Laser beam machining (LBM) as an efficient tool for material removal has attracted the attention of manufacturing industries. Accordingly, there is a great motivation in the modeling and optimization of this non-conventional machining process. In this paper, the focus is on the most common LBM process, including cutting, grooving, turning, milling, and drilling. The development of an accurate model between the input and output variables of the LBM process is difficult and complex due to the non-linear behavior of the process under various conditions. In the case of LBM, the input variables are system, material, and process parameters, and the output variables are the quality characteristics of laser machined workpiece, including geometry characteristics, metallurgical characteristics, surface roughness, and material removal rate (MRR). Recently, among computational methods, artificial intelligence (AI) has been studied by scientists as a pioneer in the field of modeling and optimizing quality features of LBM. AI techniques utilize the empirical findings and existing knowledge for modeling, optimization, monitoring, and controlling of the LBM process. In this paper, the applications of AI techniques, including artificial neural network (ANN), fuzzy logic (FL), metaheuristic optimization algorithms, and hybrid approaches in modeling and optimization of the quality characteristics of LBM are reviewed. It is shown that AI techniques are successfully capable of predicting and improving the features of the laser machined workpiece. It is also demonstrated that AI can be used as a powerful tool to obtain a comprehensive model and optimal setting parameters of LBM. In addition, according to the potential and capability of AI techniques, several ideas have been offered for future studies.

激光束加工（LBM）作为一种有效的材料去除工具已经引起了制造业的关注。因此，在这种非常规加工过程的建模和优化中有很大的动力。在本文中，重点是最常见的LBM工艺，包括切削，切槽，车削，铣削和钻孔。由于在各种条件下过程的非线性行为，在LBM过程的输入和输出变量之间建立精确的模型是困难而复杂的。对于LBM，输入变量是系统，材料和工艺参数，输出变量是激光加工工件的质量特征，包括几何特征，冶金特征，表面粗糙度和材料去除率（MRR）。最近，在计算方法中，科学家已经研究了人工智能（AI），这是对LBM的质量特征进行建模和优化的先驱。人工智能技术利用经验发现和现有知识对LBM过程进行建模，优化，监视和控制。本文综述了人工智能技术的应用，包括人工神经网络（ANN），模糊逻辑（FL），元启发式优化算法以及混合方法在LBM质量特征建模和优化中的应用。结果表明，人工智能技术能够成功地预测和改善激光加工工件的特征。还证明了AI可以用作获得全面模型和LBM最佳设置参数的强大工具。此外，