Soft Computing Techniques for Surface Roughness Prediction in Hard Turning: A Literature Review

He, Kang; Gao, Mengdi; Zhao, Zhuanzhe

doi:10.1109/access.2019.2926509

Cited by 25 publications

(12 citation statements)

References 62 publications

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“…Precision hard turning concerns the turning of material with superior mechanical properties [1], particularly high levels of strength [2] and hardness [3] such as alloy steels [4] and nickel-based super alloys [5,6], materials with high strength/weight ratio [7] and wear resistance [8] such as titanium alloys [9]. Although these materials fulfil a wide range of industrial requirements [10], they are difficult-to-cut and require advanced manufacturing processing techniques [11].…”

Section: Introductionmentioning

confidence: 99%

On the Assessment of Surface Quality and Productivity Aspects in Precision Hard Turning of AISI 4340 Steel Alloy: Relative Performance of Wiper vs. Conventional Inserts

et al. 2020

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This article reports an experimental assessment of surface quality generated in the precision turning of AISI 4340 steel alloy using conventional round and wiper nose inserts for different cutting conditions. A three-factor (each at 4 levels) full factorial design of experiment was followed for feed rate, cutting speed, and depth of cut, with resulting machined surface quality characterized by resulting average roughness (Ra). The results show that, for the provided range of cutting conditions, lower surface roughness values were obtained using wiper inserts compared with conventional inserts, indicating a superior performance. When including the type of insert as a qualitative factor, ANOVA revealed that the type of insert was most important in determining surface roughness and material removal rate, with feed rate as the second most significant, followed by the interaction of feed rate and type of insert. It was found that using wiper inserts allowed simultaneous increases in feed rate, cutting speed, and depth of cut, while providing better surface quality of lower Ra, compared to the global minimum value that could be achieved using the conventional insert. These findings show that wiper inserts produce better surface quality and a material removal rate up to ten times higher than that obtained with conventional inserts. This clearly indicates the tremendous advantages of high surface quality and productivity that wiper inserts can offer when compared with the conventional round nose type in precision hard turning of AISI 4340 alloy steel.

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Section: Introductionmentioning

confidence: 99%

On the Assessment of Surface Quality and Productivity Aspects in Precision Hard Turning of AISI 4340 Steel Alloy: Relative Performance of Wiper vs. Conventional Inserts

et al. 2020

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“…This kind of distribution can be regarded as an imaging thermophysical property feature. The gray intensity of corrosion component can be computed by (8); and its image feature M IPT _GI can be defined by (9).…”

Section: Preliminary Control Of Cleaning Effectmentioning

confidence: 99%

“…Many indices can be used to evaluate the processing effect of laser cleaning, the Surface Roughness (SR) [8] is one of them. The SR can reflect the smoothness degree of FIGURE 1.…”

Section: Introductionmentioning

confidence: 99%

Automatic Process Parameters Tuning and Surface Roughness Estimation for Laser Cleaning

Liu

Yang

et al. 2020

IEEE Access

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An image analysis-based two-stage process parameters tuning and Surface Roughness (SR) estimation algorithm is proposed for the laser cleaning application. A Cartesian coordinate robot is utilized to collect image and implement cleaning. Before cleaning, in order to tune the proper laser parameters, first, the environment lighting is controlled for the metal image collection. Second, lots of classification features are computed for the images above. The Gray-Level Co-occurrence Matrix (GLCM) texture features, the concavo-convex region features, the histogram symmetry difference feature, and the imaging thermophysical property features are computed. Third, the initial laser parameters are created randomly and an iteration computation is performed: a Support Vector Machine (SVM) is used to forecast the cleaning effect; its inputs include the classification features and the initial laser parameters; its output is the cleaning effect degree. If the SVM output cannot fulfill user's demand, the laser parameters will be updated randomly. This iteration will be implemented constantly until the SVM output becomes valid. Then the laser cleaning will be performed. When estimating SR for the cleaned metal, multiple image features are calculated for the images after cleaning. The features include the Tamura coarseness, some GLCM features, and the convex region feature. To improve the prediction precision, different feature combinations are used for different cleaning effects. The linear function and the 3-order polynomial function are considered for the SR estimation. After tests, the accuracies of SVM, the SR prediction function, and the integrated SR control and estimation algorithm can be 90.0%, 80.0% and 80.0% approximately.

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“…Both tool condition monitoring (TCM) and surface roughness prediction are active research domains that have been the focus of many studies (Abellan-Nebot & Romero Subirón, 2010;Benardos & Vosniakos, 2003;He et al, 2019;Kuntoğlu et al, 2021;Liang et al, 2019;Serin et al, 2020;Zhou & Xue, 2018) and several applications (Chen et al, 2018;Duo et al, 2019;Khorasani & Yazdi, 2017;Martínez-Arellano et al, 2019;Zhang et al, 2021b). However, while both affect the geometrical and dimensional quality of the workpiece, they do not represent it.…”

Section: Introductionmentioning

confidence: 99%

Predicting the quality of a machined workpiece with a variational autoencoder approach

et al. 2021

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In this article, it is shown that a machine learning approach based only on data from sensors (vibration and current consumption) can be used to predict the geometric dimensioning and tolerancing quality measurement values of machined workpieces in an industrial context. First, a methodology based on a variational autoencoder approach is used, and then a metric based on the concept of Euclidean distance and the 2D latent space produced by the variational autoencoder is proposed. The proposed variational autoencoder regression model is shown capable of predicting the quality measurement values, with a mean square error of 5.2573 × 10 −4 mm. The proposed measurement system also displays a confidence interval of ± 0.05 mm. Moreover, the resulting 2D latent space is capable of distributing and structuring data based on the quality level and of providing a quick visual support. Compared to the t-SNE method, this latent space displays a better structure. Furthermore, the proposed Euclidean distance metric is correlated to the quality level in both the predicted and observed subsets. This work is also based on an industrial dataset, thus increasing its potential for technological transfer; that in turn allows a better monitoring of the machining process, as well as the prediction of the workpiece quality.

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Soft Computing Techniques for Surface Roughness Prediction in Hard Turning: A Literature Review

Cited by 25 publications

References 62 publications

On the Assessment of Surface Quality and Productivity Aspects in Precision Hard Turning of AISI 4340 Steel Alloy: Relative Performance of Wiper vs. Conventional Inserts

On the Assessment of Surface Quality and Productivity Aspects in Precision Hard Turning of AISI 4340 Steel Alloy: Relative Performance of Wiper vs. Conventional Inserts

Automatic Process Parameters Tuning and Surface Roughness Estimation for Laser Cleaning

Predicting the quality of a machined workpiece with a variational autoencoder approach

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