Optimum Casting Process Selection using Analytical Hierarchy Process

Transcription

1 Volume-5, Issue-5, October-2015 International Journal of Engineering and Management Research Page Number: Optimum Casting Process Selection using Analytical Hierarchy Process Saraswati Chaturvijani 1, R. Banchhor 2 1,2 Department of Mechanical Engineering, INDIA ABSTRACT This paper present our research work on optimum casting process selection using Analytical Hierarchy Process (AHP).The goal is to determine best optimum casting process for a component. AHP provide a way to rank the alternative of a problem by deriving priorities. Nineteen criteria have been quantified to optimize casting process selection and development of casting process. A decision hierarchy for optimum casting process selection has been developed. In this paper a case study of prototype product has been taken. The proposed methodology takes casting expert opinion on the basis of criteria, product details and alternative casting processes for ranking the problem. Keywords Analytical Hierarchy Process, Optimum Casting Process (OCP) Selection, Multi Criteria Decision Making, Framework, Evaluation. I. INTRODUCTION Metal casting a very oldest technology offers the widest variety of routes to produce component in a range of shapes, sizes, metals, quantities and quality requirements. These routes (Figure 1.1) are usually classified in terms of the material and the method of producing the mold (green sand mold created by a permanent pattern, etc.) and how the metal flows into the mold (gravity, centrifugal force, vacuum, low pressure, high pressure). In practice, the process characteristics also depend on the equipment, manpower skills, quality management practices, and other company-dependent factors. Selecting the most optimum process for the given product requirements is a critical step in product life cycle. This influences all downstream decisions, such as the type of tooling, vendor selection, machining operations and quality control procedures. These in turn affect the tooling and labor costs, quality assurance and total lead time; these final factors together are referred to as castability. Product design and process design must proceed concurrently to achieve overall optimization in the shortest time. However, selecting the most suitable casting process and evaluating its optimality is a difficult task for a product designer. While most designers are aware of the importance of design for castability, they are often unaware of the variety of casting processes, their characteristics (compounded by their dependence on material and other factors) and their influence on castability. Casting is a simple, inexpensive and versatile way of forming aluminum into a wide array of products. Such items as power transmissions and car engines and airplane parts were all produced through the aluminum casting process. Most castings, especially large aluminum products, are usually made in sand molds. II. LITERATURE REVIEW The literature has been reviewed from the perspective of Analytical Hierarchy Process and Casting Process selection. In the 1980s Thomas L. Saaty developed the Analytic Hierarchy Process (AHP) technique, which constructs a decision-making problem in various hierarchies as goal, criteria, sub-criteria, and decision alternatives. Arun Mathews Varikatt and Dr. Haris Naduthodi, deals with the collection of defect data from an extrusion facility, analyzing the data and identify the critical defects and rank their causes using Fuzzy Data Envelopment Analytical Hierarchy Process (FDEAHP). Thomas L. Saaty, Kirti Peniwati, Jen S. Shang, they showed through examples how to apply the absolute measurement of the AHP together with LP to determine which positions to fill and which candidates to hire to satisfy the salary and employee number requirements and constraints for each position. A Er, R. Dias, A rule-based expert system has been proposed for casting process selection to assist casting product designers in making correct process choice decisions for a given design situation. R. G. Chougule & B. Ravi, develop a systematic 192 Copyright Vandana Publications. All Rights Reserved.

2 approach to casting process planning in a 3D design environment III. METHODOLOGY In this paper, Analytical Hierarchy Process (AHP) is used for selection of Optimum Casting Process Selection. The main aim of this paper is to develop a framework and use AHP technique for evaluation of optimum casting process among the alternatives. The method with evaluation process is shown in figure (2). AHP Analytical Hierarchy Process is a Multi-Attribute Decision Making (MADM) approach. It involves structuring multiple choice criteria into a hierarchy, assessing the relative importance of these criteria, comparing alternatives for each criterion, and determining an overall ranking of the alternatives. This approach developed by Thomas L. Saaty (1980), is one of the most widely used MADM techniques in decision-making field. Saaty described case applications ranging from the choice of a school for his son to the planning of transportation systems for the Sudan.It has been widely used in corporate planning, portfolio selection, and benefit/cost analysis. IV. CASE STUDY The case study has been conducted in an Indian aluminium casting component manufacturing organization. There existed a need for organization is prioritize optimum casting process selection. COMPUTATIONAL STEPS FOR SELECTION OF OPTIMUM CASTING PROCESS TABLE 1: Dimensions and criteria used for Optimum casting process selection Step 1: Problem analysis and construction of model. The first step of AHP method is the analysis of problem and model construction for selection of right casting process. The main aim is to select the optimum casting process among alternatives, which will give maximum financial benefit and achieve the goal. In this study, four dimension and 19 criteria are used for optimum casting process (OCP) selection, which is shown in table 1. Five alternatives A1, A2, A3, A4 and A5 are being considered as shown in figure (1). The detailed evaluation model is shown in fig. (2). Step 2: Determine pair-wise comparison of component or dimension. The second step of AHP methodology is to perform pair-wise comparison between components. First we determine the relation between criteria and dimension using experts opinion then the pair-wise comparison will be done using 193 Copyright Vandana Publications. All Rights Reserved.

