Experimental investigation and analysis for selection of rapid prototyping processes

Transcription

1 ISSN Experimental investigation and analysis for selection of rapid prototyping processes #1 V. E. Kothawade 1, #2 S. P. Kakade 2 #3 A. P. Khot 3 1 vaibhavkothawade4@gmail.com 2 sachinkakade2107@gmail.com 3 khot_amey@yahoo.co.in 1,2 Department of Mechanical Engineering, SPPU, MET s BKC IOE, Nashik, India 3 Department of Mechanical Engineering, MSBTE, SIT Poly.Nashik, India ABSTRACT Selection of correct and optimize way of the prototype manufacturing is one of the immense important requirement for manufacturers. Increasing demand of Rapid Prototyping (RP) variants makes this selection procedure more complicated and requires greater expertise involvement and hence takes more time. Eventually the selection is based on the previous records and performance of the available method with some suggestions from the expertise. This paper propose the way of selection of rapid prototyping methods with reference to part requirement such as surface finishing, accuracy, build time, Tensile Strength, cost. The study reveals the part and process requirement for efficient and economical output through prototype manufacturing. Such process selection creates criteria for conflict free decision making which routes to the minimization of prototype manufacturing lead time. It utilizes the Analytical Hierarchical Process (AHP) which is theory of measurement through pair wise evaluation and priority scale given by expertise along with the Grey Relational Analysis (GRA) to establish an accurate and correct hypocritical model which decides priority of the methods which can be implemented. Process selection will help in systematic and analytical manner to address every element of selection - complexity due to interrelation of attributes, wide range of Rapid Prototyping alternatives available, variety of Rapid Prototyping application areas, and expertise requirement. ARTICLE INFO Article History Received :18 th November 2015 Received in revised form : 19 th November 2015 Accepted : 21 st November, 2015 Published online : 22 nd November 2015 Keywords Analytical Hierarchical Process (AHP), Grey Relational Analysis (GRA), Part requirements, Rapid prototyping applications, Selection of Rapid Prototyping methods. I. INTRODUCTION Any organization in current era is trying to sustain in this immense competitive business through product development and modification. This modification leads to change in design, change in aesthetics or complete product change. For such changes the organization must ensures the available resources capability and their vendors capacity to accommodate the change. The special tooling requirement for manufacturing, another inspection media required or not, capability of existing machinery to accommodate the changes in the product can be easily visualized with the help of available physical model i.e. Prototype. Wide variants are available to manufacture the prototype in rapid prototyping methods. Selection of the correct method is very complex and requires more time.appropriate technology selection is difficult, multitasking and complex process. The best process selection depends upon many attributes which are many times interrelated to each other. It is a complex problem that cannot be solved readily using conventional statistical techniques alone. Selection of an appropriate process requires a sound understanding of the interactions between the part quality, part properties, part cost, build envelope, build time (speed) and other concerns [8].Process selection will help in systematic and analytical manner to address every element of selection - complexity due to interrelation of attributes, wide range of RP 2015, IERJ All Rights Reserved Page 1

