The Research on Thermal Error Modeling and Compensation on Machine Tools

Ren, Bing; Ren, Xiaozhen; Huang, Shan Yun; Li, Guozhi

doi:10.1109/iccect.2012.61

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Generally, PSO has been proven to be an efficient optimization algorithm for finding an optimal solution in various fields by searching an entire multidimensional problem space. The advantages of PSO include its good robustness, simplicity, and fast convergence speed, with relatively few parameters to adjust [32–35].…”

Section: Resultsmentioning

confidence: 99%

PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins

Lertampaiporn

Nuannimnoi

Vorapreeda

et al. 2019

BioMed Research International

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Several computational approaches for predicting subcellular localization have been developed and proposed. These approaches provide diverse performance because of their different combinations of protein features, training datasets, training strategies, and computational machine learning algorithms. In some cases, these tools may yield inconsistent and conflicting prediction results. It is important to consider such conflicting or contradictory predictions from multiple prediction programs during protein annotation, especially in the case of a multiclass classification problem such as subcellular localization. Hence, to address this issue, this work proposes the use of the particle swarm optimization (PSO) algorithm to combine the prediction outputs from multiple different subcellular localization predictors with the aim of integrating diverse prediction models to enhance the final predictions. Herein, we present PSO-LocBact, a consensus classifier based on PSO that can be used to combine the strengths of several preexisting protein localization predictors specially designed for bacteria. Our experimental results indicate that the proposed method can resolve inconsistency problems in subcellular localization prediction for both Gram-negative and Gram-positive bacterial proteins. The average accuracy achieved on each test dataset is over 98%, higher than that achieved with any individual predictor.

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Section: Resultsmentioning

confidence: 99%

PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins

Lertampaiporn

Nuannimnoi

Vorapreeda

et al. 2019

BioMed Research International

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show abstract

“…In such approaches, the time dependence of a series of target thermal error values (y) is not correlated to the past inputsor outputs, but rather an observation-response relationship is used instead. This has led to statistical solutions to the problem based on regression analysis, such as multivariable regression analysis (MRA) [18] and projection pursuit regression (PRR), in addition to several machine learning approaches such as multi-layer perceptron (MLP), regression Support Vector Machines (RSVM) [19], and Back Propagation Neural Network (BPNN) [20].…”

Section: Regression-based Predictionmentioning

confidence: 99%

CNC Machining Centre Thermal Error Prediction from Temperature Sensors Using Autoencoder-Augmented Long Short Term Memory Networks

Marei

Li²

2022

J. Phys.: Conf. Ser.

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To counteract thermal error due to material deformation in multi-axis heavy-duty CNC machining applications, this paper presents an approach based on Autoencoder-Augmented Long Short Term Memory deep learning models. The model predicts the z-axis offset of machine tools, in an idle state, with up to 96% accuracy, from ambient temperature sensor readings. By enabling a Long Short-Term Memory (LSTM) algorithm to learn time dependencies across input features and past output values with a fifth-order lag, a sequence-to-sequence regression problem is formulated. The model objective is to predict the z-axis thermal offset as an output from temperature sensor inputs. To reduce input data redundancy, the algorithm also leverages an Autoencoder to learn a reduced dimension feature representation for the input data. The reduction in input dimensionality achieves a considerable reduction in computation effort, measured in units of time, without compromising the prediction accuracy. The performance of the proposed technique is evaluated on a subset of the recorded data, and the model is benchmarked against several techniques from the literature. The results and qualitative evaluation demonstrate the superiority of the proposed approach at generating accurate predictions of the z-axis error of the system and, consequently, reducing the control error.

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“…China successfully developed a multi-head computer embroidery machine at the international advanced level at that time in 1988 [1] . At the same time, the pattern storage capacity of embroidery machines is also increasing [4] .…”

Section: Introductionmentioning

confidence: 99%

Design of Loading and Unloading Mechanism of Spring Steel Cloth Fixture for Computer Embroidery Machine

Yin

Long²

2023

J. Phys.: Conf. Ser.

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At present, the computer embroidery machine industry mainly uses manpower to disassemble the spring steel cloth clamp, which is very inefficient. This research designed a spring steel cloth clamp loading and unloading mechanism to solve this problem. First of all, the scheme of the loading and unloading mechanism of the spring steel cloth clamp is proposed. The mathematical model is established, and the simulation verification is carried out on this basis. Then the structural design is carried out. Finally, the prototype of the loading and unloading mechanism of the spring steel cloth clamp was trial produced, and continuous loading and unloading tests were carried out. The results show that the loading and unloading mechanism can automatically and continuously load and unload the spring steel cloth clips, meeting the design requirements. It also provides a solution for the embroidery machine industry to solve the time-consuming problem of manually loading and unloading spring steel cloth clips.

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The Research on Thermal Error Modeling and Compensation on Machine Tools

Cited by 6 publications

References 8 publications

PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins

PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins

CNC Machining Centre Thermal Error Prediction from Temperature Sensors Using Autoencoder-Augmented Long Short Term Memory Networks

Design of Loading and Unloading Mechanism of Spring Steel Cloth Fixture for Computer Embroidery Machine

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