Manufacturing Technology Today

J. S. Senthilkumar ¹, P. Asokan ², R. M. Arunachalam ², D. Suresh Babu ², V. Manickam ²

Affiliations
1 Dept. of Mechanical Engg., Sona College of Technology, Salem, Tamil Nadu, IN
2 Dept. of Prod. Engg., National Institute of Technology, Tiruchirapalli, IN

Abstract

This paper describes the combination of Genetic Algorithm (GA) and Artificial Neural Network (ANN) based hybrid approach in real data based optimization. Power consumption, cutting force and surface roughness are the major constraints which directly influence the operating parameters. Many researchers have developed mathematical model of the CNC turning process for the purpose of optimizing the operating parameters. But this will be highly suitable for a particular combination of cutting tool and workpiece. In order to avoid this problem, hybrid approach is proposed in this work (i.e) Genetic algorithm and ANN. GA is used for solving the optimization machining cost and ANN is used for the constraints evaluation. Experimental results demonstrate that this hybrid optimization approach can accurately estimate machining cost without violating those constraints.

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J. S. Senthilkumaar ¹, P. Asokan ², R. M. Arunachalam ¹

Affiliations
1 Dept. of Mechanical Engg., Sona College of Technology, Salem, IN
2 Dept. of Production Engg., National Institute of Technology, Tiruchirapalli, IN

Abstract

Selection of machining parameters is a very important task to the process planer to achieve desired surface roughness with minimum machining cost. Facing operation is an important and inevitable machining process for manufacture of aero space components like disks and blades. The cutting tool manufacturer's recommended cutting parameters are usually for turning operation and common for few materials and so adapting their turning conditions for facing process results in premature tool failure and poor surface finish. Based on the machining experience and earlier research results, uncoated tools performance are better than the coated tools and necessity to control the pollution dry machining are preferred. In this paper, the surface roughness and tool wear when facing Inconel 718 using uncoated carbide cutting tools have been investigated. The results show that the feed and cutting speed were the most influential factors on the surface roughness and flank wear. The optimum machining parameters are predicted for minimization of surface roughness and minimization of tool wear by simultaneously optimized. The predicted results have good agreements with experimental results.

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R. Ravi Kumar ¹, P. Asokan ¹, J. Jerald ¹, M. Shanthi ¹

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

Abstract

Electro Chemical Machining (ECM) is a Non-traditional machining technique that has been used in the production of dies and it is originally as signed for manufacturing complex shaped components in defense and aerospace applications. The most important performance measures in ECM are material removal rate (MRR) and surface roughness (R_a). In this work, experiments were performed to determine parameters affecting material removal rate and surface roughness. The data obtained for performance measures have been analyzed using the design of experiments method. A considerably profound equation is obtained for the MRR and R_a using current, gap voltage, flow rate and gap setting parameters. SPSS software and C programming have been used to optimize the machining parameters of ECM.

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J. S. Senthilkumaar ¹, R. Saravanan ¹, P. Asokan ²

Affiliations
1 Dept of Mechatronics Engg., Kumaraguru College of Technology, Coimbatore, IN
2 Dept of Production Engg., National Institute of Technology, Tiruchirapalli, IN

Abstract

Determination of cutting parameters for tough and hard material is very important for the process planner to achieve the economic machining process. The paper proposes a new optimization technique based on genetic algorithms (GA) to optimize the objectives like minim.um surface roughness, power required and cutting force and maximum tool life. Many researchers concentrate the single objective to optimize the process parameters, this paper presents a new methodology to optimize the process parameters for each objective each time one objective will optimize and other objective will be treated as constraints. Experimental results proved that the effectiveness of proposed genetic algorithm based multiple optimizations solving this machining problem.

