A method of gear hobbing

Abstract

The invention discloses a gear hobbing method which is characterized in that during hobbing, optimization decision on gear hobbing process parameters comprises the following specific steps: (1) building a gear hobbing process ontology base; (2) expressing a gear hobbing process parameter decision target space; and (3) performing comprehensive optimization decision on the gear hobbing process parameters. The method disclosed by the invention has the advantages that by using the gear hobbing process domain ontology base stored in a database, the method can realize sharing and reuse of the knowledge about the domain of the gear hobbing, and not only can consider the gear hobbing process parameters as a system but also can solve new optimization decision problems of the gear hobbing process parameters by combining case-based reasoning with analytic hierarchy process during use.

Description

technical field [0001] The invention relates to gear processing technology, in particular to a processing method for optimizing process parameters during gear hobbing processing. Background technique [0002] Reasonable selection and optimal decision-making of hobbing process parameters are of great significance for improving the production quality and processing efficiency of gear hobbing. At present, the method of formulating process parameters widely used is empirical method or test method: for mass production, craftsmen often complete the formulation of process parameters according to various process manuals and their own accumulated processing experience; The process experience of the personnel determines the feasible process parameters in advance, and combines a certain number of trial cutting to obtain the process parameters that can meet the processing requirements. However, the existing decision-making methods for process parameters have the following deficiencies:...

AI Technical Summary

Problems solved by technology

However, the existing decision-making methods for process parameters have the following deficiencies: ① Lack of unified expression of knowledge of gear hobbing process; ② Long cycle of gear hobbing process; ③ No consideration of processing quality, processing time, processing cost, resource consumption, environment Influence and other factors

When various optimization algorithms based on expert system deal with the optimization decision-making problem of gear hobbing process parameters, the optimization of process parameters is automatically completed through algorithms such as genetic algorithm and artificial neural network. However, due to the unpredictability of the algorithm, the decision-making results are unstable and unpredictable. Reuse, lack of unified representation of process knowledge

The case-based reasoning method starts from the perspective of historical processing instances, and solves new problems through steps such as retrieval, reuse, modification, and retention. However, the instance similarity is used as the only index, and it lacks consideration of the processing effect of the process instance, so that the process plan that best matches the instance The processing effect may not be ideal when the adopted process parameters are applied to the process problem to be decided

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