30-04-2013, 12:23 PM
Novel Heuristic Approach to Machining Parameters Optimization for Alumina Based Ceramic and Uncoated Carbide Cutting Tool
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Abstract
This paper proposes the novel heuristic approach for selection of optimal parameter. Many important problems in manufacturing and science require optimization of computationally expensive functions for selection of optimal parameter. Contribution of these optimal cutting parameter decides the production rate, unit production cost and the quality of product. To reach higher precision of the predicted results some novel heuristic approaches particularly genetic algorithm has been developed and presented to ensure simple, fast and efficient optimization of all important machining process. In this paper, optimization of Machining parameters on machining hardened die steel (AISI D2) using Ti [C, N] mixed alumina based ceramic cutting tool and carbide cutting tool is presented. Alumina based ceramic cutting tools can be operated at higher cutting speeds than carbide and cermet tools. This results in increased metal removal rates and productivity. Production cost is the main concern of the industry and it has to be optimized to fully utilize the advantages of ceramic and carbide cutting tools.
INTRODUCTION
N the recent advancement of technology, the quality of machined products depend on the cutting parameters such as cutting speed, feed rate, tool materials etc. In this paper, the best available machining parameters such as cutting speed, feed and depth of cut should be selected from different combinations on machining die steel using Ti [C, N] mixed alumina based ceramic cutting tool and uncoated carbide cutting tool to optimize the economics of machining operation (minimize cost, machining time, and maximize the tool life).
LITERATURE REVIEW
Genetic algorithm in novel heuristic approach is considered to be one of the best techniques because the optimal solution obtained from GA technique is most likely to be a global solution [Deb 1996]. Genetic algorithm is a multi-path algorithm that searches many peak in parallel, and hence reducing the possibility of local minimum trapping. So, many researchers have attempted to solve the problem of optimization of machining parameters using GA. Most commonly considered optimization objectives include production time, machining cost, material removal rate and a combination of these, and tool usage [Jawahir 2006].Suresh et al. [2002] attempted to optimize the machining parameters of coated carbide cutting tool for optimal surface finish using GA. Saravanan et al.
EXPERIMENTAL WORK
In this study, Ti [C, N] mixed alumina based ceramic (CC 650) and uncoated carbide (WC-Co) inserts were used to conduct machining operation and experimental setup. In this experiment, the insert specification of the alumina based ceramic cutting tool is CNGN 12 04 08 and uncoated carbide cutting tool is DCMT11 T3 04.Machining tests were carried out on a precision lathe having 18 spindle speeds and 18 table feeds with a maximum speed of 2500 rpm on die steel (AISI D2). The machining study was carried out on die steel at different cutting speeds and feed rates combinations in a dry environment (without any cutting fluid).Cutting speeds of 129m/min, 208m/min and 400m/min, at the cutting feeds of 0.06mm/rev, 0.08mm/rev 0.12mm/rev and at a constant depth of cut of 0.2 mm for Ti [C, N] mixed alumina based ceramic (CC 650). And the cutting speeds of 78m/min, 99m/min and 122m/min, at the cutting feeds of 0.06mm/rev, 0.08mm/rev 0.12mm/rev and at a constant depth of cut of 0.2 mm for uncoated carbide cutting tool. The tool wear, surface roughness and the cutting force readings were noted. Machining was stopped periodically to measure tool wear, cutting force and surface roughness. The machining time, tool setup time, idle time and tool change time were accurately measured with a stopwatch. The tool wear measurements were taken using a toolmakers microscope (Metzer-model METZ 1395) with 30X magnification factor. The machining parameters at maximum allowable wear as per ISO 3685 for tool life testing were found out (Senthil Kumar et al. 2003). The surface roughness was measured using a surface roughness measuring instrument (Mitutoyo, Model-Surftex 301). The above readings were used in SYSTAT software to find the models for surface roughness using MRA. In a machining process set up time, idle time, machining time and tool change time are important and they are used in the analysis to find the optimum cutting parameters. Setup time is the time spent in setting up the work piece in work holding device and the cutting tool in tool holding device. Idle time is the time during which the cutting tool does not cut. Tool change time is the time taken to change the cutting edge of the tool after it is worn out. The actual set up time, idle time and tool change time were noted methodically. The various costs, time and volume of material removal are found out actually so that results will be realistic.
CONCLUSION
In Novel heuristic approach, mainly genetic algorithm has been employed to find the optimal machining parameters for the turning operation. In that, we found the optimized parameters at lowest unit production cost. From the experimental work carried, we conclude that the optimized parameters are found to be acceptable for the usage of Ti [C, N] mixed alumina ceramic cutting tool and carbide cutting tool with the combination of the AISI D2 steel as the work material. The optimized cutting speeds for ceramic cutting tool and carbide cutting tool are 315 m/min and 92 m/min respectively. The ceramic tool has 0.55 times lower unit production cost and 1.5 times higher tool life than the carbide tool at their respective optimized machining parameters. Hence it can be recruited as one of the optimized result.