Recently, the adaptive network-based fuzzy inference system (ANFIS) has been used extensively in modeling of manufacturing processes to save both optimization time and manufacturing costs. ANFIS is a powerful iterative tool for optimizing non-linear and multivariable manufacturing operations. In the present study, ANFIS is used to predict the optimum manufacturing parameters in selective laser sintering (SLS) of cement-filled polyamide 12 (PA12) composite. For this purpose, a set of cement-filled PA12 test specimens is manufactured by SLS technique with 8 different values of laser power (4.5–8 Watt) and 8 different weight fractions of white cement (5 %–40 %). Mechanical characterization of cement-filled PA12 is carried out to evaluate the ultimate tensile strength (UTS), compressive strength, and flexural properties. The experimental data are then divided into two groups; one group for training the ANFIS model and the other group for checking the validity of the identified model. The built ANFIS model was validated experimentally and comparison with experimental results revealed mean relative errors of 2.92 %, 3.84 %, 4.75 %, and 3.31 % in the predictions of UTS, compressive strength, flexural modulus, and flexural yield strength, respectively.

最近，基于自适应网络的模糊推理系统（ANFIS）已广泛用于制造过程的建模，以节省优化时间和制造成本。ANFIS是用于优化非线性和多变量制造操作的强大迭代工具。在本研究中，ANFIS用于预测水泥填充聚酰胺12（PA12）复合材料的选择性激光烧结（SLS）中的最佳制造参数。为此，通过SLS技术制造了一组水泥填充的PA12测试样品，其具有8种不同的激光功率值（4.5-8瓦）和8种不同的重量分数的白水泥（5％至40％）。对水泥填充的PA12进行了机械表征，以评估极限抗拉强度（UTS），抗压强度和挠曲性能。然后将实验数据分为两组：一组用于训练ANFIS模型，另一组用于检查所识别模型的有效性。建立的ANFIS模型经过实验验证，与实验结果的比较表明，在预测UTS，抗压强度，弯曲模量和弯曲屈服强度方面，平均相对误差分别为2.92％，3.84％，4.75％和3.31％。