Multi-objective Optimization Strategies

In this chapter, multi-objective optimization as a strategy for quality production of parts through fused deposition modelling is presented. Various techniques used in undertaking the multi-objective optimization process are described based on case studies from the literature and the authors’ data. The general algorithms for multi-objective optimization of the FDM process are described. The most significant objectives of the various optimization cases are identified and described in relation to the quality of the fused deposition modelling of parts. The main objectives for optimizing fused deposition process are (i) to increase the rate of production, (ii) to reduce material wastage and utilize as minimum material as possible, (iii) save on the cost of power consumption during printing and (iv) achieve the highest quality of FDM parts.

[1]  S. F. P. Saramago,et al.  Multiobjective optimization techniques applied to engineering problems , 2010 .

[2]  Pascal Lafon,et al.  Multi-Objective Optimization of Additive Manufacturing Process , 2018 .

[3]  Robert Giegerich,et al.  Pareto optimization in algebraic dynamic programming , 2015, Algorithms for Molecular Biology.

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

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

[6]  David Hoffman,et al.  Optimizing multiple process parameters in fused deposition modeling with particle swarm optimization , 2020, International Journal on Interactive Design and Manufacturing (IJIDeM).

[7]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[8]  Yanan Zhang,et al.  Multi-objective optimization of process parameters for biological 3D printing composite forming based on SNR and grey correlation degree , 2015 .

[9]  Alexander Bockmayr,et al.  Double and multiple knockout simulations for genome-scale metabolic network reconstructions , 2015, Algorithms for Molecular Biology.

[10]  Ana I. Pereira,et al.  Improving additive manufacturing performance by build orientation optimization , 2020, The International Journal of Advanced Manufacturing Technology.

[11]  Nyoman Gunantara,et al.  A review of multi-objective optimization: Methods and its applications , 2018 .

[12]  Hasan Koyuncu,et al.  ScPSO-Based Multithresholding Modalities for Suspicious Region Detection on Mammograms , 2018 .