Intelligent two-process processing scheduling method based on fault RGV

Abstract

The invention discloses an intelligent two-process processing scheduling method based on a fault RGV. The method comprises the following steps: taking time length data of a single-time two-process product processed by an RGV system as system parameters; for an RGV intelligent machining system with symmetrical machining four groups of tool bits on each of two sides of the single track, selecting minimum of waiting time of idile time of a first procedure of machining bit CNC (product machining) and a second procedure of machining bit CNC (product machining) as a major target; taking into consideration the object number of machined objects, machining time and CNCcodes of machining bits for the system, establishing a biological evolution-dynamic planning prediction model so that prediction analysis is conducted on batch product production and machining; adopting a random increment learning mechanism for solving the optimal one-procedure and two-procedure distribution scheme of the machining tool bit CNC. The method provides reference to an enterprise to make a work plan.

Description

technical field [0001] The invention relates to the technical field of RGV, in particular to a fault-based RGV intelligent two-process processing scheduling method. Background technique [0002] With the development of automation control technology, the single-track RGV intelligent system has been widely used in various product deep processing industries instead of manual labor due to its advantages of low price, convenience and flexibility. However, due to different industry application requirements, a single-group single-track RGV intelligent system is often required to process products in two processes. The two-process processing is far more complicated than the one-process processing process. It is necessary to reasonably allocate the CNC machining tool heads, lack of a reasonable allocation plan, and the utilization efficiency of the RGV intelligent system is low. This has always been a small and medium-sized enterprise that does not have high requirements for RGV intel...

AI Technical Summary

Problems solved by technology

The two-process processing is far more complicated than the one-process processing. It is necessary to reasonably allocate the CNC machining tool heads. There is a lack of a reasonable allocation plan, and the utilization efficiency of the RGV intelligent system is low. Difficulties, especially for those small and medium-sized enterprises that are short of funds and aging outdated equipment, the RGV intelligent system will inevitably experience frequent failures and shutdowns, and more optimized decision-making solutions are needed

Some models that combine genetic algorithms or simulated annealing methods with network models such as directed graphs and Petri nets can evolve the processing of items into certain network changes through network construction, and at the same time seek network optimization through intelligent algorithms, which can achieve the task The optimal arrangement of the network model can well avoid the established artificial model, but the model combined with the intelligent algorithm and the network model requires a large amount of data training, the solution is cumbersome, and the practicability for small and medium-sized enterprises is not obvious

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