2 alternatives available, variety of RP prototype application areas, and expertise requirement [7]. II. LITERATURE REVIEW Sayed Masood etl. [1] developed the IRIS system selector following the methodology which accepts inputs and commands to produce a search reports detailing a most appropriate RP system. Vikram shende etl. [2] studied the part requirement to generate the feasible alternative process and after evaluation of data with graph theory, matrix approach and TOPSIS method they proposed the decision support system. H.S. Byun, K.H. Lee [3] proposed the methodology which deals with Selection of optimal RP III. METHODOLOGY In this paper, for calculation of weightage of each defined criteria AHP and to find the rankings of RP methods GRA is selected. The methodology is described in following steps [9]. A. Definition of Criteria for Method Selection The first step of method evaluation is to define a criteria or factor on the basis of which the performance of method is to be evaluated [9]. B. Finding of Weights of Defined Criteria by AHP The Analytic Hierarchy Process (AHP) is a theory of measurement through pair wise comparisons and relies on the judgments of experts to derive priority scales. It is these scales that measure intangibles in relative terms [9]. 1 b b b 1) Calculating the geometric mean of the i th row:- system using modified TOPSIS method. Mahmood Shaharbi etl. [4] Presents combined methodology for identification, comparison, and selection of RP system using AHP and modified TOPSIS method. Biranchi Narayan Panda etl. [5] uses Integrated AHP and Fuzzy TOPSIS approach for the selection of RP process under multi criteria perspective. Fancois Peres etl. [6] Introduced and discussed the problematic about intersect of implement of concurrent engineering and selection of RP process suggesting design methods and focus on relevant RP technologies and practical case studies. B = b 1 b b b b 1 b b b b 1 Construct a pair-wise comparison matrix using a scale of relative importance. The judgments are entered using the fundamental scale of the AHP. An attribute compared with it is always assigned values 1. The numbers 3, 5, 7, and 9 correspond to the verbal judgments moderate importance, strong importance, very strong importance and absolute importance (with 2, 4, 6, and 8 for compromise between these values). Assuming M attributes, the pairwise comparison of attribute i with attribute j yields a square matrix BM x M where aij denotes the comparative importance of attribute i with respect to attribute j. In the matrix, bij = 1 when i = j and bji = 1/bij.Find the relative normalized weight (w j ) of each attribute by, TABLE I Pair-Wise Comparison Matrix R 1 1 ½ A 2/ S ½ ½ T ¼ 1 ½ 1 1 C SUM Col After computing the weights the consistency of weights should be check. [9]. TABLE II Weghtage of Parameters Parameters Weight R 36 A 23 S 17 T 13 C 11 2) Normalizing the geometric means of rows in the comparison matrix:- This can be represented as below, GM = b And w = GM GM C. Ranking by Grey Relational Analysis The Grey relational analysis in the Grey theory (Deng, 1982) established a complete and accurate evaluation model for ranking [10]. 2015, IERJ All Rights Reserved Page 2

3 D. Grey Relational Generating When the units in which performance is measured are different for different attributes, the influence of some attributes may be neglected. Therefore, TABLE III Measurement Value for each Evaluation Criteria SLA SLS DP FDM For MADM problem, the ith alternative can be expressed as Ai = (yi1, yi2, yi3,,yij, yim ) where yij is the performance value of attribute j of alternative i. The term yi can be translated into the comparability sequence Xi = (xi1, xi2, xi3 xij xim.) by equations (1) and (2). y i j Min y i j, i 1, 2,..., n Max y i j, i 1, x i j 2,..., n Min y i j, i 1, 2,..., n... (1) Max y i j, i 1, 2,..., n y i j x i j Max y i j, i 1, 2,..., n Min y i j, i 1, 2,..., n (2) Equation (1) is used for larger-the-better attributes and Equation (2) for the smaller- the better attributes. TABLE IV Data Rationalizing SLA SLS DP FDM E. Reference sequence definition After the grey relational generating procedure, all the performance values are scaled into [0, 1]. An alternative will be the best choice if all of its performance processing all performance values for every alternative into comparability sequence, in a process analogous to normalization, is necessary. This processing is called grey relational generating in GRA [10]. values are closest to or equal to 1, however, such type of alternative may not exist [10]. F. Grey relational coefficient calculation Grey relational coefficient is used for determining how close x ij and x oj. The larger the grey relational coefficient, the closer x ij and x oj are. The grey relational coefficients can be calculated by equation (3). x x minmax 0 j, i j i jmax Where γ (x o j, x i j ) is the grey relational coefficient between x o j and x i j, and ij x oj min=min {Δ ij, i = 1, 2 n; j = 1, 2 m}, max =Max {Δ ij, i = 1, 2,, n; j = 1,2,..m}, ζ is the distinguishing coefficient, ζ є [0,1]. After grey minwill relational generating, max will be equal to 1 and be equal to 0. x ij TABLE V Grey Relational Coefficient SLA SLS DP FDM G. Grey relational grade calculation After calculating the entire grey relational coefficient γ (x oj, x ij ), grey relational grade can be calculated using (4). m X O, X i w j x oj, x ij j 1 fori 1,2,...,n (4) (X o, X i ) is the grey relational grade between X o and X i. The highest grey relational grade with the reference sequence, it 2015, IERJ All Rights Reserved Page 3

