Selective inhibition sintering (SIS) is an evolving additive manufacturing (AM) technology that enables to build functional parts from various polymers, metals and ceramics materials in reasonable cost and time. SIS parts are significantly influenced through their process parameters. Further, due to the brittleness and anisotropic characteristics of functional parts, optimization of SIS process variables is necessary to improve the compressive loading capacity. In this study, the effect of five main SIS process parameters such as infrared (IR) heater power, bed temperature, IR heater feedrate, layer thickness and IR heater scanning distance on the compressive strength (CS) of selective inhibition sintered ultra-high molecular weight poly ethylene (UHMWPE) test specimens was examined. Firstly, experimentations were conducted based on Box–Behnken design (BBD) scheme by considering three levels for each process parameters. Subsequently, the CS of SIS test specimens were predicted using response surface methodology (RSM) based on developed quadratic regression model. Finally, the predicted CS using RSM model were compared with that of artificial neural network (ANN) model. Further, the influence of SIS parameters on CS was studied through performing analysis of variance (ANOVA) and sensitivity analysis. The results show that the power and feed rate of the infrared heater have a significant influence on CS. Furthermore, an optimization was performed using the desirability approach to identify the optimal levels of the SIS process parameters and was validated through experimental result.

选择性抑制烧结（SIS）是一项不断发展的增材制造（AM）技术，可在合理的成本和时间下用各种聚合物，金属和陶瓷材料制造功能部件。SIS零件受其工艺参数的影响很大。此外，由于功能部件的脆性和各向异性特性，必须对SIS工艺变量进行优化以提高压缩负载能力。在这项研究中，五个主要的SIS工艺参数（如红外（IR）加热器功率，床温，IR加热器进料速率，层厚度和IR加热器扫描距离）对选择性抑制烧结超高分子材料的抗压强度（CS）的影响重量聚乙烯（UHMWPE）测试样品进行了检查。首先，实验是基于Box–Behnken设计（BBD）方案进行的，其中考虑了每个过程参数的三个级别。随后，基于已建立的二次回归模型，使用响应面方法（RSM）来预测SIS测试样品的CS。最后，将使用RSM模型预测的CS与人工神经网络（ANN）模型进行了比较。此外，通过进行方差分析（ANOVA）和敏感性分析，研究了SIS参数对CS的影响。结果表明，红外加热器的功率和进给速度对CS有很大影响。此外，使用期望方法进行了优化，以确定SIS工艺参数的最佳水平，并通过实验结果进行了验证。