Informatics Publishing Limited

G. Kannan ¹, P. Senthil ¹, A. Noorul Haq ¹

Affiliations
1 Dept. of Production Engg., National Institute of Technology, Tiruchirappalli, IN

Abstract

Wire cut EDM process is a complicated machining process, which is controlled by more than ten machining parameters. The situation is really tough to find the different parametric combinations for variety of machining situations. The design of experiments coined by Dr. Genichi Taguchi is used to find the parametric combinations. The objective of the paper is to analyze and find optimum parameters for the minimum surface roughness along with cutting speed of typical die steel plate. The workpiece selected for carrying the experiment is 30 mm thick steel OHNS plate. The surface roughness is measured using a surface roughness-measuring instrument. The cutting speed is noted directly form the LED display in the machine. The experiments are planned and conducted according to the DOE process. The ANOVA analysis is done to analyze the Taguchi's design of experiments problems. The optimum conditions for the minimum surface roughness directly vary with the optimum conditions for the cutting speed.

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A. Noorul Haq ¹, T. Tamizharasan ¹

Affiliations
1 Department of Production Engg., National Institute of Technology, Thiruchirappalli-620015, Tamil Nadu, IN

Abstract

In this investigation, the effect of workpiece hardness on flank wear of cutting tool, electrical energy consumption and roughness of machined surface in dry turning operation at two different cutting speeds are experimentally investigated. The dry turning experiments are conducted with coated carbide inserts. The workpieces are selected with ten different hardness values ranging from 10 to 55HRC. The two levels of cutting speed selected are 100m/min. and 50m/min. and the other parameters such as feed rate and depth of cut are maintained constant at 0.3mm/rev. and 0.5mm respectively. Each experiment is conducted for an experimentally determined duration of 31 minutes and at the end of each experiment, the flank wear, electrical energy consumption and arithmetic average surface roughness of machined surface are measured and recorded. The flank wear is significantly affected by the hardness of workpiece and cutting speed. This analysis provides guidelines on tool and workpiece material selection in finish dry turning operations for minimizing tool wear and energy consumption by maintaining the surface roughness value at the desired level.

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K. Sivakumar ¹, R. Saravanan ², A. Noorul Haq ³

Affiliations
1 Department of Production Engineering, J. J. College of Engineering and Technology, Tiruchirappalli, IN
2 Department of Mechatronics Engineering, Kumaraguru College of Technology, Coimbatore, IN
3 Department of Production Engineering, National Institute of Technology, Tiruchirappalli, IN

Abstract

In modern manufacturing engineering tolerances plays an important role because it is directly impact quality of the product, machining cost, and quality loss. In traditional approach, tolerances have been allocated based on designer’s experience or trial and error method. Practically it is not feasible. A more scientific approach is often desirable for better performance. In this work, the optimization of tolerance allocation of over running clutch assembly and punch and die assembly are taken for analysis. This multi objective non linear, constraint, problems are solved with the Genetic Algorithm (GA). The results are compared with conventional method and the performances are analyzed.

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A. Noorul Haq ¹, T. Tamizharasan ¹

Affiliations
1 Department of Production Engineering, National Institute of Technology, Tiruchirappalli-620015, IN

Abstract

The cutting tool life has significant importance in metal turning operation. So the cutting tool life for the maximum permissible value o f flank wear (0.2 mm) is analyzed in this study, in this paper, the Taguchi methodology of optimization is applied to optimize the cutting parameters in turning when machining engine crank pin material with CBN cutting tool inserts. The turning parameters evaluated are, cutting speed, feed rate and depth of cut. The objectives of this study are maximization o f tool life and minimization of specific energy. An orthogonal array, signal-to-noise (S/N) ratio and the S/N charts are employed in order to analyze the individual and interactive effects of the selected turning parameters on the objectives. This result o f the analysis shows the optimal combination of parameters for higher tool life, lower specific energy and good surface finish. By using Taguchi's Design of Experiments (DOE), the other significant effects such as the interaction among the various turning parameters are also analyzed. The Taguchi method is more suitable to optimize the turning parameters as compared with a full factorial design.

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A. Noorul Haq ¹, T. Tamizharasan ¹

Affiliations
1 Department of Production Engineering, National Institute of Technology, Tiruchirappalli-620015, Tamilnadu, IN

Abstract

The life of cutting tool in metal cutting plays an important role in the quality and cost of product. In this present study, an empirical model for the prediction of cutting tool life in turning operation is developed. The performance of the metal turning has been studied under varying operating conditions such as speed of cutting, feed rate and depth of cut. This study describes the operation of the experimental system and presents the measured data. The required turning operation is performed on a lathe machine with hardened material used as engine crank pin for work piece and Polycrystalline Cubic Boron Nitride (PCBN) for cutting tool. For developing the required empirical model. Linear Regression, Linear Cross Product Regression, Log Transformed Linear Regression and Log Transformed Cross Product Regression are employed. The values predicted from various empirical models are compared with the experimental values and concluded that which model is best fit for the objective. In general, the metal turning experiments and statistical tests demonstrate that "the empirical models developed in this work are best fit with acceptable range of deviations".

