Showing papers by "Mozammel Mia published in 2016"

Effect of Pulse Jet MQL in Surface Milling of Hardened Steel

Abstract: Minimum quantity lubricant (MQL) machining is an emerging cooling technology that ensures green machining. In this research work, an innovative design for the application of MQL in the form of pulse jet was created, following the development of that applicator. The surface milling of AISI 4140 steel, heat treated to 40 HRC, was investigated with pulse jet minimum quantity lubricant applicator using VG-68 grade straight cut cutting oil as the cutting fluid in respect of cutting force, surface roughness and tool flank wear with machining time. Four flute carbide end mill cutter was used to investigate the change in tool wear, and compared with that of the dry condition. The result and analysis indicate that this pulse jet MQL applicator can be utilized in hard milling operation to ensure better surface finish and minimal tool wear and cutting force.

Modeling of Principal Flank Wear: An Empirical Approach Combining the Effect of Tool, Environment and Workpiece Hardness

Abstract: Hard turning is increasingly employed in machining, lately, to replace time-consuming conventional turning followed by grinding process. An excessive amount of tool wear in hard turning is one of the main hurdles to be overcome. Many researchers have developed tool wear model, but most of them developed it for a particular work-tool-environment combination. No aggregate model is developed that can be used to predict the amount of principal flank wear for specific machining time. An empirical model of principal flank wear (VB) has been developed for the different hardness of workpiece (HRC40, HRC48 and HRC56) while turning by coated carbide insert with different configurations (SNMM and SNMG) under both dry and high pressure coolant conditions. Unlike other developed model, this model includes the use of dummy variables along with the base empirical equation to entail the effect of any changes in the input conditions on the response. The base empirical equation for principal flank wear is formulated adopting the Exponential Associate Function using the experimental results. The coefficient of dummy variable reflects the shifting of the response from one set of machining condition to another set of machining condition which is determined by simple linear regression. The independent cutting parameters (speed, rate, depth of cut) are kept constant while formulating and analyzing this model. The developed model is validated with different sets of machining responses in turning hardened medium carbon steel by coated carbide inserts. For any particular set, the model can be used to predict the amount of principal flank wear for specific machining time. Since the predicted results exhibit good resemblance with experimental data and the average percentage error is <10 %, this model can be used to predict the principal flank wear for stated conditions.

Effects of cutting parameters and machining environments on surface roughness in hard turning using design of experiment

Abstract: Hard turning is gradually replacing the time consuming conventional turning process, which is typically followed by grinding, by producing surface quality compatible to grinding. The hard turned surface roughness depends on the cutting parameters, machining environments and tool insert configurations. In this article the variation of the surface roughness of the produced surfaces with the changes in tool insert configuration, use of coolant and different cutting parameters (cutting speed, feed rate) has been investigated. This investigation was performed in machining AISI 1060 steel, hardened to 56 HRC by heat treatment, using coated carbide inserts under two different machining environments. The depth of cut, fluid pressure and material hardness were kept constant. The Design of Experiment (DOE) was performed to determine the number and combination sets of different cutting parameters. A full factorial analysis has been performed to examine the effect of main factors as well as interaction effect of factors on surface roughness. A statistical analysis of variance (ANOVA) was employed to determine the combined effect of cutting parameters, environment and tool configuration. The result of this analysis reveals that environment has the most significant impact on surface roughness followed by feed rate and tool configuration respectively.