A novel virtual metrology scheme for predicting machining precision of machine tools

Yang, Haw‐Ching; Hung, Min‐Hsiung; Cheng, Fan‐Tien

doi:10.1109/icra.2013.6630586

Cited by 17 publications

(3 citation statements)

References 10 publications

(13 reference statements)

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“…In the area of mechanical engineering, which also includes the machine tool development, the amount of VS solutions is significantly smaller. However, the most popular VS applications are vibration estimation and suppression [17,18] and prediction of the machine precision [19].…”

Section: Application Fieldsmentioning

confidence: 99%

Virtual Sensor for Calibration of Thermal Models of Machine Tools

Dementjev

Hensel

Kabitzsch

et al. 2014

Advances in Artificial Neural Systems

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Machine tools are important parts of high-complex industrial manufacturing. Thus, the end product quality strictly depends on the accuracy of these machines, but they are prone to deformation caused by their own heat. The deformation needs to be compensated in order to assure accurate production. So an adequate model of the high-dimensional thermal deformation process must be created and parameters of this model must be evaluated. Unfortunately, such parameters are often unknown and cannot be calculated a priori. Parameter identification during real experiments is not an option for these models because of its high engineering and machine time effort. The installation of additional sensors to measure these parameters directly is uneconomical. Instead, an effective calibration of thermal models can be reached by combining real and virtual measurements on a machine tool during its real operation, without additional sensors installation. In this paper, a new approach for thermal model calibration is presented. The expected results are very promising and can be recommended as an effective solution for this class of problems.

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Section: Application Fieldsmentioning

confidence: 99%

Virtual Sensor for Calibration of Thermal Models of Machine Tools

Dementjev

Hensel

Kabitzsch

et al. 2014

Advances in Artificial Neural Systems

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“…Failures of these components usually result in unplanned downtime and huge economic loss. Thus, fast and accurate diagnostics of rotating components are always the top priority of prognostics and health management (PHM), to enable a reliable and safe operation for mechanical systems by correctly and timely identifying fault types, root causes, and locations of the failures as well as the time of detection [2][3][4][5][6][7][8]. DL redefines the diagnostic paradigm in manufacturing industries through autofeaturing instead of traditional ML methods, as the latter need to extract hand-crafted and fault-related features manually and empirically based on the expertise knowledge [9].…”

Section: Introductionmentioning

confidence: 99%

Using Transfer Learning to Reduce Preparing Time of Online Diagnosis with Limited Rotating-Component Data

Li¹

2022

J Mater Sci Manufac Res

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Under the environment of mass-customization, valid samples for a specific target object become fewer and fewer. Thus, Transfer Learning (TL) is widely applied to those data-consuming Deep Learning (DL) and Machine Learning (ML) models to avoid recollecting high-volume training samples from scratch. However, TL-based methods used in fault diagnosis for mechanical rotating components are still in the infancy phase. Poor practicality and usability problems increase the total preparing time. Without online diagnosis, there might be more unknown risks during the preparing time of a TL method. To reduce the preparing time of existing TL techniques, this paper proposes a TL-based Gradual Improvement Scheme (TLGIS) to achieve a systematic online diagnosis of rotating components. TLGIS solves the problem through seamless interactions among five modules of Data Preprocessing (DP), Data Estimating (DE), Data Modeling (DM), Model Improving (MI), and Accuracy Checking, (AC). Via illustrative examples, TLGIS is demonstrated to be reliable and robust using deep Convolutional Neural Networks (CNN) by two public bearing datasets from the Internet and two types of rotating wheel datasets from a practical CNC machine. In total, TLGIS only requires at least 80 samples and at most 250 samples divided into small-size batches to gradually and continuously fine-tune the model with an accuracy of 90% and 99%, respectively, instead of inputting a large-scale dataset all at once, which is unrealistic in a practical production scenario. Compared to conventional approaches, TLGIS efficiently saves at most 90% of preparing time.

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“…e virtual metrology has been widely applied in diverse high-tech industries, including the semiconductor industry [1], TFT-LCD industry [2], and the solar-cell industry [3]. Moreover, the virtual metrology technology has been applied for processing the CNC machine tool and reasonable results have been achieved accordingly [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Performance Prediction Model for the Machining Process Based on the Interval Type-2 Fuzzy Neural Network

Tian

Zhao

Sun

et al. 2020

Mathematical Problems in Engineering

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The prediction model is the most important part of the virtual metrology system. Predicting the performance of the machining process has been widely applied in manufacturing, which can reduce costs and improve efficiency compared with the manual operation. In this paper, a novel performance prediction model for the machining process is proposed based on the interval type-2 fuzzy neural network. The interval type-2 fuzzy logic system with a complete rule base, type-reduction, and defuzzified output is simplified by the BMM method to meet the requirements of the prediction. The proposed prediction model is trained using a gradient-based optimization algorithm. To evaluate the performance of the proposed approach, it is applied to wire electrical discharge turning process for predicting material removal rate and surface roughness with a published dataset. The results show that the proposed method is an effective scheme in the studied cases.

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A novel virtual metrology scheme for predicting machining precision of machine tools

Cited by 17 publications

References 10 publications

Virtual Sensor for Calibration of Thermal Models of Machine Tools

Virtual Sensor for Calibration of Thermal Models of Machine Tools

Using Transfer Learning to Reduce Preparing Time of Online Diagnosis with Limited Rotating-Component Data

A Novel Performance Prediction Model for the Machining Process Based on the Interval Type-2 Fuzzy Neural Network

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