Partition of the workspace for machine tool based on position-dependent modal energy distribution and clustering algorithm

Peng, Yili; Li, Bin; Mao, Xinyong; Li, Chunhui; Liu, Hongqi; Peng, Fangyu

doi:10.1007/s00170-020-05487-4

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…Stiffness is an essential characteristic of a manufacturing equipment because it contributes to its ability to accurately maintain the position between a tool center point and work-piece under stress [50]. In a previous case study, the stiffness response of the machine tool was discovered to vary by up to 27 % [51]. To enhance the static stiffness of the machine, the type of ground support upon which it rests was modified.…”

Section: Experimental Validation Of Static Analysismentioning

confidence: 99%

Finite Element Analysis and Structure Optimization of a Gantry-Type High-Precision Machine Tool

Chan

Ullah

Roy

et al. 2023

Preprint

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The high-precision machine tool's dynamic, static, and rigid nature directly affects the machining efficiency and surface quality. Static and dynamic analyses are essential for the design and improvement of precision machine tools to ensure good tool performance under difficult and demanding machining conditions. In this study, the performance of a high-precision machine tool was analyzed using its virtual model created using SOLIDWORKS. Static and model analysis using ANSYS Workbench software was conducted to establish the tool's static deformation and static stiffness. Furthermore, the static and dynamic characteristics of the tool were explored using a ﬁnite element modeling approach to study their performance. In particular, the structure, static force, modal, frequency spectrum, and topology optimization of machine tools were primarily analyzed. Using model analysis, we found the first four different frequencies (22.5, 28.9, 40.6, and 47.4 Hz) and vibration type, which suggested of a weak link. Further static structural analysis revealed that the deformation of the spindle was 67.26 μm. An experimental static rigidity analysis was performed, and the experimental deformation values of the tool and spindle were obtained. The static and dynamic characteristics, as well as the accuracy and efficiency of the finite element model, were verified by comparing the data with the Finite Element Analysis (FEA) results. Subsequently, we modified the settings and analysis model to ensure that the analysis results were consistent with the experimental findings. The error between the two results was within 1.56%. For an applied load of 5000 N on the spindle nose, the tool nose transient response was 0.5 s based on transient analysis. Under the condition that the structural deformation is as constant as possible, the lightweight structure may achieve the minimum weight and enhance the natural frequency; thus, the ideal structure will be obtained, and finite element analysis will then be performed. The optimal conditions for topology optimization include a lightweight structure, reduced structural deformation, and increased natural frequency of the structure. The developed method improves structural optimization, increases the ability of the product to be manufactured, and offers designers a variety of price-effective options.

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Section: Experimental Validation Of Static Analysismentioning

confidence: 99%

Finite Element Analysis and Structure Optimization of a Gantry-Type High-Precision Machine Tool

Chan

Ullah

Roy

et al. 2023

Preprint

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“…Random initialization generates executable determinants and initial data sets for each set of executable solutions. Calculate the fitness value; perform genetic operations in the initial data set, such as selective crossmutation; apply the fitness function to new individuals to calculate the fitness value; and select new individuals using simulated annealing algo-rithm [20], design the maximum number of loop iterations [21]. Determine whether the current temperature is lower than the end temperature.…”

Section: Multiobjective Flexible Workhopmentioning

confidence: 99%

The Implementation of Multiobjective Flexible Workshop Scheduling Based on Genetic Simulated Annealing-Inspired Clustering Algorithm

Huang

Wang

2022

Wireless Communications and Mobile Computing

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Multiobjective flexible workshop scheduling is an important subject to improve resource utilization and production efficiency and enhance the competitiveness of enterprises. As the situation of resource constraints becomes more and more severe, the problem of companies rationally allocating limited resources in production is becoming more and more serious. Today, the manufacturing industry widely adopts advanced manufacturing modes such as computer-integrated manufacturing and intelligent manufacturing, but in these semi-intelligent manufacturing modes with a high degree of uncertainty and a high degree of personnel dependence, it is difficult to adapt to the work of large-scale production. Therefore, suitable clustering algorithms are urgently needed to help solve these problems, and this paper selects a clustering algorithm based on the genetic simulation annealing algorithm. This article is aimed at studying the problem of efficiency improvement in the production process of large-scale manufacturing and at finding a stronger and more effective production mode for the manufacturing industry. Firstly, this paper introduces the basic principles of simulated annealing genetic algorithm and regularized clustering algorithm. These algorithms have excellent performance in searching for global optimal solutions. They can be constantly tested and computed to keep the calculation results close to the global optimal solution. In this paper, the K -means clustering algorithm is used to select the shortest completion time to represent the clustering target. According to the minimum distance principle, the machine, workpiece, and other objects are input into the clustering of the algorithm, and the K -means algorithm will send out the sorting plan. Therefore, a multiobjective flexible job shop scheduling model based on genetic simulated annealing algorithm and clustering algorithm is established. Then, by using hypothetical production data to simulate the operation of the workshop, the scheduling model was applied to conduct a deduction and empirical comparative study. The experimental results showed that the model shortened the completion time of the workpiece by 4.4% and increased the average load rate of the machine by 10%.

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“…Peng Y et al divided the whole machine-tool workspace, selected limited representative points, and then identified the natural frequency and damping ratio of each position individually and represented the dynamic characteristics of the machine tool [17]. However, such dynamics are the intermediate information about the dynamics of the whole machine tool.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Dynamic Characteristics for Machine Tools Based on Dynamic Stiffness Sensitivity

Li¹,

Song²,

Huang³

et al. 2021

Processes

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Dynamic parameters are the intermediate information of the entirety of machine dynamics. The differences between components have not been combined with the structural vibration in the cutting process, so it is difficult to directly represent the dynamic characteristics of the whole machine related to spatial position. This paper presents a method to identify sensitive parts according to the dynamic stiffness-sensitivity algorithm, which represents the dynamic characteristics of the whole machine tool. In this study, two experiments were carried out, the simulation verification experiment (dynamic experiment with variable stiffness) and modal analysis experiment (vibration test of five-axis gantry milling machine). The key modes of sensitive parts obtained by this method can represent the position-related dynamic characteristics of the whole machine. The characteristic obtained is that the inherent properties of machine-tool structure are independent of excitation. The method proposed in this paper can accurately represent the dynamic characteristics of the whole machine tool.

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Partition of the workspace for machine tool based on position-dependent modal energy distribution and clustering algorithm

Cited by 7 publications

References 22 publications

Finite Element Analysis and Structure Optimization of a Gantry-Type High-Precision Machine Tool

Finite Element Analysis and Structure Optimization of a Gantry-Type High-Precision Machine Tool

The Implementation of Multiobjective Flexible Workshop Scheduling Based on Genetic Simulated Annealing-Inspired Clustering Algorithm

Analysis of Dynamic Characteristics for Machine Tools Based on Dynamic Stiffness Sensitivity

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