Automated Unsupervised 3D Tool-Path Generation Using Stacked 2D Image Processing Technique

Tadele Belay Tuli,Andrea Cesarini

doi:10.3390/jmmp3040084

Abstract

Tool-path, feed-rate, and depth-of-cut of a tool determine the machining time, tool wear, power consumption, and realization costs. Before the commissioning and production, a preliminary phase of failure-mode identification and effect analysis allows for selecting the optimal machining parameters for cutting, which, in turn, reduces machinery faults, production errors and, ultimately, decreases costs. For this, scalable high-precision path generation algorithms requiring a low amount of computation might be advisable. The present work provides such a simplified scalable computationally low-intensive technique for tool-path generation. From a three dimensional (3D) digital model, the presented algorithm extracts multiple two dimensional (2D) layers. Depending on the required resolution, each layer is converted to a spatial image, and an algebraic analytic closed-form solution provides a geometrical tool path in Cartesian coordinates. The produced tool paths are stacked after processing all object layers. Finally, the generated tool path is translated into a machine code using a G-code generator algorithm. The introduced technique was implemented and simulated using MATLAB® pseudocode with a G-code interpreter and a simulator. The results showed that the proposed technique produced an automated unsupervised reliable tool-path-generator algorithm and reduced tool wear and costs, by allowing the selection of the tool depth-of-cut as an input.

Highlights

Computer numerical controlled (CNC) machinery opened a window, decades ago, for automating the machining system
Much work is being done in order to allow the machinery tools to perform numerous complex and constrained tasks [3,4,5,6] in the optimization of cutting-tool parameters, such as feed rate, cutting speed, depth-of-cut, and feeding distance [7,8], which are provided as input
In each 2D layer, a start point was selected, a tool path was generated in the Cartesian coordinates between the image contiguous pixels and it was translated into a machine language like G-code

Summary

Introduction

Computer numerical controlled (CNC) machinery opened a window, decades ago, for automating the machining system. Optimal, and accurate machining processes demand for automated, robust but low-intensity computation tool-path planning, to lower machinery vibration, tool wear or breakage, and thermal deformation of the machine tools [2]. In this context, much work is being done in order to allow the machinery tools to perform numerous complex and constrained tasks [3,4,5,6] in the optimization of cutting-tool parameters, such as feed rate, cutting speed, depth-of-cut, and feeding distance [7,8], which are provided as input. In each 2D layer, a start point was selected, a tool path was generated in the Cartesian coordinates between the image contiguous pixels and it was translated into a machine language like G-code

State of the Art

Materials and Methods

G-Code Generation

Result and Discussion