Abstract
Tool wear is a key factor that dominates the surface quality and distinctly influences the generated workpiece surface texture. In order to realize accurate evaluation of the tool wear from the generated workpiece surface after machining process, a new tool wear monitoring method is developed by fractal dimension of the acquired workpiece surface digital image. A self-made simple apparatus is employed to capture the local digital images around the region of interest. In addition, a skew correction method based on local fast Fourier transformation energy is also proposed for the surface texture direction adjustment. Furthermore, the tool wear quantitative evaluation was derived based on fractal dimension utilizing its high reliability for inherent irregularity description. The proposed tool wear monitoring method has verified its feasibility as well as its effectiveness in actual milling experiments using the material of AISI 1045 in a vertical machining center. Testing results demonstrate that the proposed method was capable of tool wear condition evaluation.
Highlights
Machining tool is a major component in a manufacturing system, and its failure attributes up to 20% of the machine downtime [1], and the costs of tools and tool changes account for 3–12% of the total processing cost [2]. erefore, machining tool reliability becomes a crucial important aspect in ubiquitous manufacturing which directly influences the energy consumption and production rate [3]
Rizal et al proposed a novel approach for tool wear classification and detection in milling process using multisensor signals and Mahalanobis-Taguchi system (MTS) [19]
In [28], authors found that the surface roughness firstly increased and declined when flank wear varied from 0 to 0.3 mm. erefore, a tool wear evaluation method based on a more reliable surface topography statistical indicator is indispensable. Inspirited by these different topography parameters tool wear condition monitoring methods, a new tool wear evaluation method is developed by fractal dimension from the acquired workpiece surface digital image
Summary
Machining tool is a major component in a manufacturing system, and its failure (tool wear and breakage) attributes up to 20% of the machine downtime [1], and the costs of tools and tool changes account for 3–12% of the total processing cost [2]. erefore, machining tool reliability becomes a crucial important aspect in ubiquitous manufacturing which directly influences the energy consumption and production rate [3]. Rizal et al proposed a novel approach for tool wear classification and detection in milling process using multisensor signals and Mahalanobis-Taguchi system (MTS) [19] These indirect methods present a significant drawback: all these signals could be seriously affected by the inherent noise generated in industrial environments [20,21,22,23], which reduces their performance. Compared with conventional direct and indirect tool wear evaluation method, machined surface topographies evaluation can be performed without the need to stop the cutting process and enjoys the merit of free noise interference. Erefore, a tool wear evaluation method based on a more reliable surface topography statistical indicator is indispensable Inspirited by these different topography parameters tool wear condition monitoring methods, a new tool wear evaluation method is developed by fractal dimension from the acquired workpiece surface digital image.
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