## 4 Conclusions  
We present a systematic approach that provides the optimal printing process parameters for high quality AM parts using a computational model and the particle swarm optimization algorithm. A computational model representing the photopolymerization kinetics involved in PuSL process was \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Objectives** & **Point C** & **Point D** & **Point E** & **Point F** & **Non-Opt. 1** & **Non-Opt. 2** & **Non-Opt. 3** \\ \hline Total printing time (s) & 300 & **48.33** & 300 & **48.33** & 300 & 210.95 & 48.33 \\ Surface profile RMSE (\(\mu\)m) & **2.13** & 19.25 & **5.48** & 18.28 & 2.47 & 3.57 & 22.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Objectives comparison between optimized and non-optimized printing conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Upper/Lower** & **Point C** & **Point D** & **Point E (Upper** & **Point F** \\ \hline
**Symbol** & **Name** & **Bounds** & **(Lower Side)** & **(Lower Side)** & **Side)** & **(Upper Side)** \\ \hline
[PI] & Photoinitiator & 24.14-72.4 mol/m\({}^{3}\) & 53.89 mol/m\({}^{3}\) & 72.4 mol/m\({}^{3}\) & 60.56 mol/m\({}^{3}\) & 64.30 mol/m\({}^{3}\) \\  & Photo-absorber & 2.03–6.1 mol/m\({}^{3}\) & 6.10 mol/m\({}^{3}\) & 2.03 mol/m\({}^{3}\) & 6.10 mol/m\({}^{3}\) & 3.91 mol/m\({}^{3}\) \\ LT & Layer thickness & 20–120 \(\mu\)m & 20 \(\mu\)m & 120 \(\mu\)m & 20 \(\mu\)m & 120 \(\mu\)m \\ CT & Curing time & 1–4 s & 1.0 s & 4.0 s & 1.0 s & 1.0 s \\ \hline \hline \end{tabular}
\end{table} 
Table 9: List of optimizing parameters. implemented and process constants were carefully calibrated by using experimental data obtained from a custom-built P\(\mu\)SL system. [PERSON]'s Orthogonal Array (OA) method was used to select the parameters that affect the surface quality of AM parts significantly. Four process parameters (PI and PA concentrations, layer thickness, and curing time) selected from [PERSON]'s OA were used as controlling parameters for optimization algorithm. A meta-heuristic population-based optimization algorithm, particle swarm algorithm for multiple objectives, called multi-objective particle swarm optimization (MOPSO), was used to determine optimal printing process parameters with given geometry of AM parts. Two struts with different printing angles (vertical and 60\({}^{\circ}\)) were considered and the optimized printing process parameters were determined for each case. The Pareto optimal set in each printing angle was obtained so that it can be used as a guideline when the printing process parameters need to be set. The result showed that the proposed optimization framework reduces 50% of printing time while keeping the surface quality equal for the vertical strut, and increases 18% of surface quality of the angled strut even in the fastest printing speed, compared to the samples produced by using non-optimized parameters. This framework consisting of the computational model for photopolymerization, process parameter selection by [PERSON]'s method, and MOPSO for optimization printing process parameters resulted in significant improvement in the quality of AM parts while keeping the printing time minimum. By changing the objective functions of MOPSO, presented approach can also be used for optimizing various quality measures, such as minimizing printing cost and maximizing mechanical strength. Our proposed optimization technique can be easily applied to other photocurable polymers by incorporating material-specific photocuring kinetics parameters for a given polymer in the photopolymerization process modeling. In addition, increasing dimensionality of search space in MOPSO is straightforward without modification of the algorithm, so multiple printing parameters as well as external inputs can be easily increased by adding an additional dimension in the search space.