Optimization of Cutting Conditions for Surface Roughness in VMC 5-Axis

Vertical machining center (VMC) five-axis is advanced metal cutting process which used tomachine advanced materials for creating parts for industries like die, automotive, aerospace, machinerydesign, etc. Input parameters selection very important in VMC-five axis to obtain better surface finishon milled part and enhanced machining economics. In the present work, experimental analysis has beenplanned to study the significances of milling parameters on quality response, surface roughness (Ra) ofD3 steel. The experiments have been planned on D3 steel in VMC five axis as per Box-Behnken designof response surface methodology (RSM). Modeling and optimization have been done by hybrid RSMand Jaya optimization algorithm. The factor effects on Ra has been studied by analysis of signal-tonoise ratio. The concluding remarks has been drawn from the study

Economic production quantity model with variable machining rates and product quality

The literature has several models that jointly determine the economic production quantity (EPQ) and the rate of production. Very few models define production rate explicitly. In this article, the cutting speed controls the production rate and the proportion nonconforming. Thus, we propose a model that integrates EPQ, machining economics and quality. Even though the objective function of the model is non-convex, we show for realistic values of some technical parameters that a local minimum of the proposed mathematical model is also global. Examples clearly show the effect of the production economics factors on the optimal machining speed and production quantity.

Correlation of the Volumetric Tool Wear Rate of Carbide Milling Inserts With the Material Removal Rate of Ti–6Al–4V

The objective of this paper is to assess the correlation of volumetric tool wear (VTW) and wear rate of carbide tools on the material removal rate (MRR) of titanium alloys. A previously developed methodology for assessing the worn tool material volume is utilized for quantifying the VTW of carbide tools when machining Ti–6Al–4V. To capture the tool response, controlled milling experiments are conducted at suitable corner points of the recommended feed-speed design space, for constant stock material removal volumes. For each case, the tool material volume worn away, as well as the corresponding volumetric wear profile evolution in terms of a set of geometric coefficients, is quantified—these are then related to the MRR. Further, the volumetric wear rate and the M-ratio (volume of stock removed to VTW) which is a measure of the cutting tool efficiency, are related to the MRR—these provide a tool-life based optimal MRR for profitability. This work not only elevates tool wear from a 1D to 3D concept, but helps in assessing machining economics from a stock material-removal-efficiency perspective as well.

The Correlation of Volumetric Tool Wear and Wear Rate of Machining Tools With the Material Removal Rate of Titanium Alloys

The objective of this paper is to assess the correlation of volumetric tool wear (VTW) and wear rate of carbide tools on the material removal rate (MRR) of titanium alloys. A previously developed methodology for assessing the worn tool material volume is utilized for quantifying the VTW of carbide tools when machining Ti-6Al-4V. To capture the tool substrate response, controlled milling experiments are conducted at suitable corner points of the feed-speed design space for constant stock material removal volumes. For each case, the tool material volumes worn away, as well as the corresponding volumetric wear profile evolution in terms of a set of geometric coefficients are quantified — these are then related to the MRR. Further, the volumetric wear rate and the M-ratio (volume of stock removed to VTW), which is a measure of the cutting tool efficiency, are related to the MRR — these provide a tool-centered optimal MRR in terms of profitability. This work not only elevates tool wear from a 1-D to 3-D concept, but helps in assessing machining economics from a stock material removal efficiency perspective as well.

Cutting Parameter Optimization for Multi-Pass Milling Operations by Genetic Algorithms

Parameter optimization in multi-pass cutting operations involves optimal selection of cutting speed, feed rate, depth of cut, and the number of passes, duo to significant influence of these parameters on the quality of machined parts and machining economics. In this paper, a non-linear mathematical model based on minimum production cost for multi-pass milling operations is presented. The unwanted material is removed by one finishing pass and one or multiple roughing passes depending on the total depth of cut. Various realistic constraints are considered when developing the model. Optimal values of machining parameters are found by Genetic Algorithms. An example is presented to illustrate the optimization model and solution approach. The method yields lower unit production costs compared with the results from the literature and machining data handbook.