Depth of dressing optimization in CBN wheels of different friabilities using acoustic emission (AE) technique

Alexandre, Felipe Aparecido; Lopes, José Cláudio; Fernandes, Lucas de Martini; Ribeiro, Fernando Sabino Fonteque; Fernandez, Breno Ortega; Sanchez, Luiz Eduardo de Ângelo; Oliveira, Rodolfo Fischer Moreira de; Mello, Hamilton José de; Aguiar, Paulo Roberto de; Bianchi, Eduardo Carlos

doi:10.1007/s00170-020-04994-8

Cited by 26 publications

(2 citation statements)

References 49 publications

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“…Internal structural changes in rock can be attributed to a variety of factors, such as differences in the expansion coefficients of its mineral components, structural pre-cracking that occurs during roasting treatment, growth of microscopic internal fractures, pores and micropores, residual microscopic defects after water loss, and uneven stresses caused by structural thermal stresses 8 . Roasting treatment can reduce the strength of rock as well as induce grain boundary fractures, causing local stress concentrations that lead to comminution 9 , 10 . Since the twentieth century, studies of thermally-assisted crushing of a variety of geological minerals have shown exceptional results 11 , with some indicating that potassium-bearing shale roasting leads to acid leaching during potassium extraction 12 , 13 .…”

Section: Introductionmentioning

confidence: 99%

Characteristics of ultrafine-ground potassium-bearing shale and the effects of roasting pre-cracking

Zhu

Gai

2022

Sci Rep

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Potassium-bearing shale is being developed as a potential alternative to potash for use in fertilisers. The first step in this process is to reduce its particle size by crushing. This paper explores whether roasting pre-cracked potassium-bearing shale can improve the quality of the resulting ultrafine product. Analysis of the particle size distribution of the ultrafine product and its fractal dimension found contradictory results: the minimum particle size distribution was obtained by roasting for 2.5 h, while the minimum fractal dimension was obtained by roasting for 1 h. Fuzzy comprehensive evaluation was conducted with three indicators—(1) the weight of the − 10 μm product, (2) the fractal dimension of the particle size distribution, and (3) d97—to obtain a unique combination of indicators that reflects the quality and quantity of the products. The weights of the three indicators were calculated by an analytic hierarchical process to be 0.69, 0.149 and 0.161, respectively. Roasting pre-cracked shale for 2–2.5 h was found to improve the mean values of the fuzzy comprehensive evaluation indicators by about 0.07. However, the cost increased from 2.82 RMB to ≥ 10.08 RMB, which is not feasible for widespread industrial implementation.

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Section: Introductionmentioning

confidence: 99%

Characteristics of ultrafine-ground potassium-bearing shale and the effects of roasting pre-cracking

Zhu

Gai

2022

Sci Rep

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“…The sensitivity and immunity from interference make AE signals quite suitable 2 of 17 for tool condition monitoring. AE signals are used to evaluate grinding wheel conditions in many studies [7][8][9]. AE signals dramatically change for the occurrence of a sudden damage on the tool during machining process.…”

Section: Introductionmentioning

confidence: 99%

Diamond Grinding Wheel Condition Monitoring Based on Acoustic Emission Signals

Liu

et al. 2021

Sensors

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Acoustic emission (AE) phenomenon has a direct relationship with the interaction of tool and material which makes AE the most sensitive one among various process variables. However, its prominent sensitivity also means the characteristics of random and board band. Feature representation is a difficult problem for AE-based monitoring and determines the accuracy of monitoring system. It is knottier for the situation of using diamond wheel grinding optical components, not only because of the complexity of grinding process but also the high requirement on surface and subsurface quality. This paper is dedicated to AE-based condition monitoring of diamond wheel during grinding brittle materials and feature representation is paid more attention. AE signal of brittle-regime grinding is modeled as a superposition of a series of burst-type AE events. Theory analysis manifested that original time waveform and frequency spectrum are all suitable for feature representation. Considering the convolution form of b-AE in time domain, a convolutional neural network with original time waveform of AE signals as the input is built for multi-class classification of wheel state. Detailed state division in a wheel’s whole life cycle is realized and the accuracy is over 90%. Different from the overlapping in time domain, AE components of different crack mechanisms are probably separated in frequency domain. From this point of view, AE spectrums are more suitable for feature extraction than the original time waveform. In addition, the time sequence of AE samples is essential for the evaluation of wheel’s life elapse and making use of sequential information is just the idea behind recurrent neural network (RNN). Therefore, long short-term memory (LSTM), a special kind of RNN, is used to build a regression prediction model of wheel state with AE spectrums as the model input and satisfactory prediction accuracy is acquired on the test set.

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Performance of austempered ductile iron (ADI) grinding using diluted oil in MQL combined with wheel cleaning jet and different CBN grains friability

Lopes

Fernandes

Garcia

et al. 2020

Int J Adv Manuf Technol

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Depth of dressing optimization in CBN wheels of different friabilities using acoustic emission (AE) technique

Cited by 26 publications

References 49 publications

Characteristics of ultrafine-ground potassium-bearing shale and the effects of roasting pre-cracking

Characteristics of ultrafine-ground potassium-bearing shale and the effects of roasting pre-cracking

Diamond Grinding Wheel Condition Monitoring Based on Acoustic Emission Signals

Performance of austempered ductile iron (ADI) grinding using diluted oil in MQL combined with wheel cleaning jet and different CBN grains friability

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