Comparative evaluation on the performance of nanostructured TiAlN, AlCrN, TiAlN/AlCrN coated and uncoated carbide cutting tool on turning En24 alloy steel

45-59<span style="font-size:11.0pt;mso-bidi-font-size: 10.0pt;font-family:" times="" new="" roman","serif";mso-fareast-font-family:"times="" roman";="" mso-ansi-language:en-us;mso-fareast-language:en-us;mso-bidi-language:ar-sa"="" lang="EN-US">In the present work, the performances of the nanostructured TiAlN, AlCrN, TiAlN/AlCrN coated are evaluated by comparing the machining performance with uncoated carbide cutting tool by conducting the machining studies on En24 alloy steel. Taguchi’s experimental design is used to design the turning experiments and fix the turning parameters, such as the cutting speed (<i style="mso-bidi-font-style: normal">V), feed rate (f) and depth of cut (d). The signal-to-noise ratio and anova were used to investigate the effects of the machining parameters and their contribution to the tool wear and surface roughness. The results show that the nanostructured TiAlN/AlCrN coated insert has developed minimum flank wear and shown minimum surface roughness on the machined surface, compared to the TiAlN, AlCrN coated and uncoated tools. The cutting parameters in which the TiAlN, TiAlN/AlCrN coated and uncoated inserts have shown lesser tool flank wear and better surface finish of the work-piece are identified. For the TiAlN tool, the better machining parameters are, cutting speed = 160 m/min, feed rate = 0.119 mm/rev, and the depth of cut = 1.0 mm. For TiAlN/AlCrN, the better machining parameters are, cutting speed = 160 m/min, feed rate = 0.318 mm/rev, and the depth of cut = 0.3 mm, and for the uncoated tool, the cutting speed = 100 m/min, feed rate = 0.318 mm/rev, and the depth of cut = 1.0 mm is the best machining condition. But for the AlCrN tool the minimum tool wear was obtained, when the cutting speed = 40 m/min, feed rate = 0.477 mm/rev, and the depth of cut = 1.0mm and better surface finish of the work-piece was obtained, when the cutting speed = 160 m/min, feed rate = 0.119 mm/rev, and the depth of cut = 1.0 mm.</span

A REVIEW OF SOFT COMPUTING TECHNIQUES IN MATERIALS ENGINEERING IJARET © I A E M E

ABSTRACT Within the last three decades, a solid and real amount of research efforts has been directed at the application of soft computing (SC) techniques in engineering. This paper provides a systematic review of the literature originating from these efforts which focus on materials engineering (ME) particularly sheet metals. The primary aim is to provide background information, motivation for application and an exposition to the methodologies employed in the development of soft computing technologies in engineering. Our review shows that all the works on the application of SC to sheet metal have reported excellent, good, positive or at least encouraging results. Our appraisal of the literature suggest that the interface between material engineering and intellectual systems engineering techniques, such as soft computing, is still blur. The need to formalize the computational and intelligent system engineering methodology used in sheet material, therefore, arises. We also provide a brief exposition to our on-going efforts in this direction. Although our study focuses on materials engineering in particular, we think that our findings applies to other areas of engineering as well