Optimizing multiple process parameters in fused deposition modeling with particle swarm optimization

Fused deposition modeling (FDM) is one of the most widely used additive manufacturing processes to produce prototypes as well as functional parts from thermoplastics. However, the applications of the FDM process are still limited due to several drawbacks, such as poor surface quality, poor mechanical properties, or high built time. It has been deemed that the part characteristics and build time can be improved by determining an optimum combination of process parameters. To optimize multiple process parameters in FDM, this paper employs a multi-objective particle swarm optimization based on the data collected from the experimental study. Four (04) process parameters, namely layer thickness, build orientation, infill density, and extrusion temperature are optimized to achieve higher compressive strength and lower build time. The optimization results provide information on the combined impacts of the four process parameters on compressive strength and build time. This information can aid decision makers with better judgment when dealing with multiple conflicting objectives.

[1]  Tim Caffrey,et al.  Wohlers report 2013 : additive manufacturing and 3D printing state of the industry : annual worldwide progress report , 2013 .

[2]  Ventola Cl.  Medical Applications for 3D Printing: Current and Projected Uses. , 2014 .

[3]  E. García-Plaza,et al.  Additive manufacturing of PLA structures using fused deposition modelling: Effect of process parameters on mechanical properties and their optimal selection , 2017 .

[4]  Xingming Xiao,et al.  Process parameter optimization for fused deposition modeling using response surface methodology combined with fuzzy inference system , 2014 .

[5]  T. K. Kundra,et al.  Multi-objective optimisation of fused deposition modelling process parameters using RSM and fuzzy logic for build time and support material , 2018 .

[6]  Ruth Jill Urbanic,et al.  An Optimization Approach for Components Built by Fused Deposition Modeling with Parametric Internal Structures , 2014 .

[7]  Stephen Oluwashola Akande.  Dimensional Accuracy and Surface Finish Optimization of Fused Deposition Modelling Parts using Desirability Function Analysis , 2015 .

[8]  D. C. Howell.  Statistical Methods for Psychology , 1987 .

[9]  Omar Ahmed Mohamed,et al.  Mathematical modeling and FDM process parameters optimization using response surface methodology based on Q-optimal design , 2016 .

[10]  Howon Lee,et al.  Improving Surface Roughness of Additively Manufactured Parts Using a Photopolymerization Model and Multi-Objective Particle Swarm Optimization , 2019, Applied Sciences.

[11]  A. K. Sood,et al.  Improving dimensional accuracy of Fused Deposition Modelling processed part using grey Taguchi method , 2009 .

[12]  M. Deaton,et al.  Response Surfaces: Designs and Analyses , 1989 .

[13]  Zhen Hu,et al.  Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities , 2017, The International Journal of Advanced Manufacturing Technology.

[14]  Bibhuti Bhusan Biswal,et al.  Comparative Evaluation of Optimization Algorithms at Training of Genetic Programming for Tensile Strength Prediction of FDM Processed Part , 2014 .

[15]  B. H. Lee,et al.  Optimization of rapid prototyping parameters for production of flexible ABS object , 2005 .

[16]  Soniya Lalwani,et al.  A comprehensive survey: Applications of multi-objective particle swarm optimization (MOPSO) algorithm , 2013 .

[17]  Gideon Levy,et al.  RAPID MANUFACTURING AND RAPID TOOLING WITH LAYER MANUFACTURING (LM) TECHNOLOGIES, STATE OF THE ART AND FUTURE PERSPECTIVES , 2003 .

[18]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[19]  Ali Siadat,et al.  Impact of fused deposition modeling (FDM) process parameters on strength of built parts using Taguchi’s design of experiments , 2018, The International Journal of Advanced Manufacturing Technology.

[20]  Yuan Hu,et al.  Mechanical property parametric appraisal of fused deposition modeling parts based on the gray Taguchi method , 2017 .

[21]  T. Nancharaiah,et al.  An experimental investigation on surface quality and dimensional accuracy of FDM components , 2010 .

[22]  Anoop Kumar Sood,et al.  Experimental investigation and empirical modelling of FDM process for compressive strength improvement , 2012 .

[23]  K. Leong,et al.  Investigation of the mechanical properties and porosity relationships in fused deposition modelling‐fabricated porous structures , 2006 .

[24]  R. Venkata Rao,et al.  Optimization of fused deposition modeling process using teaching-learning-based optimization algorithm , 2016 .

[25]  Godfrey C. Onwubolu,et al.  Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE) , 2014 .

[26]  B. J. Alvarez,et al.  Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm , 2013 .

[27]  R. Keshavamurthy,et al.  Studies on Parametric Optimization for Fused Deposition Modelling Process , 2015 .

[28]  A. K. Sood,et al.  Parametric appraisal of mechanical property of fused deposition modelling processed parts , 2010 .

[29]  Ala Qattawi,et al.  Experimental Optimization of Fused Deposition Modelling Processing Parameters: A Design-for-Manufacturing Approach , 2017 .

[30]  R. K. Ohdar,et al.  Parametric appraisal of fused deposition modelling process using the grey Taguchi method , 2010 .

[31]  Srinivasa Prakash Regalla,et al.  Multi-objective optimisation of strength and volumetric shrinkage of FDM parts , 2014 .

[32]  T. Nancharaiah,et al.  Optimization of Process Parameters in FDM Process Using Design of Experiments , 2011 .

[33]  Todd Letcher,et al.  Experimental Study of Mechanical Properties of Additively Manufactured ABS Plastic as a Function of Layer Parameters , 2015 .

[34]  Ali P. Gordon,et al.  Mechanical Property Optimization of FDM PLA in Shear with Multiple Objectives , 2015, JOM.

[35]  Eko Pujiyanto,et al.  Simultaneous Optimization of TensileStrength, Energy Consumption and Processing Time on FDM Process Using Taguchi and PCR-TOPSIS , 2018, 2018 4th International Conference on Science and Technology (ICST).

[36]  P. M. Pandey,et al.  Optimal part deposition orientation in FDM by using a multicriteria genetic algorithm , 2004 .