Optimized tool path planning for five-axis flank milling of ruled surfaces using geometric decomposition strategy and multi-population harmony search algorithm

Yi, Jin; Chu, Chih-Hsing; Kuo, Chi-Lung; Li, Xinyu; Gao, Liang

doi:10.1016/j.asoc.2018.08.041

Cited by 26 publications

(8 citation statements)

References 25 publications

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“…"rand" refers to a randomly generated number that falls within the interval of [0, 1]. Equation (12) shows how to generate new harmonies in a phase-wise nonlinear dynamic convergence region. When , that is, the number of iterations is before half of the maximum number of iterations, new harmonies are still randomly generated in the global search domain like HS algorithm to increase the diversity of harmonies.…”

Section: Phase-wise Nonlinear Dynamic Convergence Regionmentioning

confidence: 99%

“…HS has several advantages, including simplicity of implementation, ease of parameter tuning, and relatively quick convergence compared to other optimization algorithms. It has found successful applications in various fields, such as feature selection [12] , robot path planning [13] , economic dispatch [14−16] , shop scheduling [17−19] , neural networks [20−24] , and image processing [25−28] . Despite its successes, the HS algorithm still faces challenges, including slow convergence speed and weak local search capabilities, which can impact its optimization performance.…”

Section: Introductionmentioning

confidence: 99%

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Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Wang,

Ouyang,

Zhou

et al. 2023

Complex Syst. Model. Simul.

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Harmony Search (HS) algorithm is highly effective in solving a wide range of real-world engineering optimization problems. However, it still has the problems such as being prone to local optima, low optimization accuracy, and low search efficiency. To address the limitations of the HS algorithm, a novel approach called the Dual-Memory Dynamic Search Harmony Search (DMDS-HS) algorithm is introduced. The main innovations of this algorithm are as follows: Firstly, a dual-memory structure is introduced to rank and hierarchically organize the harmonies in the harmony memory, creating an effective and selectable trust region to reduce approach blind searching. Furthermore, the trust region is dynamically adjusted to improve the convergence of the algorithm while maintaining its global search capability. Secondly, to boost the algorithm's convergence speed, a phased dynamic convergence domain concept is introduced to strategically devise a global random search strategy. Lastly, the algorithm constructs an adaptive parameter adjustment strategy to adjust the usage probability of the algorithm's search strategies, which aim to rationalize the abilities of exploration and exploitation of the algorithm. The results tested on the Computational Experiment Competition on 2017 (CEC2017) test function set show that DMDS-HS outperforms the other nine HS algorithms and the other four state-of-the-art algorithms in terms of diversity, freedom from local optima, and solution accuracy. In addition, applying DMDS-HS to data clustering problems, the results show that it exhibits clustering performance that exceeds the other seven classical clustering algorithms, which verifies the effectiveness and reliability of DMDS-HS in solving complex data clustering problems.

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Section: Phase-wise Nonlinear Dynamic Convergence Regionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Wang,

Ouyang,

Zhou

et al. 2023

Complex Syst. Model. Simul.

View full text Add to dashboard Cite

show abstract

“…In this study, we chose the Harmony Search (HS) method, which is a metaheuristic algorithm developed by Geem et al [41]. HS has been applied to many types of science and engineering problems [3,[42][43][44], including path optimization [45][46][47][48] and traveling salesman [49][50][51][52][53] problems.…”

Section: Coding and Encoding Systemmentioning

confidence: 99%

Harmony Search Optimization of Nozzle Movement for Additive Manufacturing of Concrete Structures and Concrete Elements

2020

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There are several ways of using three-dimensional printing techniques in the construction industry. One method that seems quite feasible is the concreting of walls and structural components starting at the bottom and progressing up in layers according to the principles of additive manufacturing. The goal of this study is to optimize the movements of a nozzle at one level that will result in this operation. This study considers that the movements of the nozzle can be of two types: rectangular only (i.e., only in x and y directions) or more freely, including moving in diagonal directions. Applications are performed on four hypothetical flats (with 7, 8, 14, and 31 walls, respectively) and a structural component with 17 members. It is shown that as the number of walls and members increase, the problem of optimizing the movements of the nozzle becomes increasingly difficult due to exponentially increasing path combinations. A comparison is presented in terms of the ratio of movements of the nozzle without concreting to total distances traveled. The optimization process is conducted using the Harmony Search algorithm with a special coding and encoding system.

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“…Chu et al [25] proposed an optimisation scheme to search for optimal curve control points to preserve high-order continuity in the cutter motion and reduce the machining error to some extent. Yi et al [26] divided the ruled surface into segments and independently optimised the toolpaths on the sub-surfaces using the proposed multi-population harmony search algorithm. The individual toolpaths were then combined to form one complete one, subsequently obtaining higher machining precision with less computational time.…”

Section: Introductionmentioning

confidence: 99%

Positioning of a conical cutter for centrifugal blades finish machining based on the best uniform approximation (BUA) method

Dong

2022

Preprint

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To demonstrate the closeness between the tool and nominal surfaces during the flank milling of centrifugal blades, discrete points on the conical cutter tool axis are projected in the nominal surface normal direction, and a mapping curve on the conical surface is obtained. On this basis, a novel toolpath planning method, under hub surface constraints, is proposed for flank milling centrifugal blades using a conical cutter based on the BUA method. This is a nonlinear optimisation problem, therefore the programming linearisation method is provided. The effectiveness of the proposed BUA method is verified via numerical example of a ruled surface and compared with the least square (LS) and two-point offset (DPO) methods. The influence of the conical cutter cone angle on the planning error is also analysed. The proposed BUA method is also applied to a centrifugal blade flank milling numerical example and its feasibility is verified.

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Optimized tool path planning for five-axis flank milling of ruled surfaces using geometric decomposition strategy and multi-population harmony search algorithm

Cited by 26 publications

References 25 publications

Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Harmony Search Optimization of Nozzle Movement for Additive Manufacturing of Concrete Structures and Concrete Elements

Positioning of a conical cutter for centrifugal blades finish machining based on the best uniform approximation (BUA) method

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