4 means that the comparability sequence is most similar to the reference sequence, and that alternative would be the best choice [10]. TABLE VI Grey Relational Grade SLA SLS DP FDM IV. CONCLUSIONS This paper tries to propose an evaluation method to determine the overall performance for each RP method. The optimum decision can then be made based on the overall performance. Moreover, from the equations and a numeric example for method selection, this study obtains the following advantages: It is very convenient to perform overall measurement based on each component s requirements. The overall performance can determine the order for selecting the suitable RP method. The enterprise can choose its own appropriate goal and weighting value for each evaluation factor based on the characteristic demand of the component in order to select the most suitable component. TABLE VII Grey Relational Grades & Ranking GRA Grades Ranking SLA SLS DP FDM 0.5 R SLA 0.4 C A 0.2 SLS 0 3DP FD M T S The above graph depicts that the considered parameters in the various RP methods are performing better when they are closer to the outer side of the radar and poor performing or undesirable parameters are near to the zero point or closer to it. For any change in the obtained values the parameter must need to be optimize or improved. REFRENCES [1] Syed H. Masood and Mazen Al-alawi, The IRIS Rapid Prototyping System Selector for Educational and Manufacturing Users, Int. J. Engng Ed. Vol. 18, No. 1, pp , 2002 [2] VikramShende*, Dr.PrafullaKulkarni, Decision Support System for Rapid Prototyping Process Selection, International Journal of Scientific and Research Publications, Volume 4, Issue 1, January ISSN [3] H.S. Byun, K.H. Lee, A decision support system for the selection of the RP process using the modified TOPSIS method, Int.J. Adv. ManufTechnol (2005) 26: , DOI /s [4] MahmoodShahrabi, MehrdadJavadi, Selection of Rapid Prototyping Process Using Combined AHP and TOPSISMethodology, International Journal of InformationScienceand System, 2014, 3(1): 15-22,ISSN [5] Biranchi Narayan Panda, BibhutiBhusanBiswal, B B L V Deepak, Integrated AHP and fuzzy TOPSIS Approachfor the Selection of a Rapid Prototyping Process undermulti-criteria Perspective Department of IndustrialDesign, NIT Rourkela ,2013. [6] Frencoisperes, Carmen martin, Design methods applied. to the selection of a rapid prototyping resource Rapid PrototypingCenter, LaboratoireProductiqueLogistiqueEcoleCentrale de Paris, Grande Voie des Vignes Châtenay- Malabrycedex FRANCE,2010. [7] Ahmed M. Romouzy-Ali, SiamakNoroozi, Philip Sewell, and Tania Humphries-Smith, Adopting Rapid Prototyping Technology within Small and Medium- Sized Enterprises: The Differences between Reality and Expectation, International Journal of Innovation, Management and Technology, Vol. 3, No. 4, August 2012 [8] M. N. Islam, Brian Boswell and A. Pramanik, An investigation of dimensional accuracy of parts produced by Three dimensional printing Proceedings 2015, IERJ All Rights Reserved Page 4

5 of the World Congress on Engineering 2013 Vol I, WCE 2013, July 3-5, 2013, London, U.K. [9] Chi-Hung Tsai, Ching-Liang Chang, and LiehChen, Applying Grey Relational Analysis to the Vendor Evaluation Model International Journal of The Computer, The Internet and Management, Vol. 11, No.3, 2003, pp. 45, 53. [10] Thomas L. Saaty, Decision making with the analytic hierarchy process Int. J. Services Sciences, Vol. 1, No.1, , IERJ All Rights Reserved Page 5