High-speed 3D printing temperature system with optimized PlD parameters based on improved ant lion optimization algorithm

IF 1.9 3区工程技术 Q3 ENGINEERING, MANUFACTURING

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture Pub Date : 2024-07-23 DOI:10.1177/09544054241255980

Rui Zhou, Junchi Jiang, Chunzhi Du, Shuaiyuan Lu

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Abstract

An Ant Lion Optimization algorithm based on elite Opposition-based learning and Cosine factors (OCALO) is proposed to address the problem of poor response and stability during heating process in high-speed 3D printing temperature system.The generation of the initial solution in OCALO algorithm is enhanced by the introduction of a new Tent-Logistic-Cotangent composite chaotic mapping, which guarantees the diversity of population. The PID parameters are optimized using the improved algorithm. Compared with two existing classical algorithms and three improved ALO algorithms, the proposed algorithm improves the convergence speed, global search ability and the ability to jump out of the local optimal solution. The outcomes of simulation and experimentation demonstrate that the algorithm improves the transient and steady-state performance of temperature control with better accuracy and robustness. It takes at least 123 s faster than other controllers to reach stability and is more than two times stronger than other PID controllers, making it better suited to high-speed 3D printing temperature systems.

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基于改进的蚁狮优化算法优化 PlD 参数的高速 3D 打印温度系统

针对高速三维打印温度系统加热过程中响应和稳定性差的问题，提出了一种基于精英对立学习和余弦因子（OCALO）的蚁狮优化算法。通过引入新的Tent-Logistic-Cotangent复合混沌映射，OCALO算法中初始解的生成得到了增强，从而保证了种群的多样性。使用改进算法对 PID 参数进行了优化。与现有的两种经典算法和三种改进 ALO 算法相比，所提出的算法提高了收敛速度、全局搜索能力和跳出局部最优解的能力。仿真和实验结果表明，该算法改善了温度控制的瞬态和稳态性能，具有更好的精度和鲁棒性。它比其他控制器至少快 123 秒达到稳定，比其他 PID 控制器强两倍以上，因此更适合高速 3D 打印温度系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 工程技术-工程：机械

CiteScore

5.10

自引率

30.80%

发文量

167

审稿时长

5.1 months

期刊介绍： Manufacturing industries throughout the world are changing very rapidly. New concepts and methods are being developed and exploited to enable efficient and effective manufacturing. Existing manufacturing processes are being improved to meet the requirements of lean and agile manufacturing. The aim of the Journal of Engineering Manufacture is to provide a focus for these developments in engineering manufacture by publishing original papers and review papers covering technological and scientific research, developments and management implementation in manufacturing. This journal is also peer reviewed. Contributions are welcomed in the broad areas of manufacturing processes, manufacturing technology and factory automation, digital manufacturing, design and manufacturing systems including management relevant to engineering manufacture. Of particular interest at the present time would be papers concerned with digital manufacturing, metrology enabled manufacturing, smart factory, additive manufacturing and composites as well as specialist manufacturing fields like nanotechnology, sustainable & clean manufacturing and bio-manufacturing. Articles may be Research Papers, Reviews, Technical Notes, or Short Communications.