Comparison of ranking models to evaluate desktop 3D printers in a growing market

While additive manufacturing (AM), commonly known as 3D printing, has been in existence commercially for similar to 30 years, desktop 3D printers are a relatively new and rapidly growing market segment. Both well-established AM companies and an increasing number of new enterprises are designing and retailing desktop systems of various sizes, capabilities, and prices. With the abundance of desktop systems now on the market, a consumer may benefit from determining which system best serves their needs. This research highlights differences amongst 45 desktop 3D printers and suggests a method by which to evaluate such differences. For this, a standard part consisting of various geometric features was designed and printed using each system. An updated version of a previously developed quantitative ranking model was utilized to rate the build precision of each system as well as other features, including build volume, size, cost, weight, and layer resolution. In addition, the research team observed part aesthetics and quantified mechanical properties. The criteria evaluated in this ranking model may be modified by each user, to extend this methodology to other desktop AM systems, including professional-grade machines. The results from the model presented in this research were compared with other commonly used ranking methods to help evaluate each technique. These included a simple ascending order rank based model adjusted for ties (1-best and 45-worst), a percentile value based model evaluating the factor contribution values presented in this paper; and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) which evaluated separation measures from ideal and worst solutions. As expected, the comparisons demonstrated that each model had slightly different rankings as compared to the model presented in this paper, with some outliers. Consequently, it was observed that percentile value based models (such as one presented in this paper) provide rankings different from separation value based models (from ideal and worst solutions) such as TOPSIS.