Directional morphological approaches from image processing applied to automatic tool selection in computer numerical control milling machine

Abstract

Automatic tool selection in milling operation has become a very important step in the manufacturing and planning processes for 2.5D piece machining. The main contribution of this article is the development of a new method based on directional morphological approaches, applied to automatic tool selection in computer numerical control milling machines for machining a 2.5D of a geometry piece provided of three-dimensional model of computer-aided design or from an image taken with other devices. First, the image is preprocessed by applying several image processing techniques. Later, mathematical morphology as erosion or dilation to create structural element with the shape of the cutting tool is used. The method displaces a structural element throughout the entire image with the values of the lengths of the piece boundary and the cutting tool to select the correct cutting tool and tool path. Besides, with the same structural element, the zig and zig-zag contour trajectories are obtained in standard computer numerical control code. Results from these experiments show that the method makes it possible to obtain good performance in automatic tool selection when several types of pieces are processed.

Triangulation intersection approach from Poisson’s equation applied to automatic tool selection in computer numerical control mill-lathe

“…27,28 The normal vector at a point (x 0 , y 0 , z 0 ) on a surface S = f(x, y, z) is given by equation (26) S = rf = f x (x 0 , y 0 , z 0 ) f y (x 0 , y 0 , z 0 ) f z (x 0 , y 0 , z 0 ) gradient in the edge of the triangles for 3D, for CNC mill-lathe, and the algorithm is shown in Table 1.…”

“…Determination of gradient of the piece in the triangles is obtained by applying the normal vector at a point of the triangulation obtained from the discretization; the gradient gives the position and direction of the piece in the workspace. 27,28 The normal vector at a point ( x 0 , y 0 , z 0 ) on a surface S = f ( x , y , z ) is given by equation (26)…”

Artificial Vision for Manufacturing System

“…The tool selection, task commonly made by a human operator, is an important aspect in machining processes, since if the tool selected is incorrect, it can produce dimensional errors in workpiece, possible crashes, and consequently, reject the piece. In this way, some researches based their principles on the selection of the tool for machining 2.5D parts as in the case of Ahmad et al [13], who present an optimization algorithm for the problem of tool sequence [8].…”

“…In the same way, Hemant et al [15] developed an algorithm for the tool selection used by Veeramani tool, although using dynamic programming, human intervention is necessary because of its mathematical complexity [4]. The tool selection using image processing based on the shape is a new method in the literature, the core of this research issimple and easy to implement algorithms [8].…”

“…At present, the development of a system capable of recognizing complex features for tool selection in commercial computer-aided manufacturing (CAM) transfers this task to the workers who do it based on their own experience, ability, and knowledge. The literature on automatic tool selection using image processing is minimal; on the other hand, simple and easy to implement algorithms are the core of this method [8].…”