Samik Dutta

Evaluation of primary phase morphology of cooling slope cast Al-Si-Mg alloy samples using image texture analysis

Rheopressure die casting is one of the newest casting processes of present era for manufacturing of near-net-shaped cast components with improved mechanical properties and high-dimensional accuracy. Rheopressure die casting demands especially prepared semi-solid alloy slurry having nearly globular primary Al phase. In this study, a cooling slope has been employed to produce semi-solid slurry of Al-Si-Mg (A356) alloy and successively cast in a metallic mould. Image texture analysis techniques have been implemented for accurate evaluation of the primary phase morphology of cast samples. In this research, efforts have been made to apply fractal analysis and run-length statistical analysis techniques for automatic characterization of optical micrographs of cast samples produced at different processing conditions.

Texture Analysis of Turned Surface Images Using Grey Level Co-Occurrence Technique

The main purpose of this work was to study the applicability of an image texture analysis method, namely, the grey level co-occurrence matrix (GLCM) method for the examination of the smoothness of the images of a turned surface. The effect of the variation of the pixel pair spacing (pps) on the construction of the GLCM was also considered and then, contrast and homogeneity were calculated from the GLCMs which served as texture descriptors for the quality of the machined surface. Finally, the variation of these texture descriptors with cutting time was analyzed and compared with the variation of tool wear and surface roughness with cutting time.

Digital Image Processing in Machining

This chapter speaks about the application of digital image processing in conventional machining. Advantages and disadvantages of digital image processing techniques over the other sensors used in machining for product quality improvement is also discussed here. A short introduction to image processing techniques used in machining is presented here. A detailed review of image processing applications in machining for over the past decade is discussed in this chapter. Also, an example of an image texture analysis method utilized for cutting tool condition detection through machined surface images is presented. An overall conclusion leading to future work required in this field has been mentioned.

Progressive tool condition monitoring of end milling from machined surface images

Indirect tool condition monitoring in end milling is inevitable to produce high-quality finished products due to the complexity of end-milling process. Among the various indirect tool condition monitoring techniques, monitoring based on image processing by analyzing the surface images of final product is gaining high importance due to its non-tactile and flexible nature. The advances in computing facilities, texture analysis techniques and learning machines make these techniques feasible for progressive tool flank wear monitoring. In this article, captured end-milled surface images are analyzed using gray level co-occurrence matrix–based and discrete wavelet transform–based texture analyses to extract features which have a good correlation with progressive tool flank wear. Contrast and second diagonal moment are extracted from gray level co-occurrence matrix and root mean square and energy are extracted from discrete wavelet decomposition of end-milled surface images as features. Finally, these four features are utilized to build support vector machine–based regression models for predicting progressive tool flank wear with 94.8% average correlation between predicted and measured tool flank wear values.

Simultaneous position and angular error measurement of precision positioning stages using miniature interferometer with step–size variation

Precision of machine tools is mainly dependent on positional and angular errors of the positioning stage attached to it. Those errors can be measured precisely by laser interferometers. In this work, a measurement system with a miniature interferometer of size 33 × 139 × 94 mm with 0.1 nm linear resolution and 0.002 arcsec angular resolution has been employed to measure positional and angular errors, simultaneously, of two precision positioning stages of different metrological instruments. Measurements have been taken with varying step sizes. A variation of error propagation with the change of step size has been observed.