Manufacturing Technology Today

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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R. Saravanan ¹, V. Janakiraman ²

Affiliations
1 Dept. of Mechatronics, Kumaraguru College of Technology, Coimbatore, IN
2 Dept. of Mechanical Engg, Arulmigu Kalasalingam College of Engg., Krishnan Koil, IN

Abstract

Machining optimization has an inevitable role in contemporary operation of CNC and non-conventional machining processes. The operating parameters in this context are cutting speed, feed rate, depth of cut etc. that do not violate any of the constraints that may apply on the process and satisfy the objective function such as minimizing the total production time or production cost or maximizing the production rate or combination of several objective functions. Turning is one of the important operations in industries. Hence it is desired to optimize the operating parameters of the turning process. In this work a turned component is considered with turning, facing and undercutting operations. The objective function is to minimize the machining time with constraints such as cutting power, cutting force, tool life, surface finish of the product. For solving the above problem optimization procedure was developed using Genetic Algorithm. The optimization problem was solved and the result was compared with the current practice used in industries.

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R. Saravanan ¹, S. Ramabalan ², C. Balamurugan ³, A. Subash ²

Affiliations
1 Dept. of Mechatronics Engg., Kumaraguru College of Technology, Coimbatore, IN
2 J. J. College of Engg. and Technology, Trichy, IN
3 Dept. of Production Engg., J. J. College of Engg. and Technology, Trichy, IN

Abstract

This paper presents a new general method fo r computing the optimal motions of industrial robot manipulators in the presence of fixed and moving obstacles. The mathematical model considers the nonlinear manipulator dynamics, actuator constraints, joint limits and obstacle avoidance. The problem considered has 5 objective functions, 88 variables and 21 constraints. Two evolutionary algorithms such as Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Differential Evolution (DE) techniques have been used for the optimization. Given the initial and final configurations, the trajectory is defined using B-spline function and is obtained through off-line computation for on-line operation. The obstacles are considered as objects sharing the same workspace performed by the robot. The obstacle avoidance is expressed in terms of the distances between potentially colliding parts and the motion is represented using translation and rotational matrices. Numerical application involving an industrial manipulator (Adeptone XL robot) is presented. The results obtained from NSGA-II and DE are compared and analyzed. A comprehensive user-friendly general-purpose software package has been developed for the DE algorithm using VC++ to obtain the optimal solutions.

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R. Saravanan ¹, S. Ramabalan ², P. Sasikumar ³, P. Sriram ²

Affiliations
1 Dept. of Mechatronics Engg., Kumaraguru College of Technology, Coimbatore, Tamil Nadu, IN
2 J. J. College of Engg. and Technology, Trichy, Tamil Nadu, IN
3 Dept, of Production Engg., J. J. College of Engg. and Technology, Trichy, Tamil Nadu, IN

Abstract

This paper concerns with the use of intelligent techniques such as Genetic Algorithm (GA) and Differential Evolution (DE) to find optimum geometrical dimensions of a robot gripper. The problem is finding a combined objective function, which has five objectives, seven constraints and five variables. The objective functions are difference between maximum and minimum griping forces, force transmission ratio between gripper actuator and gripper ends, shift transmission ratio between gripper actuator and gripper ends, length of all elements of gripper and efficiency of gripper mechanism. The problem is dealt with three cases. A very original, new optimization model is derived and used. Also, a comprehensive user-friendly general-purpose software package has been developed to obtain the optimal parameters using the proposed DE algorithm.

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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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R. Saravanan ¹, S. Ramabalan ², M. Thilak ²

Affiliations
1 Dept. of Mechanical Engg., Kumaraguru College of Technology, Coimbatore, IN
2 J.J. College of Engg. and Technology, Trichy, IN

Abstract

The minimum-time path for a robot arm has been a long standing and unsolved problem of considerable interest. This paper presents two intelligent optimization algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) for obtaining minimum time paths for robot manipulators. To illustrate the proposed methods, time optimal trajectories for a two-link IBM planar robot Is considered in this work. The results obtained using the proposed GA and PSO are compared with neural network results equations. It Is proved from this paper that the proposed intelligent optimization algorithms can be used successfully to find optimal travel time by approximating the robot inverse dynamics. Also both proposed GA and PSO methods give better results than the neural network.

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T. Kannan ¹, R. Saravanan ¹, T. Ramesh ¹

Affiliations
1 Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore-641006, IN

Abstract

Welding process is used in most of the manufacturing Industrie which requires metal joining in a large scale. In any welding process it is very essential to optimize process parameters in order to achieve desired weld bead characteristics. In this work, combined objective function of maximizing the bead penetration, minimizing the dilution, reinforcement and width w/as considered. Four SAW process parameters (voltage, wire feed rate, welding speed, and nozzle to plate distance) were identified for optimization subjected to realistic process constraints. Several conventional techniques had been suggested in the literature for solving this problem. But these techniques are not robust and take iot of time to find the global optimum and are difficult to understand and implement. In order to over come the difficulties with conventional techniques a new technique called particle swarm optimization is implemented In this work. PSO is a simple and powerful technique based on the concept of social interaction to problem solving. In PSO a swarm search of n individuals communicate either directly or indirectly with one another for getting the search direction. This algorithm starts with 20 particles (solutions) and searches for the new ones by updating the velocities. Maximum of 500 iterations were performed and the solution was obtained. Software has been written using VC++ language. The solution obtained by this procedure is found to be superior. The computational effort is very less and easy to implement.

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K. Raja ¹, R. Saravanan ¹, V. Selladurai ²

Affiliations
1 Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore - 641 006, IN
2 Department of Mechanical Engineering, Coimbatore Institute of Technology, Coimbatore - 641 014, IN

Abstract

Optimization of parallel machine scheduling in a shop floor environment is considered with the objectives of minimizing both the total earliness and the total tardiness. A hybrid optimization procedure based on genetic algorithm and fuzzy logic technique has been developed in this work in order to achieve the multi-objective functions. The proposed optimization procedure is compared with the existing heuristics and the performances are analyzed.

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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. Thangadurai ¹, J. Jerald ¹, P. Satheesh ¹, R. Saravanan ²

Affiliations
1 School of Mechanical Engineering, Shanmugha Arts, Science, Technology & Research Academy, Thanjavur - 613 402, IN
2 Department of Mechanical Engineering, J.J. College of Engineering, Tiruchirappalli-620 009, IN

Abstract

Wire Electric Discharge Machining process is one of the important nontraditional machining process which is used to machine hard materials, complex shapes and contours which are difficult by conventional methods. In this paper Particle Swarm Optimization (PSO) and Memetic Algorithm (MA) based optimization procedures have been developed to optimize machining parameters viz machining speed, pulse on time, pulse off time, and peak current by using two response equations for material removal rate and surface roughness. The objective function considered for optimization is maximization of material removal rate and minimization of surface roughness. Here objective function is solved by taking combined objective function (weightage given 50% to material removal rate and 50% to surface roughness) i.e. minimization of material removal rate and surface roughness. The output results o f these two algorithms are compared.

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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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