工艺规划过程中，工艺知识具有复杂性、模糊性、离散性，不同加工方法的工序

In the process of process planning, process knowledge has complexity, fuzziness, and discreteness. There are also fuzzy constraint relationships among the processes of different processing methods, which are suitable for analysis and processing by the related theories of fuzzy mathematics. As an important promotion and supplement of fuzzy set theory, intuitionistic fuzzy set comprehensively considers the three aspects of the element’s membership degree, non-membership degree and hesitancy degree [15], and it is more flexible and more flexible when analyzing the problems of fuzziness and uncertainty. objectivity. The ant colony algorithm is a simulation optimization algorithm derived from the foraging behavior of ants. It has strong local search capabilities, positive information feedback, distributed computing and other characteristics. It has significant advantages in solving optimization problems. Through adaptive improvement, Changing the pheromone update strategy can quickly jump out of local convergence. Based on this, this paper proposes a multi-process route optimization method based on intuitionistic fuzzy information fusion and adaptive improved ant colony algorithm. Intuitionistic fuzzy information is used to construct the fuzzy constraint relationship between processing elements and processes, and then the processing methods of each process are determined, and then the sorting efficiency of the processing methods is optimized by adaptively improving the ant colony algorithm, effectively avoiding the local optimal solution and improving the convergence speed. Finally, the effectiveness and usability of this method are verified through engineering examples.

In the process of process planning, the process knowledge is complex, fuzzy and discrete, and there is also a fuzzy constraint relationship between the processes of different processing methods, which is suitable for analysis and treatment using the relevant theories of fuzzy mathematics. As an important extension and supplement of fuzzy set theory, the intuition fuzzy set considers the information of element affiliation, non-belonging and hesitancy, and is more flexible and objective in analyzing fuzzy and uncertain problems. Ant colony algorithm is a simulation optimization algorithm derived from ant foraging behavior, which has strong local search ability, positive information feedback, distributed computing and other characteristics, which shows significant advantages in solving optimization problems, and can quickly jump out of local convergence by adaptive improvement and changing the informationin update strategy. Based on this, this paper proposes a multi-route optimization method for intuition fuzzy information fusion adaptive improvement of ant colony algorithm. Using intuitive fuzzy information to construct the fuzzy constraint relationship between processing meta and process, and then determine the processing method of each process, and then optimize the sorting efficiency of the processing method by adaptively improving the ant colony algorithm, effectively avoid falling into the local optimal solution, and improve the convergence speed. Finally, the validity and availability of this method are verified by engineering examples.

工艺规划过程中，工艺知识具有复杂性、模糊性、离散性，不同加工方法的工序间也存在着模糊约束关系，适合采用模糊数学的相关理论来分析处理。直觉模糊集作为模糊集理论的重要推广和补充，综合考虑元素的隶属度、非隶属度和犹豫度3个方面的信息[15]，在分析模糊性、不确定性问题时更具灵活性和客观性。蚁群算法是一种源于蚂蚁觅食行为的模拟优化算法，具有较强的局部搜索能力，信息正反馈、分布式计算等特征，在求解优化问题上体现出显著优势，通过自适应改进，改变信息素更新策略，可快速跳出局部收敛。基于此，本文提出直觉模糊信息融合自适应改进蚁群算法的多工艺路线优化方法。采用直觉模糊信息构建加工元及工序间的模糊约束关系，进而确定各工序加工方法，然后通过自适应改进蚁群算法优化加工方法的排序效率，有效避免陷入局部最优解，提高了收敛速度。最后通过工程实例验证了本方法的有效性和可用性。<br>