Bayesian linear regression for surface roughness prediction

Kong, Dongdong; Zhu, Junjiang; Duan, Chaoqun; Lu, Lixin; Chen, Dongxing

doi:10.1016/j.ymssp.2020.106770

Cited by 67 publications

(24 citation statements)

References 33 publications

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“…As the excavation advances, more and more on-site data from sonic tests and the blasting vibration monitoring at lower benches are progressively accumulated. In order to make the best of those accumulated data and update the predictive model in real time as new data are continually added, the Bayesian linear regression that can make full use of the prior knowledge and include the uncertainty of posterior parameters in predicted results [ 54 ] was adopted.…”

Section: Bayesian Approach To Predict Blast-induced Damagementioning

confidence: 99%

A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data

Sun

et al. 2021

Sensors

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The blast-induced damage of a high rock slope is directly related to construction safety and the operation performance of the slope. Approaches currently used to measure and predict the blast-induced damage are time-consuming and costly. A Bayesian approach was proposed to predict the blast-induced damage of high rock slopes using vibration and sonic data. The relationship between the blast-induced damage and the natural frequency of the rock mass was firstly developed. Based on the developed relationship, specific procedures of the Bayesian approach were then illustrated. Finally, the proposed approach was used to predict the blast-induced damage of the rock slope at the Baihetan Hydropower Station. The results showed that the damage depth representing the blast-induced damage is proportional to the change in the natural frequency. The first step of the approach is establishing a predictive model by undertaking Bayesian linear regression, and the second step is predicting the damage depth for the next bench blasting by inputting the change rate in the natural frequency into the predictive model. Probabilities of predicted results being below corresponding observations are all above 0.85. The approach can make the best of observations and includes uncertainty in predicted results.

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Section: Bayesian Approach To Predict Blast-induced Damagementioning

confidence: 99%

A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data

Sun

et al. 2021

Sensors

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show abstract

“…In order to improve the selection of features, further methods, called filters, may be considered that classify the features based on external criteria. In this case, PCA (Principal Component Analysis) [22,23] and PCC (Pearson Correlation Coefficient) evaluations may be applied [24,25]. The filters could be used as the first processing phase, while the wrapper methods could be implemented to obtain the optimal feature selection for the ML applications [26,27].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances on Machine Learning Applications in Machining Processes

2021

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This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance- in terms of production quality and equipment availability. Artificial Intelligence (AI) offers new opportunities to develop and integrate innovative solutions in conventional machine tools to reduce undesirable effects during operational activities. In particular, the significant increase of the computational capacity may permit the application of complex algorithms to big data volumes in a short time, expanding the potentialities of ML techniques. ML applications are present in several contexts of machining processes, from roughness quality prediction to tool condition monitoring. This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring. Finally, a discussion on the advantages and limits of ML algorithms is summarized for future investigations.

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“…Zhang et al [6] developed a novel theoretical roughness prediction model, into which the components of kinematics, plastic side flow, material elastic recovery, and cracks effect were integrated, to determine the underlying mechanisms of the surface roughness variation during oblique diamond turning of the potassium dihydrogen phosphate (KDP) crystal. Kong et al [7] investigated improving the prediction accuracy of surface roughness in the milling process.…”

Section: Introductionmentioning

confidence: 99%

Forward and Inverse Relational Models between Machined Surface Roughness and Key Parameters of Machining Process

Pan

Ren

Wang

et al. 2021

Preprint

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The relational model between machined surface roughness (MSR) and the adopted key machining parameters (KMPs) significantly influences the predictability and controllability of the machining process; therefore, it has attracted considerable attention. However, two critical problems still persist in this field. First, although most existing studies focus on the prediction model for MSR (forward model), wherein the MSR is dependent on input KMPs values, the inverse model that can calculate the KMP based on input MSR value is equally important; however, the inverse model has not been investigated as extensively as the forward model. The second issue is that most of the existing forward models are mainly established based on mechanism analysis; however, due to the complexity of most machining processes, the accuracy and generality of the model are not optimal. Therefore, this paper proposes a universal method for mathematically establishing the inverse model of the relation between the MSR and KMP. Initially, based on the response surface methodology, orthogonal experiments were designed and conducted, and the results were used to establish the forward model between the MSR and KMP. Subsequently, by combining the forward model with a self-developed genetic algorithm-based multi-objective optimization algorithm, an establishing method for inverse model between MSR and KMPs was proposed. Finally, experiments were conducted to validate the developed models. The experimental results show that for the forward model, all the 10 experimental MSR values approach the MSR values predicted by the forward model, and the average deviation was only approximately 7%. Contrarily, for the inverse model, the average deviation was only approximately 7.64%. Both these results verify the accuracy and effectiveness of the proposed models. With this method, as long as the desired processing results and constraints are given, the process parameters can be accurately derived.

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Bayesian linear regression for surface roughness prediction

Cited by 67 publications

References 33 publications

A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data

A Bayesian Approach to Predict Blast-Induced Damage of High Rock Slope Using Vibration and Sonic Data

Recent Advances on Machine Learning Applications in Machining Processes

Forward and Inverse Relational Models between Machined Surface Roughness and Key Parameters of Machining Process

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