基于自学习算法和多目标优化的智能封边机设计与实现  

Design and Implementation of Intelligent Edge Banding Machine based on Self-learning Algorithm and Multi-objective Optimization

作　　者：郑智华 ZHENG Zhihua(Sofia Home Furnishings Co.,Ltd.,Guangzhou 511358,China)

机构地区：[1]索菲亚家居股份有限公司,广东广州511358

出　　处：《数字通信世界》2024年第1期76-79,共4页Digital Communication World

摘　　要：封边工艺是家具制造过程中的关键环节,其质量和效率对最终产品的性能有着显著影响,然而传统的封边机往往缺乏自动优化和调整的能力,导致生产过程中存在资源浪费和质量问题。为了解决这一问题,文章提出了一种基于自学习算法和多目标优化的智能封边机设计。首先,利用传感器和图像识别技术实时监测封边工艺的各项参数;然后,通过建立多目标优化模型和支持向量回归(SVR)模型,实现封边质量、效率和材料利用率的综合优化。实验结果表明,与传统封边机相比,本文介绍的智能封边机可以显著提高封边质量和生产效率。Edge sealing technology is a key link in the furniture manufacturing process,and its quality and efficiency have a significant impact on the performance of the final product.However,traditional edge banding machines often lack the ability to automatically optimize and adjust,leading to resource waste and quality issues in the production process.To address this issue,the article proposes an intelligent edge banding machine design based on self-learning algorithms and multi-objective optimization.Firstly,real-time monitoring of various parameters of the edge banding process is carried out using sensors and image recognition technology.Then,by establishing multi-objective optimization models and support vector regression(SVR)models,comprehensive optimization of edge sealing quality,efficiency,and material utilization rate is achieved.The experimental results show that compared with traditional edge banding machines,the proposed intelligent edge banding machine can significantly improve edge banding quality and production efficiency.

关 键 词：封边机 自学习算法 多目标优化 支持向量回归 智能制造 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]