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MOEA/D extrusion process parameter multi-objective optimization method and device

A multi-objective optimization and process parameter technology, applied in data processing applications, instruments, calculation models, etc., to achieve excellent performance, good population diversity and convergence, and reduce energy consumption

Active Publication Date: 2021-08-27
GUANGDONG UNIV OF TECH
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Problems solved by technology

However, the current optimization research methods for the extrusion production process of aluminum profiles focus on physical modeling, simulation and machine learning, and there are few research results using methods in the field of deep learning, so this research direction has great research potential

Method used

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  • MOEA/D extrusion process parameter multi-objective optimization method and device
  • MOEA/D extrusion process parameter multi-objective optimization method and device
  • MOEA/D extrusion process parameter multi-objective optimization method and device

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Experimental program
Comparison scheme
Effect test

Embodiment

[0119] 1. Evaluation indicators

[0120] The anti-generational distance evaluation index IGD is the average value of the minimum distance between the actual PF individual and the approximate solution set obtained by the algorithm. Therefore, the smaller the value of IGD, the better the convergence and distribution of the algorithm. IGD is calculated as follows:

[0121]

[0122] Hypervolume evaluation means that HV calculates a set of algorithm PF approximate solutions and distributes them at the reference point of the target space governs the volume of the target space, the formula is as follows:

[0123]

[0124] In the formula, VLO(·) represents the Lebesgue measure. The larger the HV value, the more S is similar to the entire PF, so the higher the HV value, the better the convergence and distribution of the algorithm.

[0125] 2. Algorithm comparison

[0126] 2.1. Parameter setting

[0127] In order to verify the performance of the improved MOEA / D algorithm, the...

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Abstract

The invention belongs to the technical field of extrusion process parameter optimization, and particularly relates to an MOEA / D extrusion process parameter multi-objective optimization method and device. The extrusion process parameters are optimized by taking the energy consumption, the yield and the die wear as targets, so that the energy consumption is effectively reduced, the yield is improved, and the die wear is reduced; the provided improved MOEA / D algorithm has more excellent performance, and has better population diversity and convergence.

Description

technical field [0001] The invention belongs to the technical field of optimization of extrusion process parameters, and more specifically relates to a multi-objective optimization method and device for MOEA / D extrusion process parameters. Background technique [0002] Low energy consumption, high yield and low mold wear in the production of aluminum profiles have become the goals pursued by enterprises. In traditional production, the process parameters are mainly set according to the experience of the workers and the operation manual, and the appropriate process parameters are selected according to the actual energy consumption and product conditions to make the production of aluminum profiles the best among energy consumption, yield and mold wear. Merit becomes difficult. With the advent of the industrial Internet, artificial intelligence and big data era, sensors can be used to obtain real-time production data, and the data can be transmitted to the database through the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06N3/00
CPCG06Q10/04G06Q10/067G06N3/006
Inventor 徐康康谭喜杨海东印四华朱成就陈铭俊
Owner GUANGDONG UNIV OF TECH
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