interviews with managerial level experts. The industries located in western part of India were classified as small, medium and large-sized and have already procured AM technologies. Managerial level experts were the participants. Survey analysis revealed that top level management commitment was the most important in the prioritization of the factors. The financial factor (cost) was ranked 4th unlike the finding of Yeh and Chen (2018).
The intention to use AM technologies vary for different users. Schniederjans (2017) explored the reasons for adopting AM by different users in industries. For this, she conducted a survey. It was found that 67% of the operators had used it for developing prototype and product design, 16% for experimentation purpose, 10% for producing end- usable products and the remaining 7% for demonstrating products to customers. Apart from this, author also emphasized that management must carry out a proper cost-benefit analysis before adopting AM. In a similar study, Niaki et al. (2019) carried out a survey in different companies to identify different motives for procuring AM. A questionnaire was prepared that comprised questions the reasons for using AM in industry. The questionnaire was sent to 807 companies across 22 countries. However, out of these only 88 companies took part in the survey. After obtaining the response, the second round of questionnaire was sent to experts of different sectors to prioritize the reasons for adopting AM. It was found that the motives vary in different sectors. For example, the main reason for the medical sector to procure AM was its ability to produce customised parts. However, for automotive and aerospace sector, the main reason to adopt AM was its ability to produce complex parts.
According to authors, the dominating sustainability factor in AM was the economic aspect instead of environmental and social aspects.
Several industries show tremendous interest in AM, but they are not willing to adopt it as a manufacturing route. Yi et al. (2019) explored the reasons for this. They conducted a survey in a German-based manufacturing industry to identify the barriers to deployment of AM. Those were identified insufficient knowledge regarding AM technology, high investment cost, organization transformation and unpredictable benefits. Also, there is an apprehension about intellectual property protection and privacy protection with AM. It was emphasized that the cost estimation in AM is an effective tool that guides decision-making.
2.5.1 Decision support system for procuring 3D Printing
Researchers have also proposed several decision support models to select a proper AM based machinery as per requirement of the user. For example, Masood and Soo (2002)
proposed a rule based expert system to select the best 3D printer as per the users’
requirement. A survey in the form of questionnaires was prepared for the user as well the vendors. The questions asked were related to system features, selection criteria, applications, price and machine performance. The response rate of the vendors and users were 70% and 13%, respectively. A computer program was developed based on several selection criteria, viz., cost of the 3D printer, dimensional accuracy along the x-y and z direction, finishing of the part, range of layer thickness, available raw material and the speed of printing a part. Based on the questions and requirement of the user, the program recommends the desired 3D printer along with its specifications and other valuable information.
Byun and Lee (2005) proposed a scheme to select a suitable 3D printer based on TOPSIS, a popular multi-attribute decision making (MADM). Data obtained from the questionnaire were sent to different users such as industries, government institutes and service bureau. Based on these data, the selection criteria were accessed. Six different 3D printers were considered. Apart from cost and build time as the selection criteria, mechanical properties such as surface roughness, accuracy, tensile strength and elongation were also chosen. The mechanical properties were expressed as a single crisp value, whereas the cost and build time was expressed as a fuzzy number. Different weights were assigned to each selection criteria and based on this, a ranking order was developed.
Roberson et al. (2013) proposed a decision-making model for selecting the best printer amongst five desktop 3D printers. Three AM processes, viz., FDM, SLA and LOM were considered. The cost of 3D printers was in the range of $ 1499–$ 20900. A part was printed on each machine and the ranking system was proposed based on material cost, build time, cost of the printer and dimensional accuracy. A scaling factor was employed to compare the differences between single and multiple parts. The scaling factor (sf) for build time was given by
1 n ,
f
s t
t n (2.18) where n is the number of parts, t1 is the build time for one part and tn is the build time of n parts. Similar approach was also used for estimating the scaling factor for material usage.
If sf is less than one, the respective 3D printer is more efficient for producing multiple parts than printing a single part. Else, the printer is suitable for printing only a single part. Based on the ranking system, FDM was ranked the highest.
Recently, Raigar et al. (2020) proposed a decision-making technique comprising the best worst and proximity indexed method. Apart from all the selection criteria considered by Byun and Lee (2005), heat deflection temperature was also chosen a criterion for comparing the AM processes. It is the temperature at which a polymer starts deforming at a particular load. A spur gear was fabricated by four different processes, viz., SLA, FDM, SLS and material jetting (MJ). It was desired to have the high dimensional accuracy and hence was assigned the highest weightage. Based on this, MJ and SLA were ranked the highest and lowest, respectively.