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S. Nickolas ¹, C. S. P. Rao ², A. V. Reddy ¹, P. Asokan ¹

Affiliations
1 National Institute of Technology, Trichirappalli, IN
2 National Institute of Technology, Warangal, AP, IN

Abstract

Data Mining aims at discovering knowledge consisting of rules describing properties of data. In this paper, the optimized schedules for a flow shop scheduling problem are generated by the metaheuristic methods. The optimal schedules are used to generate the training set by identifying the predictor attributes affecting the schedules. The classifier model is generated based on decision tree induction by discretising the data in the training set based on chi2 algorithm. The classifier model is tested by applying for the new set of problems. The optimized schedule is generated by the classifier model is comparatively equal to that of the scheduling done by the metaheuristic methods. The sees tool is used for performing the data mining classification. Thus the rules generated by the Data Mining classifier allows the production managers to easily take decisions regarding the flow shop scheduling based on various objective functions and constraints.

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R. Christu Paul ¹, P. Asokan ¹, S. Kumanan ¹

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

Abstract

The facility layout design problem is concerned with determining the arrangement and configuration of facilities, which optimizes a prescribed objective such as profit, cost, or distance, and which satisfies various prescribed constraints pertaining to available resources. In industry, facility layout design problems arise in manufacturing, in warehousing, and in various assignment type situations. The solution of this problem has impacts on the viability of the industry. For example, using the optimization methods associated with the facility layout design can reduce material-handling costs, which can comprise between 30 and 75% of the total manufacturing costs. This paper is concerned with the application of the Particle Swarm Optimization algorithm to solve the problem of optimal facility layout in manufacturing system design. The general mathematical model available in literature is followed. The production flow data for varying number of products, machines and flow line are adapted for extensive application. The paper considers the different types of material flow patterns for the generalization of proposed method. The effectiveness of Particle Swarm Optimization is evaluated and compared with the benchmarked problems.

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P. Udhayakumar ¹, S. Kumanan ¹, P. Asokan ¹

Affiliations
1 Dept. of Production Engg., National Institute of Technology, Tiruchirappalli - 620 015, Tamilnadu, IN

Abstract

Flexible Manufacturing Systems (FMS) that are equipped with several automated machine tools, automated material handling, and automated storage and automated retrieval systems are designed and implemented to gain the flexibility and efficiency of production. Effective sequencing and scheduling of the Material Handling Systems (MHS) can have a major impact on the productivity of the manufacturing system. Automated Guided Vehicles (AGVs) are widely used for material handling in such a manufacturing systems. AGVs are much more flexible than other automated material handling devices such as conveyor systems. When products change or production processes change, the reconfiguration of AGVs is much easier than that of a conveyor system. The objective of this paper is to minimize the total distance travel time of AGV in a network with bi-directional paths. In this context, different dispatching strategies such as First in first serve (FIFS), random call stations are simulated. The random call stations are simulated by using non-traditional optimization technique as Simulated Annealing Algorithm (SAA). The computer simulation algorithm is developed using C language. The outputs of the FIFS system and the random call stations using SAA are compared based on the shortest route and minimum total distance traveled and the results are presented.

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P. Asokan ¹, S. Kumanan ¹, S. Satish Kumar ¹

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

Abstract

The examination of the economics of multi-pass machining operations has significant practical importance. Determination of optimal cutting parameters like the number of passes, depth of cut for each pass, speed and feed is considered as a crucial stage in multi-pass machining. The effective optimization of these parameters affects dramatically the cost and production time of machining components as well as quality of final products. This paper outlines the development of an optimization strategy to determine the optimum cutting parameters. Total production cost model is presented in this paper for multi-pass face milling process. The developed strategy is based on the "minimization of production cost" criterion and incorporates eight technological constraints. The optimal number of passes and optimal values of cutting conditions is determined using Simulated Annealing.

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R. Ravi Kumar ¹, P. Narendar Singh ², P. Asokan ¹, S. Kumanan ¹

Affiliations
1 Department of Production Engineering, National Institute of Technology, Tiruchirappalli-620015, IN
2 Department of Manufacturing Engineering, Annamalai University, Chidambaram-608002, IN

Abstract

This paper deals with the development of mathematical models for electrical discharge machining process (EDM) and also an attempt has been made to optimize the machining parameters of EDM processes. Through hole drilling experiment has been conducted on mild steel and mathematical models are formulated for maximizing the metal removal rate and minimizing the surface roughness. SPSS and 'C' programming have been used to optimize the machining parameters for EDM.

