Publication: Development and validation of the lateral material shift (lms) ratio method for surface finish quality assessment in machining with palm oils as cutting fluids
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Date
2024-09-01
Authors
Mohd Naqib, Derani
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Abstract
Surface texture plays a crucial role in various applications, including optical, electrical, thermal performance, and appearance. In order to produce the desired surface texture in machining, several measures such as cutting zone temperature, tool wear, cutting forces, surface roughness of workpiece, vibration, chip formation, etc have been used in the past to investigate the effectiveness of machining. Among these the two most common parameters investigated in the past are tool flank wear and average surface roughness (Ra). The use of flank wear and average roughness, however, have resulted in confusing and contradicting findings in some of the published literature, such as improvement in surface roughness in spite of increase in flank wear. This is mainly due to the poor correlation between flank wear and surface roughness. Moreover, since Ra is a measure of the average absolute height of the roughness profile and, therefore, is insenstive to lateral changes in the topography of the surface profile of the workpiece as a consequence of tool wear. The use of Ra as the sole roughness measure could potentially lead to errorneous conclusions. No previous attempt has been made to analyze surface finish quality other than looking at two common parameters which are tool flank wear and current roughness parameters. In this research, a new and more effective method of measuring surface finish quality has been developed to assess the effectiveness of palm oils as cutting fluids. Three methods of workpiece surface analysis, namely autocorrelation, cross-correlation, and lateral material shift (LMS) ratio are investigated. Machining experiments were carried out on AISI 316 stainless steel. Images of tool nose and workpiece profiles were captured using digital camera, and the edges were extracted using sub-pixel edge detection. In the autocorrelation approach, each workpiece profile was correlated with a shifted version of the same profile. In the cross-correlation approach, the workpiece profiles at different stages of machining were correlated with a reference profile generated using the unworn tool edge. In the LMS ratio method, the material shift ratios were determined from each waveform on the workpiece profile at various stages of tool wear. Among the three methods, the LMS ratio method produced the best correlation with tool flank wear with the maximum R-squared value of 0.9466, while average roughness Ra showed no correlation at all with both major and nose flank wear. The proposed LMS ratio method provides a novel method of measuring the workpiece surface deterioration thus giving correlation result in assessing the workpiece surface deterioration.