapping the tool-workpiece displacement and altering the finishing tool path or the interpolated tool position to compensate the dimensional errors. The aim of this paper is to propose a new compensation approach, based on adaptation of the geometrical 3D CAD model used to generate trajectories by CAM software. The concept is to produce a first workpiece using a CAM-generated tool path. Then, the workpiece is measured using optical methods and the displacements between the ideal workpiece model and the measured point-cloud are calculated. Eventually, the displacement vectors are applied to calculate a compensated workpiece model. Such model is then used as a reference by CAM software to calculate the compensated tool path, which is applied for production of subsequent workpieces. The mathematical background and implementation details are given together with an example of application to a benchmark workpiece purposely machined with inaccurate tools. As the results show, the new approach was able to compensate the geometrical inaccuracies of the benchmark workpiece

Automatic path-planning algorithm for realistic decorative robotic painting

In this paper an innovative algorithm to reproduce non-uniform, photorealistic, gray-scale images on large surfaces, using an ordinary industrial spray-painting robot is proposed. The algorithm splits the process into a set of iterative steps with decreasing spray-gun stroke diameters. Thus, it can efficiently build up the image starting with large strokes to paint the larger details of the image. Then, with increasingly smaller strokes, it can paint the rest of the smaller details. The target image is segmented and a tool-path is computed. A set of critical points in the image is then chosen to avoid oversaturation and used to implement an algorithm to calculate spray-gun operational speed at each path point. Eventually, such conditions lead to a linear system which is solved using an ordinary least squares method. Depending on the image to reproduce, this methodology promises to be far more efficient than painting processes where the image is built entirely at the smallest detail level. For this reason, it would be particularly suitable for large building facade decoration, for example. (C) 2015 Elsevier B.V. All rights reserved

An investigation on swarm intelligence methods for the optimization of complex part programs in CNC turning

Automation of engineering procedures for the development of new manufacturing processes is of great importance in modern competitive conditions. For example, metalworking companies would greatly benefit from the development of methods for automatic generation, testing and optimization of part programs for machining operations. Indeed, the generation of part programs-even by using CAM software-does still require strong human intervention and it is basically a best guess approach with minimum optimization. Moreover, further refinement and correction of the part program on the machine tool is often necessary. Machining operations are generally based on a large number of parameters and therefore optimization strategies should be able to deal with high-dimensional spaces and disjoint domains. In this paper, two swarm intelligence optimization algorithms-particle swarm optimization (PSO) and artificial bee colony (ABC)-have been applied for optimizating the part program of a complex turning part. The optimizers were implemented in a framework for automatic part program generation, realistic simulation, and feasibility analysis. The results evidenced that both approaches were capable of optimizing efficiently the part program, and that the optimization time of the PSO approach on modern computers may be suitable for application in production