International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 05 | May 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1352 Experimental assessment of Repeatability of Openware 3D Printer Pooja Padyal 1 , Dr. A. Mulay 2 , Dr. M.R. Dhanvijay 3 1 M.Tech. in Production Engineering, College of Engineering Pune, MH, India 2 Associate Professor, Department of Production & Industrial Management, College of Engineering Pune, MH, India 3 Assistant Professor, Department of Production & Industrial Management, College of Engineering Pune, MH, India ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - As promising manufacturing process, additive manufacturing technique is looked upon. Apart from other additive techniques, the fused deposition modeling invented by Stratasys is widely practiced because of its simple design and fabrication. Apart from the prevalent advantages, it also has limitation such as dimensional accuracy, repeatability, and surface finish. This paper was aimed at evaluation of repeatability of an Openware desktop 3D printer in terms of standard deviation obtained from a set of samples. At first, an optimum set of parameters (layer height, speed of deposition and fill density) were evaluated using Taguchi method of DOE. Then with optimum parameters sample specimens were printed. On this sample set, statistical analysis was performed. From the statistical analysis of data about dimensional deviation, the repeatability of printer was interpreted. Keywords: Additive manufacturing, Fused deposition modeling printer, Taguchi Analysis, Repeatability, Anderson-Darling Test 1. INTRODUCTION Additive manufacturing (AM) is defined as “the process of joining materials to make objects from three-dimensional (3D) model data, usually layer upon layer, as opposed to subtractive manufacturing methodologies”, as per ASTM F42. Continuously large amount of research has taken place in additive manufacturing. The application of AM is substantial in aerospace, automotive, biomedical and conventional prototyping. AM can be classified according to the type of material used (filament, sheets, liquid polymer), the technologies incorporated (FDM, SLA, VAT, and photo polymerization etc.). AM processes have an edge over the conventional subtractive manufacturing process in such way that it can make complex geometrical parts modeled in any CAD software. Fused deposition modeling (FDM) is one of the AM techniques [1]. In FDM, a thin filament of material is fed into a heated extruder machine, and the molten material is deposited on a heated bed layer by layer onto each other (Figure 1). With advancement in FDM process it has evolved over the period. Due to which it is showing its application in manufacturing sectors also, than just prototyping. FDM is prevalent among organization in various businesses, from automobile to consumer product fabrication [2]. Being simpler in design and construction it has process specific limitations such as availability of printable materials, existing CAD system, STL file size and its management, low-volume production, financial overheads, surface quality, dimensional accuracy and repeatability[3]. Its use further in manufacturing is limited due to such restrictions. For extensive use of 3D printer, knowledge of its capabilities should be more researched and these limitations needs to be minimized. Fig - 1: Fused deposition modeling process 2. LITERATURE REVIEW With number of printing parameters involving in the process, there has been need to find out the significant and optimum parameters for better surface roughness, accuracy, or other physical properties of printed parts. Concerning this, a noticeable amount of study has taken place to evaluate qualitative output of 3D printer. R. Anitha[4], found out the effect of parameters such as layer thickness, road width and speed of deposition on the printed parts using Taguchi technique. Further using ANOVA observed that layer thickness was significant factor among other two. This paper also asserted using Taguchi for design of experiment (DOE). C. K. Basavaraj[5], Che Chung Wang[6], Garrett W. Melenka[7] investigated on various physical properties such as dimensional accuracy, surface roughness or material properties using different techniques such as GRA analysis, TOPSIS, and CLT. Basavraj tried on achieving maximum tensile strength, optimum dimensional accuracy, and manufacturing time using Taguchi. For DOE, parameters such as layer thickness, part orientation angle and shell thickness were considered. This research found out the optimum parameters and also their significant level by ANOVA. Wang integrated Taguchi method with Gray relational analysis and verified using TOPSIS. Optimum levels of parameters such as layer height, deposition style, support style, deposition orientation in X and Z direction, and build location were evaluated by Taguchi. At first, Malenka