基于模糊规则推理的注塑成型缺陷诊断及工艺智能优化

针对注塑成型缺陷成因求解的模糊性与不确定性，综合运用规则推理、模糊推理等人工智能技术，完成了注塑成型缺陷诊断及工艺优化系统的总体结构设计。通过采用规则推理和Mamdani模糊推理分别进行了注塑制品缺陷成因判定和工艺参数智能优化，详细阐述了模糊推理用于优化注塑成型工艺参数的整个过程。基于上述理论及VisualProlog应用平台，采用人工智能系统语言Prolog开发了注塑成型缺陷诊断及工艺优化专家系统，并给出应用实例。结果表明，此系统具备较好的注塑制品缺陷诊断及工艺优化能力，有一定的推广应用前景。

Injection defect diagnosis and process intellectual optimization based on fuzzy rule inference

According to the ambiguity and uncertainty of solution for injection defect cause, rule-based reasoning, fuzzy reasoning and other artificial intelligence techniques were utilized together to complete the overall structural design of injection defect diagnosis and process optimization system. Through the use of rule-based reasoning, injection defect cause could be determined, and process parameters were intelligent optimized based on fuzzy reasoning. The entire process of optimizing injection molding process parameters with fuzzy reasoning was elaborated. Based on the theory above and application platform of Visual Prolog, the expert system for the injection defect diagnosis and process optimization was developed with the artificial intelligence system language Prolog, and application example was given. The result shows that this system is with the ability to diagnose the injection defect and optimize the process, and possesses better promotion prospects as well.