Optimization of process planning for reducing material waste in extrusion based additive manufacturing

Abstract Among the available additive manufacturing technologies, extrusion based 3D printing (otherwise known as fused deposition modelling or fused filament fabrication) is among the most commonly used due to low cost and relative simplicity. However, such printers still suffer from redundant support material waste (both interior and exterior) when printing large-volume solid objects or objects with overhangs. The support material can also be a significant cause of long part production time and higher energy consumption during manufacture. Hence, we propose a new support generation strategy considering both interior and exterior support via AM process planning to reduce the total amount of material consumption, production time and energy consumed for manufacturing an object. Print path and print orientation are both considered as significant factors and are both optimized for achieving the lowest consumption of material. The areas to be filled on each layer are determined according to the printable threshold overhang angle (PTOA) and the longest printable bridge length (LPBL). The characteristics of LPBL and PTOA are fully considered for saving more material. Several tests are used to verify the proposed strategy and the results show that this strategy can considerably reduce material waste, production time and energy consumed compared with conventional strategies, enabling AM to be a more environmentally friendly and sustainable manufacturing technique.

[1]  Ray Y. Zhong,et al.  Investigation of printable threshold overhang angle in extrusion-based additive manufacturing for reducing support waste , 2018, Int. J. Comput. Integr. Manuf..

[2]  Johann Sienz,et al.  Part orientation optimisation for the additive layer manufacture of metal components , 2016 .

[3]  Di Wang,et al.  Surface quality of the curved overhanging structure manufactured from 316-L stainless steel by SLM , 2016 .

[4]  Jikai Liu,et al.  Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint , 2017, Comput. Aided Des..

[5]  Xiao Li,et al.  Analysis and prediction of printable bridge length in fused deposition modelling based on back propagation neural network , 2019, Virtual and Physical Prototyping.

[6]  Kunwoo Lee,et al.  Block-based inner support structure generation algorithm for 3D printing using fused deposition modeling , 2017 .

[7]  Kunwoo Lee,et al.  Extended block based infill generation , 2017 .

[8]  Jingchao Jiang,et al.  Effect of support on printed properties in fused deposition modelling processes , 2019, Virtual and Physical Prototyping.

[9]  Charlie C. L. Wang,et al.  Current and future trends in topology optimization for additive manufacturing , 2018 .

[10]  Yaoyao Fiona Zhao,et al.  A framework to reduce product environmental impact through design optimization for additive manufacturing , 2016 .

[11]  Sam Anand,et al.  Selection of build orientation for optimal support structures and minimum part errors in additive manufacturing , 2017 .

[12]  Jean-Pierre Kruth,et al.  Optimization of Scan Strategies in Selective Laser Melting of Aluminum Parts With Downfacing Areas , 2014 .

[13]  Mattia Bellotti,et al.  Warpage of FDM parts , 2018 .

[14]  Kelsey Phelan,et al.  Mechanical Strength of 3-D Printed Filaments , 2016, 2016 32nd Southern Biomedical Engineering Conference (SBEC).

[15]  Xun Xu,et al.  Optimisation of multi-part production in additive manufacturing for reducing support waste , 2019, Virtual and Physical Prototyping.

[16]  Rafiq Ahmad,et al.  Light-weight shape and topology optimization with hybrid deposition path planning for FDM parts , 2018 .

[17]  Radovan Kovacevic,et al.  Process planning for 8-axis robotized laser-based direct metal deposition system , 2017 .

[18]  Jayaprakash Sharma Panchagnula,et al.  Manufacture of complex thin-walled metallic objects using weld-deposition based additive manufacturing , 2018 .

[19]  Cassandra Telenko,et al.  Material and energy loss due to human and machine error in commercial FDM printers , 2017 .

[20]  Jack Howarth,et al.  A design of experiments approach for the optimisation of energy and waste during the production of parts manufactured by 3D printing , 2016 .

[21]  Xun Xu,et al.  A weighted rough set based fuzzy axiomatic design approach for the selection of AM processes , 2017 .

[22]  Georges Fadel,et al.  Expert system-based selection of the preferred direction of build for rapid prototyping processes , 1995, J. Intell. Manuf..

[23]  Xun Xu,et al.  A benchmarking part for evaluating and comparing support structures of additive manufacturing , 2018 .

[24]  Di Liu,et al.  Research and implementation of a non-supporting 3D printing method based on 5-axis dynamic slice algorithm , 2019, Robotics and Computer-Integrated Manufacturing.

[25]  Daniel Cohen-Or,et al.  Build-to-last , 2014, ACM Trans. Graph..

[26]  Duc Truong Pham,et al.  Part Orientation in Stereolithography , 1999 .

[27]  JiangJingchao,et al.  Support Optimization for Flat Features via Path Planning in Additive Manufacturing , 2019, 3D Printing and Additive Manufacturing.

[28]  Jianzhong Fu,et al.  Inclined layer printing for fused deposition modeling without assisted supporting structure , 2018, Robotics and Computer-Integrated Manufacturing.

[29]  Xun Xu,et al.  Support Structures for Additive Manufacturing: A Review , 2018, Journal of Manufacturing and Materials Processing.

[30]  Yong He,et al.  Optimization of process planning for reducing material consumption in additive manufacturing , 2017 .