Prioritization analysis and compensation of geometric errors for ultra-precision lathe based on the random forest methodology

Tao, Hongfei; Chen, Ran; Xuan, Jianhua; Xia, Qi; Yang, Zhongyuan; Zhang, Xin; He, Shaoming; Shi, Tielin

doi:10.1016/j.precisioneng.2019.09.012

Cited by 26 publications

(10 citation statements)

References 34 publications

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“…The ideal homogeneous coordinate transformation matrix from the tactile probe coordinate system to the machine's base coordinate system wt_ideal ideal = ( , , ) f x y z T can be derived via MBS theory [26] [27], it is an averaged fitting model on y and z about x. For PIGEs, S is positionindependent, so it is treated as a constant vector.…”

Section: Original Volumetric Error Models' Unionmentioning

confidence: 99%

“…Thus, we are no longer dedicated to identifying the explicit geometric errors in this paper, but to seeking a further equivalent solution in the intermediate solution space. First, we establish the original volumetric models union based on MBS [26], DMM [7], and response surface method [27], then, a new equivalent dimensionality reduction method is proposed, to construct the equivalent volumetric models union (EVEMU), which is more suitable for small sample measuring data. To improve the measurement flexibility and efficiency of artifacts as much as possible, a new 3-D ball array calibrator is proposed, it has 3 measurable dimensions, a more simple structure with a small volume compared to conventional 2-D or 3-D calibrators.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Local Calibration of a Weak Stiffness On-Machine Measuring Device with a New 3-D Calibrator

Lin

Sui

et al. 2022

SSRN Journal

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Section: Original Volumetric Error Models' Unionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Local Calibration of a Weak Stiffness On-Machine Measuring Device with a New 3-D Calibrator

Lin

Sui

et al. 2022

SSRN Journal

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“…RF possesses the capability of random feature selection at each node and no pruning or stopping rule during the training process (Tan et al 2020). Each CART is trained on a bootstrapped sample of the original training data by selecting many bootstrap observations from the original data (Tao et al 2020). The RF uses a random subset of predictive variables in the division of every node, which reduces the generalization error (Chen et al 2020), and after a large number of regression trees have been generated, they are used to predict the class of new data, the best split at each node of the tree is searched only amongst a randomly selected subset of predictors, using the so-called out-of-bag (OOB) data (Hanna et al 2020).…”

Section: Modelling Approachesmentioning

confidence: 99%

Intelligent Data Analytics Approaches for Predicting Dissolved Oxygen Concentration in River: Extremely Randomized Tree Versus Random Forest, MLPNN and MLR

Heddam

2020

Springer Transactions in Civil and Environmental Engineering

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Control of water quality by monitoring water variables is still of major importance for the protection of human life (El Najjar et al. 2019). Rivers and streams are the major's component of the freshwater ecosystems and constitute the source of life for both humans and animals and they become the most "endangered ecosystems" in the world (Kumar and Jayakumar 2020; Kebede et al. 2020;Emenike et al. 2020). From year to year, a considerable work was carried out to maintain a good water quality status (Jerves-Cobo et al. 2020). Dissolved oxygen concentration (DO) in rivers, lakes and streams freshwater is a key variable in the assessment and control of the aquatic life and freshwater health (Banerjee et al. 2019). It also plays tremendous role in the control of water quality, and has many important effects on aquatic hydrochemistry and aquatic toxicology, and used as an "early warning" and classifying the eutrophication status of lakes (Hoang et al. 2019).Dissolved oxygen should be sufficiently and accurately estimated and monitored, and the variability of DO in freshwaters ecosystems can stem from various reasons and the amount of variability is particularly great when considering the interaction between several other water quality variables (Hutchins and Hitt 2019;Yaseen et al. 2018a). Over the years, it was demonstrated that there is substantial evidence that DO concentration is highly related to water quality variables (e.g. water temperature, water pH, specific conductance), which can be linked together. For example, it was demonstrated that decrease of DO concentration was mainly related to the increase S. Heddam (B) University 20 Août 1955 Skikda,

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“…Therefore, reasonable distribution of weights to each geometric error is the prerequisite for the accuracy optimization design. In addition, error compensation is another way to improve the machining accuracy by compensating the key geometric errors [9][10][11][12][13]. In economic terms, it is unreasonable to compensate all geometric errors.…”

Section: Introductionmentioning

confidence: 99%

Machining Accuracy Enhancement of Five-axis Machine Tools by a New Sensitivity Analysis Method Based on Machining Error Model

Tao

Chen

et al. 2022

Preprint

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Identification of key geometric errors is an essential prerequisite for improving the machining accuracy of five-axis machine tools. This paper presents a new sensitivity analysis (SA) method to extract key geometric errors, and then to improve the machining performance of machine tools by compensating key geometric error components. Development of geometric error prediction model is involved to obtain geometric error values at arbitrary positions at first. Based on the multi-body system theory and flank milling theory, the machining error prediction model is developed, which considers 37 geometric errors. Then, a new SA method is introduced by taking the machining error model as sensitivity analysis model and taking the geometric errors as analytical factors. Meanwhile, a sensitivity index, which has the characteristics of simple expression and clear physical meaning, is proposed, i.e., the peak value of the machining error caused by each geometric error. Moreover, the simulations analysis is carried out to obtain the sensitivity coefficient of each geometric error and the key error components. Finally, the validity and correctness of the proposed method is demonstrated by the experiments. Furthermore, the SA method can be extended to multi-axis machine tools.

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Prioritization analysis and compensation of geometric errors for ultra-precision lathe based on the random forest methodology

Cited by 26 publications

References 34 publications

Local Calibration of a Weak Stiffness On-Machine Measuring Device with a New 3-D Calibrator

Local Calibration of a Weak Stiffness On-Machine Measuring Device with a New 3-D Calibrator

Intelligent Data Analytics Approaches for Predicting Dissolved Oxygen Concentration in River: Extremely Randomized Tree Versus Random Forest, MLPNN and MLR

Machining Accuracy Enhancement of Five-axis Machine Tools by a New Sensitivity Analysis Method Based on Machining Error Model

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