ABSTRACT Every production process consists of a large number of dependent and independent variables, which substantially influence the quality of the machined parts. Due to the large impact of process variabilities, it is difficult to design optimal models for the machining processes. Mathematical or numerical models for production processes are resource driven, which are not cost effective approaches in terms of computation and economical production. In this paper, a new artificial neural network (ANN) based predictive model is introduced, which exploits particle swarm optimization (PSO) algorithm to minimize the root mean square errors (RMSE) for the network training. This approach can effectively obtain an optimized predictive model that can calculate precise output responses for the production processes. In order to verify the proposed approach, two case studies are considered from literature and shown to produce significant improvements. Furthermore, the proposed model is validated on abrasive water jet machining (AWJM) with industrial garnet abrasives and optimal machining conditions have been obtained with optimized responses, which are substantially improved while compared with gray relational analysis (GRA).

中文翻译：

---

CFNN-PSO：加工过程通用参数化设计的迭代预测模型

摘要 每个生产过程都由大量因变量和自变量组成，这些变量对加工零件的质量产生重大影响。由于过程变量的影响很大，很难为加工过程设计最佳模型。生产过程的数学或数值模型是资源驱动的，在计算和经济生产方面不是成本有效的方法。在本文中，介绍了一种新的基于人工神经网络 (ANN) 的预测模型，该模型利用粒子群优化 (PSO) 算法来最小化网络训练的均方根误差 (RMSE)。这种方法可以有效地获得优化的预测模型，可以计算出生产过程的精确输出响应。为了验证所提出的方法，从文献中考虑了两个案例研究，并表明它们产生了显着的改进。此外，所提出的模型在工业石榴石磨料磨料水射流加工（AWJM）上进行了验证，获得了优化响应的最佳加工条件，与灰色关联分析（GRA）相比有显着改善。