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V. Srinivasa Raman ¹, K. Barathi Raja ¹, P. Asokan ¹, S. Kumanan ¹

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

Abstract

This paper presents the application of particle swarm optimization (PSO) technique and its variants to gearbox design problem. The gearbox design problem is a highly constrained, multi-objective optimization problem. PSO is one of the swarm intelligence (SI) techniques, which use the group intelligence behavior along with individual intelligence to solve the combinatorial optimization problem. In this work a multi-speed gearbox is designed for the HMT high speed lathe, LB-17, which is available in the Production Engineering Department, National Institute of Technology, Trichy. This work focuses on the design improvement of existing gearbox so as to increase the power output and to reduce the size, using non-traditional optimization techniques. This paper presents particle swarm optimization algorithm for finding the gearbox parameters with reasonable time.

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T. Panneerselvam ¹, G. Karthikeyan ¹, N. Baskar ¹, R. Sivasankar ², P. Asokan ²

Affiliations
1 School of Mechanical Engineering, Shanmugha Arts, Science, Technology & Research Academy (SASTRA), Deemed University, Thanjavur-613402, Tamilnadu, IN
2 Department of Production Engineering, National Institute of Technology, Tiruchirappalli-620015, Tamilnadu, IN

Abstract

Machining parameters play a significant role for successful and efficient machining operations. Therefore, it is required to use the optimal parameters, especially for NC/CNC machines. Optimization of machining parameters can be defined as the appropriate selection of economical machining parameters in order to achieve maximum profit rate or minimum production cost or maximum production rate. The aim of this work is to maximize the profit rate by an optimization technique. Researchers have used many optimization techniques to solve the various engineering problems. This paper describes effectiveness and utilization of an optimization system, called Ants Colony Algorithm, for multi tool milling operations. The Ants Colony Algorithm based Optimization procedure evaluates the unit production cost, unit production time and maximum profit rate. The machining parameters such as optimal number of passes, cutting speed, feed, and depth of cut, are subjected to the constraints-maximum power, surface finish and cutting force. The Ants Colony System is a new kind of co-operative search procedure inspired by the foraging behavior of colonies of real ants. The problem has been solved by Ants Colony Algorithm with an example taken from the literature. The result is obtained through MATLAB software and it is compared with other methods.

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R. Siva Sankar ¹, P. Asokan ¹, S. Kumanan ¹, T. Selvaraj ¹

Affiliations
1 Department of Production Engineering, National Institute of Technology, Tiruchirappalli, Tamilnadu - 620 015, IN

Abstract

This paper proposes a new optimization technique based on Adaptive Genetic Algorithm (AGA) to solve multi-pass turning optimization problems. The objective function is to determine machining parameters by minimizing the unit production cost subjected to various practical machining constraints. The proposed Adaptive Genetic Algorithm scheme for optimization of multi-pass turning operation proves p be competent with Genetic Algorithm and Simulated Annealing.

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J. Jerald ¹, P. Asokan ², R. Saravanan ³, A. Delphin Carolina Rani ⁴

Affiliations
1 School of Mechanical Engg., SASTRA (Deemed University), Thanjavur-613 402, IN
2 Dept. of Production Engg., National Institute of Technology, Trichy-625 015, IN
3 Dept. of Mechanical Engg., Kumaraguru College of Engg., Coimbatore-641 006, IN
4 Dept. of Computer Science and Engg., PR Engg. College, Thanjavur-613 403, IN