3 AHP technique. The scale is required for pair-wise comparison, here 1-9 scale is used, and the comparison scale with their linguistic terms is shown in table 2. Calculate Eigen value and Eigen vector for all matrix and normalize each matrix, the sum of normalized value should be one. The pair-wise comparison matrix of dimensions is shown in Table Copyright Vandana Publications. All Rights Reserved.

4 TABLE 3: Pair-wise comparison matrix of dimension Step 3: Determine pair-wise comparison between criteria In this step, pair-wise comparison will be done between criteria. In this paper, we have taken 19 criteria under four dimensions for OCP selection. So four pairwise matrix will be created. Example of one of the pairwise comparison matrix for Design (D1) dimension having criteria material (C1), (C2), (C3),..,(C7) is shown in Table 4. TABLE 4: Pair- wise comparison between criteria under design dimension Fig. 2. Proposed model for optimum casting process selection TABLE 2: Linguistic scale for optimum casting process selection Step 4: Establish evaluation for alternatives. In this step, after completion of pair-wise comparison of criteria, we have to make evaluation of alternatives. In this paper, we have taken five alternatives i.e. A1, A2, A3, A4 and A5, which is shown in figure 1. The pair-wise comparison of alternatives is performed for each criteria, which influence the dimension. In this step, total 19 pair-wise comparison matrices are being formed. Example of one of the pairwise comparison matrix of alternatives under Design (C1) criteria is shown in table 5. TABLE 5: Pair-wise comparison matrix of alternatives under design 195 Copyright Vandana Publications. All Rights Reserved.

5 Step 6: Design a supermatrix and Ranking of optimum casting processes The calculated Eigen Vector of all 19 pair-wise comparison matrices for alternatives under 19 criteria is used in final supermatrix for OCP selection, as shown in Table 6. Final ranking for OCP selection we get. Place each value in the final supermatrix, then ranking of the OCP is done.the calculated values are A1 = 0.452, A2 = 0.280, A3 = 0.156, A4 = and A5 = Copyright Vandana Publications. All Rights Reserved.

6 S. No Calculated Weights TABLE 6: Design a supermatrix A1 A 2 A 3 A 4 A 5 A1 A2 A3 A4 A Ranking V. CONCLUSION 1. Pressure die casting (alternative A1) is found to be the optimum casting process for that component A1= Ranking of this alternative processes are A1=0.452, A2=0.280, A3=0.156, A4=0.081, A5= All matrix data have consistency ratio 0.1 i.e. acceptable. 4. Optimum casting process selection formulated as a multi criteria decision making (MCDM) problem. 5. AHP is used as a solution methodology. 6. Four dimension and nineteen criteria are used for optimum casting process selection. 7. Computations are performed using AHP. REFERENCES [1] Sarah S.Y. Lam, Kimberly L. Petry and Alice E. Smith (2000) Prediction and optimization of a ceramic casting process using a hierarchical hybrid system of neural networks and fuzzy logic. IIE Transactions 32, [2] R.G. Chougule, B. Ravi, (2003), Casting Process Planning Using Case Based Reasoning. Transactions of the American Foundry Society, 111, [3] R. K. Singh, S. Kumar, A. K. Choudhury and M. K. Tiwari, (2007) Lean tool selection in a die casting unit: a 197 Copyright Vandana Publications. All Rights Reserved.

7 fuzzy-based decision support heuristic, International Journal of Production Research, 44:7, [4] Crister Charlsson, Robert Fuller (1996) Fuzzy multiple criteria decision making: Recent development. Fuzzy sets and systems [5] Askiner Gungor, Surendra M. Gupta (1999), Issues in environmentally conscious manufacturing and product recovery: a survey, Computers & Industrial Engineering 36 (1999) [6] Khalid Hafeeza, YanBing Zhanga, Naila Malak (2002) Determining key capabilities of a firm using analytic hierarchy process, International Journal Production Economics [7] R.G. Chougule, B. Ravi, (2007), Collaborative design for manufacture- Metal casting applications. Transactions of the American Foundry Society, 111, [8] Thomas L. Saatya, Kirti Peniwatib, Jen S. Shang (2007) The analytic hierarchy process and human resource allocation: Half the story, Mathematical and Computer Modelling [9] Alessio Ishizaka, Ashraf Labib (2011) Review of the main developments in the analytic hierarchy process, Expert Systems with Applications [10] Milind Akarte, B Ravi (2002) Web-based process and producer evaluation for collaborative engineering of cast product, IDMME. 198 Copyright Vandana Publications. All Rights Reserved.