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G. Mohan Kumar ¹, A. Noorul Haq ²

Affiliations
1 Dept. of Mechatronics Engineering, Kumaraguru College of Technology, Coimbatore-641006, IN
2 Dept. of Production Engineering, National Institute of Technology, Tiruchirappalli, IN

Abstract

Aggregate Production Plan (APP), a managerial statement of time phased production rates, workforce levels and inventory investment, considers the customer's needs and capacity limitations. It should balance various objectives viz. maximizing customer service, minimizing inventory investment, maintenance of stability in workforce, production cost minimization and profit maximization. In this paper, an APP problem Is solved for optimality using a hybrid algorithm, that combines the procedures of two efficient meta heuristics Genetic Algorithms and Tabu Search.

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P. Sathiya ¹, S. Aravindan ², A. Noorul Haq ², K. Panneerselvam ²

Affiliations
1 Department of Mechanical Engineering, J.J. College of Engineering & Technology, Tiruchirappalli-620009, IN
2 Department of Production Engineering, National Institute of Technology, Tiruchirappalli-620015, IN

Abstract

Friction Welding is a variation of pressure welding method. The joint is formed in the solid state, without melting the metal by utilizing the heat generated by friction. Consequently, some experience has already been accumulated in the industrial application of friction welding as well as in the development of the corresponding theories, but still we are way behind to find the optimal friction. This work is a step forward to achieve the best possible design. The purpose of this study is to propose a method to decide near optimal settings of the welding process parameters in friction welding of austenitic stainless steel (AISI 304) by using a Genetic Algorithm. This method tries to find near optimal settings of the welding process parameters through experiments without a model between the input and output variable. It has an advantage of being able to carryout search without modifying the design space, which includes some irregular points. The method suggested in this study is used to determine the welding process parameters by which the desired tensile strength can be obtained in friction welding. The output variable is the tensile strength. The output variable can be determined according to the input variables, which are the Heating Pressure (HP), Heating Time (HT), Upsetting Pressure (UP) and Upsetting Time (UT). This study describes how to obtain near optimal welding conditions over a wide search space conducting relatively small number of experiments. The main consideration in this study is maximization of tensile strength. Also, experimental variation of tensile strength with friction time Is verified theoretically.

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G. Mohan Kumar ¹, A. Noorul Haq ²

Affiliations
1 Department of Mechatronics Engineering, Kumaraguru College of Technology, Coimbatore, IN
2 Department of Production Engineering, National Institute of Technology, Tiruchirappalli, IN

Abstract

For any industry, it is mandatory to develop production plans to be executable for a moderate time duration, after the long range corporate decisions on planning strategies, with the reference of long-range forecasts, are developed. Management must work for this intermediate range plan also referred as aggregate production plan, consistently with the long-range policies and resources allocated by long-range decisions. Various essential factors viz., minimizing inventory investment and cost of manufacturing and maximizing profit and service to customers are to be achieved. In today's industrial manufacturing context, virtual planning of manufacturing systems finds the attention of global research activities which is due to their advantages in total elimination of wastages in man hours, materials, money, etc. Mathematical modeling and their simulations are realistic and widely accepted approaches to process various engineering problems Into expected final solutions. Metaheuristics are non-traditional approaches used to optimize various engineering and non-engineering problems. In this paper, an aggregate production-planning problem is modeled with the biologically inspired classical search procedure, Genetic Algorithms. The model is developed to simulate the above problem so as to arrive an effective solution. The implementation procedure is explained step by step and finally the results are analyzed using graphical analyzing software.

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P. Venkumar ¹, K. Chandra Sekar ¹, R. Sudhakarapandian ¹, A. Noorul Haq ²

Affiliations
1 Dept. of Mechanical Engineering, Arulmigu Kalasalingam College of Engineering, Anand Nagar, Krishnankoil - 626 190, IN
2 Department of Production Engineering, Regional Engineering College, Tiruchirappalli - 620 015, IN

Abstract

Cellular manufacturing system (CMS) is the application of Group Technology (GT) in which similar parts and machines are grouped into part families and machining cells. The success of the CMS depends on the efficiency in forming part families and manufacturing cells. In this work, a back propagation Artificial Neural Network (ANN) is developed for the above task. Learning (i.e.) adjustment of weight age to each connection in the network is incorporated using Genetic algorithm (GA). Multiple objectives (Exceptional elements, Intracell move. Intracell machine load unbalances. Intercell machine load unbalances) are considered to evaluate the goodness of the cell formation. The network is tested for different problem sizes.

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