Selection of 3D Printer for Innovation Centre of Academic Institution Based on AHP and TOPSIS Methods

Abstract

This paper describes a computer-based tool for the selection of 3D printer for educational propose by using Multi Attribute Decision Making (MADM) strategies particularly Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). In education, 3D printing technologies facilitate improved learning, skills development, and increased student and teacher engagement with the subject matter. Furthermore, 3D printing sparks greater creativity and collaboration in solving problems, to settle on a best option for teaching learning process tasks into account. MADM methods are interpretative processes which are well suited in choice of different 3D printers. This work suggests AHP and TOPSIS to judge 3D printer alternatives for choice of method, based on the AHP and TOPSIS methodology, ranks available techniques by a score resulting from the composition of priorities at different levels, each considering homogeneous and independent evaluation criteria. In this work proposes a comprehensive list of key factors that have a significant influence on 3D printer selection. In this work type of material used for printing considered as common for all printers such as ABS (Acrylonitrile Butadiene Styrene), PLA (Polylactic Acid), PET or Polyethylene terephthalate etc. A total of 09 sub-criteria have been identified and grouped under three main criteria, namely, (i) Physical Characteristics (ii) Economic consideration, (iii) Operational Requirements. These entire criteria area unit extracted from on-line literature and skilled opinion. Result of study shows that 3D Printer one (ET4 PRO 3IDEA model) was designated because the best suited for Innovation Centre Academic Institution.

Introduction

I. INTRODUCTION

Innovation is characterized by breaking patterns both by thinking differently and acting in new ways. A natural way to put across new innovative ideas is to communicate, share experiences and in collaboration with others build a thorough understanding of given ideas, concepts and range of feasible manifestations (i.e. prototypes). Shared learning and exchange of experiences across intra-organizational levels is seen as a way of assessing critical areas of improvement for the engineering role in industry [1]. Main Objectives of this paper is to present variables associated with specifications of chosen 3D printer and a chance of selecting an optimum model 3D Printer to be used to facilitate improved learning, skills development, and increased student and teacher engagement with the subject matter using MADM technique. MADM ways facilitate to settle on a most effective mode by taking in account varied attribute and interpreting all the alternatives. An academic literature has some samples appliance of MADM in different sector. 3D printing applications are proliferated in all the fields due to its speed, low cost, customisation and its sustainability. Rakhade R. D.et al. [2] reported the application of MADM methods in agriculture sector. Prabhu S.R. et al. [3] have used for academic application. Severini et al. [4] have used the 3D printer for food industry application. Schubert et al. [5] reported the recent applications of 3D printers in medical field. Dwivedi et al. [6] have examined the applications of rapid prototyping (RP) in automobile sectors. Pei et al. [7] have investigated the implications of AM process in textile field. Panda et al. [8] reported that the selection of suitable 3D printer for the particular application is difficult task and involves several evaluation criteria. It is essential to select the appropriate 3D printer for a particular application; the improper selection of 3D printers may adversely affect the profitability of the organisation. The conflicting nature of the assessment criteria of 3D printer selection process can be resolved by using multi criteria decision making (MCDM) technique. The purpose of this paper is to deal with the selection of opt 3D printer for the innovation centre of academic institution. The work represented during this paper has 2 specific goals: (1) Selection of optimal 3D printing technologies (2) to offer an analytic method that's supported MADM ways for most effective selection among the choice 3D printers.

3D Printer Figure 1 [9] uses computer-aided design (CAD) to create three-dimensional objects through a layering method. Sometimes referred to as additive manufacturing, 3D printing involves layering materials, like plastics, composites or bio-materials to create objects that range in shape, size, rigidity and colour. Following are some description of paper. Section 2 provides proposes critical factors that have a significant influence on this selection process. Section 3 introduces AHP and TOPSIS decision making model by illustrating each step of model. Section 4 actual selection procedure of optimal solution among all different types of 3D printers available in market considering for demonstrate purpose to students in innovation centre. Finally, conclude and present most suitable 3D printer selection in Section 5.

II. 3D PRINTER AND ITS CRITERION SELECTION

Main aim of this study is to beat complexness of 3D printer analysis method for educational purpose, integrated with MADM ways that area unit multi attribute decision-making ways area unit used for choice method. These strategies embrace a straightforward analytic method, basic calculations, and lower level of process complexness. Several variants of delivery 3D printer are available in market that can successfully handle demonstrate purpose to students in teaching learning process. These 3D printers possess distinguishing features that might make one 3D printer more preferred over another depending on particular use cases. Therefore, selecting appropriate 3D printer is critical for both teaching and learning process. This paper proposes a comprehensive list of key factors that have a significant influence on 3D printer selection. A total of 9 sub-criteria have been identified and grouped under three main criteria namely, (i) Physical Characteristics (ii) Economic consideration, (iii) Operational Requirements. These entire criteria area unit extracted from on-line literature and skilled opinion. Detailed descriptions for each sub-criterion are provided in this section while Figure 2 visualizes hierarchical representation of sub-criterions under each main criterion.

A. Physical Characteristics (P)

1. Print Size (C1): This indicates volume of a printer gives the maximum size that one object can have in order to be printed.
2. Layer Thickness (C2): Layer thickness in 3D printing is a measure of the layer height of each successive addition of material in the additive manufacturing or 3D printing process in which layers are stacked
3. Build Speed (C3): 3D printing speed measures the amount of manufactured material over a given time period.
4. Printer Weight (C4): The total weight of a 3D printer comes down to its size, what it’s made from, and the design of the printer

B. Economical Consideration (E)

1. Product Cost (C5): This cost includes all infrastructure costs (fixed, variable, and overhead cost) associated with each unit of a 3D printer.  
2. GST Cost (C6): This cost associate with Goods and Services Tax, it is a tax that customers need to bear after they obtain any product or services, like food, clothes, things of daily desires, transportation etc. 

