Development of empirical relationship for surface roughness during the machining of metal matrix composite

Khare, Manu; Gupta, Ravi Kant; Bhardwaj, Bhuvnesh

doi:10.1088/1757-899x/402/1/012010

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Therefore, many scholars have tried to construct models of machining parameters and surface roughness of machined parts to accomplish the prediction of surface roughness. [17][18][19] However, surface roughness was influenced not only by controllable factors (machining parameters), but also by uncontrollable factors (e.g., machine vibration, tool wear). 20 Hence, the prediction of such roughness was hard.…”

Section: Introductionmentioning

confidence: 99%

Roughness detection method based on image multi-features

Pan

Liu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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Roughness was one of the most visual manifestations of the surface quality of metal parts. It affected the performance and life of the parts. Accurate and efficient roughness grade detection technology was of great significance to smart manufacturing. Traditional machine shops often used roughness comparison sample blocks and stylus profilers to check roughness. However, there were disadvantages such as slow detection speed and high influence by human factors. As a non-destructive testing technique, optical imaging gad already demonstrated to be an effective roughness inspection method. In this paper, a roughness detection approach based on image multi-features was proposed, using part surface images as the research object. First, gray level co-occurrence matrix (GLCM), Gabor transform, and local binary patterns (LBP) were used for the extraction of image texture features. After using principal components analysis to reduce the dimensionality of texture features, multiple texture features were concatenated to form a multi-feature vector. Finally, the multi-feature vectors were input into the Gaussian radial basis kernel support vector machine to classify the part surface images and thus completed the detection of roughness grade.

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Section: Introductionmentioning

confidence: 99%

Roughness detection method based on image multi-features

Pan

Liu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…Additionally, the irregular surface texture is observed due to particles in the matrix, and particle fracture affects the surface roughness [12][13][14]. Khare et al [15] investigated the influence of cutting parameters and wt.% Al 2 O 3 /Gr on surface roughness during the end milling of Al/Al 2 O 3 /Gr composite. This study shows a significant feed rate, cutting speed, and wt.% Al 2 O 3 /Gr on surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Surface Roughness in Milling of DuralcanTM Composite

et al. 2021

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In today’s developing aircraft and automotive industry, extremely durable and wear-resistant materials, especially in high temperatures, are applied. Due to this practical approach, conventional materials have been superseded by composite materials. In recent years, the application of metal matrix composites has become evident in industry 4.0. A study has been performed to analyze the surface roughness of aluminum matrix composites named Duralcan® during end milling. Two roughness surface parameters have been selected: arithmetical mean roughness value Ra and mean roughness depth Rz regarding the variable cutting speed. Due to the classification of aluminum matrix composites as hard-to-cut materials concerning excessive tool wear, this paper describes the possibility of surface roughness prediction using machine learning algorithms. In order to find the best algorithm, Classification and Regression Tree (CART) and pattern recognition models based on artificial neural networks (ANN) have been compared. By following the obtained models, the experiment shows the effectiveness of roughness prediction based on verification models. Based on experimental research, the authors obtained the coefficient R2 for the CART model 0.91 and the mean square error for the model ANN 0.11.

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