A new in-process material removal rate monitoring approach in abrasive belt grinding

Ren, Lijuan; Zhang, Guangpeng; Wang, Yuan; Zhang, Qian; Wang, Fei; Huang, Yumei

doi:10.1007/s00170-019-04124-z

Cited by 24 publications

(7 citation statements)

References 19 publications

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“…Such strong correlations between MRR, grinding forces, and chip temperature are regularly found in literature (Jin and Stephenson, 2006;Malkin and Guo, 2007;Klocke, 2018;Ren et al, 2019) and observed in various real-life applications, even though temperatures in grinding processes are difficult to measure. There are several limiters on the maximum temperature for a good grinding process of steel, such as the austenitizing temperature of around 730 °C or surface softening temperatures of around 400 °C, depending on the steel in question (Jin and Stephenson, 2006).…”

Section: Frontiers In Manufacturing Technologymentioning

confidence: 59%

Towards a multi-abrasive grinding model for the material point method

Leroch

Grützmacher²,

Heckes

et al. 2023

Front. Manuf. Technol.

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An efficient optimization of surface finishing processes can save high amounts of energy and resources. Because of the large occurring deformations, grinding processes are notoriously difficult to model using standard (mesh-based) micro-scale modeling techniques. In this work, we use the meshless material point method to study the influence of abrasive shape, orientation, rake angle, and infeed depth on the grinding result. We discuss the chip morphology, the surface topography, cutting versus plowing mode, the material removal rate, and the chip temperature. A generalization of our model from a straightforward single-abrasive approach to a multiple-abrasive simulation with pseudo-periodical boundary conditions greatly increases the degree of realism and lays the foundation for comparison with real finishing processes. We finally compare our results for multiple abrasives to those obtained for a scaled-down molecular dynamics system and discuss similarities and differences.

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Section: Frontiers In Manufacturing Technologymentioning

confidence: 59%

Towards a multi-abrasive grinding model for the material point method

Leroch

Grützmacher²,

Heckes

et al. 2023

Front. Manuf. Technol.

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“…• Endpoint detection of weld seam removal • Surface quality/ material removal [152][153][154] Vibratory media finishing • Measure surface media velocity fields [155] Grinding…”

Section: Sensing Systemsmentioning

confidence: 99%

Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: A review

Pandiyan

Shevchik

Wasmer

et al. 2020

Journal of Manufacturing Processes

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Abrasive finishing processes such as grinding, lapping or disc polishing are one of the most practical means for processing materials to manufacture products with fine surface finish, surface quality and dimensional accuracy. However, they are one of the most difficult and least-understood processes for two main reasons. Firstly, the abrasive grains present in the tool surface are randomly oriented. Secondly, they undergo complex interactions in the machining zone. Given the advances in sensor technologies, the finishing processes can now be sensorized, and the vast amount of data produced can be exploited to model and monitor the processes using Artificial Intelligence techniques. Data-driven models have turned into a hot focus in engineering with the rise of machine learning and deep learning algorithms, which have greatly spread all through the academic community. The scope of this paper is mainly to review the application of Artificial Intelligence as well as supporting sensing and signal processing techniques in modelling and monitoring on different types of abrasive processes in metal finishing. The paper gives a detailed background on the key mechanisms and defects in the different abrasive finishing process and lists the suitable sensing techniques for their monitoring. The paper reports that most of the Artificial Intelligence algorithms available are not fully exploited for monitoring and modelling in abrasive finishing and emphasizes on bridging this gap. The probable research tendency on datadriven monitoring and modelling for abrasive finishing is also forecasted.

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“…Ren et al proposed a method for monitoring the material removal rate of belt grinding by spark field measurement, the correct rate of grinding depth recognition can reach 95%, which is an effective method for monitoring the material removal rate of belt grinding [12]. Zhong et al Zhong et al proposed a backpropagation artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

A Material Removal State Prediction Method Based on Multi-Scale Attention Mechanism

Tang

Liu

et al. 2022

Volume 2: 42nd Computers and Information in Engineering Conference (CIE)

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The exact removal of material in abrasive belt grinding determines the final machining quality of the workpiece. However, it is difficult to determine the removal state of materials in actual processing, which is affected by factors such as abrasive belt wear and processing errors. Therefore, a multi-scale attention convolutional neural network for material removal state prediction method is proposed based on the analysis of displacement data. First, the first-order difference and sliding-window expansion methods for displacement data are adopted, making it possible to use displacement data for deep learning, which is the premise of material removal state prediction. Then, the multi-scale convolutional neural network is Employed to extract important features of the displacement data. Due to the different importance of different features, Squeeze-and-Excitation Networks are used to independently assign the importance of features based on the loss function, so that the model pays more attention to those main features and ignores the secondary features, which can improve the convergence speed and prediction accuracy of the model. The K6 cross-validation of experiment results shows that this method can accurately predict the material removal state with an average prediction accuracy of 87.9%, which can be practically applied to the online prediction of the material removal state in industrial processing to further control the processing quality.

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A new in-process material removal rate monitoring approach in abrasive belt grinding

Cited by 24 publications

References 19 publications

Towards a multi-abrasive grinding model for the material point method

Towards a multi-abrasive grinding model for the material point method

Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: A review

A Material Removal State Prediction Method Based on Multi-Scale Attention Mechanism

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