C. Operational Requirements (O)

1. Power Consumption (C7): Power consumption refers to the electrical energy per unit time, supplied to operate 3D printer
2. Power Requirement (C8):  The amount of potential energy between two points on a circuit
3. Extruder Temp Maximum (C9): Extrusion temperature is the temperature the extruder heats to during print. It depends on a few other variables, mainly the properties of the plastic filament and print speed.

III. PRINCIPLES MADM METHODS

This study applies two MADM techniques, AHP to see weights of attribute and AHP- TOPSIS to rank substitutes and choose most effective substitute by scrutiny each in this way. A short descriptive methodology is provided as follows.

A. AHP method

A decision hierarchy structure of AHP contains different levels that are goal, criteria, sub criteria, and alternatives. The choice method or conniving weights in AHP has 5 major steps [10]:

IV. APPLICATION OF MADM METHOD ON 3D PRINTERS

In this study standardize foremost critical parameters of ten 3D printers that are out there of late and that are appropriate for educational use ([11] – [17]). Taking under consideration, established criteria variants of solutions to current problem were adopted for analyses, as shown in Table 2. In consideration, a total of 9 sub-criteria have been identified and grouped under three main criteria, namely, (i) Physical Characteristics (ii) Economic consideration, (iii) Operational Requirements. Sub criteria were assumed, these include: Print Size (C1); Layer Thickness (C2); Build Speed (C3); Printer Weight (C4); Product cost (C5); GST Cost (C6). Power Consumption (C7); Power Requirement (C8); Extruder Temp Maximum (C9). Out of 9 sub criteria 4 are non-beneficial such as C5, C6, C7, C8 and remaining 5 are beneficial

Table 3 represents the relative importance matrix of main three criteria’s and valise consistency ratio (CR) defined by using equation 3.1. Evaluation of individual attribute was consistent and less than 10 %. Similarly Table 4, Table 5, and Table 6 represents relative importance matrix of three sub criteria and value of CR was also less than 10%. Table 7 represents global weight of respective attribute which will be used to calculate the Pi score in table 8.

Conclusion

3D Printers have extremely distributed technical options that verify requirement to pick out specific criteria their assessment. The correctness of distributed analyses depends on these criteria. The bestowed problems supported the strategy of multi-criteria optimization area unit do able to be utilized in broadly speaking understood educational sector; significantly 3D printing technologies facilitate improved learning, skills development, and increased student and teacher engagement with the subject matter By application of MADM technique, the result distinctly display best-suited device is 3D Printer one (ET4 PRO 3IDEA model). Overall conclusion is that, adopted AHP and TOPSIS methodology are associates in optimum choice for selecting the optimum 3D printer; however these are not the only methods suggested. It looks fair to acquire benefit of strategies directly using each attribute values for comparison method.

References

[1] P. Adler, Shared Learning, Management Science 36: 938–957, 1990. [2] R. D. Rakhade, et al., Optimal Choice of Agricultural Drone using MADM Methods, International Journal of Technological Innovation in Modern Engineering and Science (IJTIMES),e- ISSN :2455-2585, Volume 7, Issue 4,PP 1-7, 2021. [3] S. R. Prabhu, et al., Selection of 3D printer based on FAHP integrated with GRA-TOPSIS, Int. J. Materials and Product Technology, Vol. 58, Nos. 2/3, pp.155–177, 2019 [4] C. Severini, et al., Printing a blend of fruit and vegetables. New advances on critical variables and shelf life of 3D edible objects, Journal of Food Engineering, Vol. 220, pp.89–100, 2917 [5] C. Schubert, Innovations in 3D printing: a 3D overview from optics to organs, British Journal of Ophthalmology, Vol. 98, No. 2, pp.159–161, 2014 [6] G. Dwivedi, et al., Analysis of barriers to implement additive manufacturing technology in the Indian automotive sector, International Journal of Physical Distribution & Logistics Management, Vol. 47, No. 10, pp.972–991, 2017 [7] E. Pei, et al., Direct 3D printing of polymers onto textiles: experimental studies and applications, Rapid Prototyping Journal, Vol. 21, No. 5, pp.556–571, 2015 [8] B. N. Panda, Integrated AHP and fuzzy TOPSIS approach for the selection of a rapid prototyping process under multi-criteria perspective, All India Manufacturing Technology, Design and Research Conference, IIT Guwahati, India, 2014 [9] https://commons.wikimedia.org/wiki/File:3D_printer_EAC.jpg [10] R. V. Rao, Decision Making in the Manufacturing Environment, Springer Series in Advanced Manufacturing ISSN 1860-5168, ISBN 978-1-84628-818-0 e-ISBN 978-1-84628-819-7, 2007 [11] https://www.3dcubic.net/ [12] https://www.3idea.in/ [13] https://www.make3d.in [14] https://protomont.com/ [15] https://dir.indiamart.com/pune/fdm-3d-printer.html [16] https://www.indiamart.com/proddetail/creality-ennder-5-pro-22432169733.html [17] https://www.3dspectratech.com/

Copyright

Copyright © 2022 R. D. Rakhade, N. V. Patil, M. R. Pardeshi, B. G. Patil . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.