Abstract

Flexible Manufacturing System (FMS) is a highly automated system consisting of computer controlled machines and peripherals combined with intensive material and dataflow. Extensive research has been conducted to design and solve the operational problems of FMS, but many of the problems still remain unsolved. In particular, the scheduling task, the control problem during the operation, is of importance owing to the dynamic nature of the FMS such as flexible parts, tools and Automated Guided Vehicle (AGV) routings. Owing to its highly automated nature, a typical FMS has a high investment cost. Hence, it becomes necessary to identify the most efficient scheduling rules at the operating stage. Automated Guided Vehicles (AGVs) are among various advanced material handling techniques that are finding increasing applications today. They can be interfaced to various other production and storage equipment and controlled through an intelligent computer control system. Simultaneous scheduling can be defined as the scheduling of machines and a number of identical AGVs in a FMS. In this paper, simultaneous scheduling of parts and AGVs is done for a particular type of FMS environment by using a nontraditional optimization technique called Genetic Algorithm (GA). The problem considered is a large variety problem and objective is combined objective (minimizing penalty cost and minimizing machine idle time). The results are found and conclusions are presented.

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N. Baskar ¹, P. Asokan ², R. Saravanan ³, G. Prabhaharan ²

Affiliations
1 School of Mechanical Engineering, Shanmugha Arts, Science, Technology & Research Academy, Thanjavur-613402, IN
2 Department of Production Engineering, Regional Engineering College, Tiruchirappalli-620015, IN
3 Department of Mechanical Engineering, J.J. College of Engineering & Technology, Tiruchirappalli-620009, IN

Abstract

In metal cutting process, cutting conditions have an influence on reducing the production time and cost. The variables affecting the economics of machining operations are numerous and include machine tool capacity, cutting conditions of velocity, feed rate and depth of cut. This paper describes a procedure to calculate the machining conditions for milling operations according to maximum profit rate as the objective function. In this work optimization procedures based on the Memetic Algorithm were developed for the optimization of machining parameters for multi-tool milling operation. An example has been presented at the end of the paper to give clear picture from the application of the system and its efficiency The results are compared and analyzed with Method of feasible direction and Handbook recommendations.

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K. Babu ¹, N. Baskar ¹, P. Asokan ², R. Saravanan ³

Affiliations
1 School of Mechanical Engineering, Shanmuga Arts Science Technology and Research Academy, Thanjavur-613402, IN
2 Department of Production Engineering, National Institute of Technology, Trichy, IN
3 Department of Mechanical Engineering, J.J College of Engineering and Technology, Trichy, IN

Abstract

A Simulated Annealing (SA) based optimization procedure has been developed to optimize grinding conditions viz. wheel speed, workpiece speed, depth of dressing and lead of dressing, using multi-objective function model with a weighted approach for surface grinding process. The procedure evaluates the production cost, production rate and surface finish for optimum grinding conditions, subjected to thermal damage, wheel-wear parameter, machine-tool stiffness and surface finish or production rate constraints. A computer program written in Visual C++ has been developed for optimization computations. The program prompts the user to input or modify all the constants related to the grinding operations. All the constants used here are default values, unless over-written by the user. User can also alter the specific Input values to perform sensitivity analysis of the relative contributions of grinding parameters to the weighted objective function. Simulated Annealing (SA) optimization is illustrated with an example and the optimum results are compared with Quadratic programming and Genetic Algorithm techniques.

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A. Thillaivanan ¹, P. Asokan ¹, G. Prabhaharan ¹

Affiliations
1 Department of Production Engineering, Regional Engineering College, Tiruchirappalli-620015, IN

Abstract

The tool and die making industry has always been faced with an increasing demand for precision and accuracy. The necessity to machine hard materials, complex shapes and contours which are difficult by conventional methods has created the need for Wire Electro Discharge Machine (WEDM). The economy of machining mainly depends on the machining parameters. Optimization of machining parameters is an important step and has been dealt by many researchers. In this paper Genetic algorithm {GA}, Simulated annealing (SA) and Continuous Ants Colony algorithm (CACO) based optimization procedures have been developed to optimize machining parameters viz. pulse on time, pulse off time, peak current, wire feed rate, and machining speed. The Objective functions considered for the optimization of cutting parameters are viz. maximization of Metal Removal Rate (MRR) and minimization of surface roughness. Test results show that among the other optimization techniques, CACO Is an effective method for wire EDM.

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