{'fraction_non_alphanumeric': 0.020129403306973402, 'fraction_numerical': 0.004313443565780014, 'mean_word_length': 5.105263157894737, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Experimental Investigation of Tool Wear in Machining of SiC Based Al-MMC

ABSTRACT The machining of hard particle reinforced metal matrix composite material is always challenging. The machining behaviour of aluminium-based metal matrix composites (Al-MMCs) can be entirely different from conventional materials. This paper presents an experimental study of the effect of cutting parameters like cutting speed on surface quality and tool wear. Even the matrix material can be ductile, but hard particles and their binding with matrix material can bring a difference in the machinability of composite materials, specially MMCs. Three different MMC compositions, i.e. MMC reinforced with 15%, 20% and 25% SiC, were machined at various cutting speeds and feed rates. Tool wear and surface roughness of the machined surface of MMC were examined. Increased content of SiC increases the mechanical properties like hardness and compressive strength but was found detrimental to tool and surface quality. Tool wear was found increasing with both increasing feed rate and cutting speed. Application of image processing for measurement and prediction of tool wear is attempted by capturing the images of tool insert and processing using a MATLAB® based algorithm. Results are in line with many other research and establish the scope of future work for non-contact measurement of tool wear in real-time.