Study on model for cutting force when milling SCM440 steel

Thien, Nguyen Van; Trung, Do Duc

doi:10.21303/2461-4262.2021.001743

Cited by 7 publications

(7 citation statements)

References 30 publications

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“…SCM440 steel has been widely used in manufacturing parts with variable loads such as motor drives, gears, plastic injection moulds, rolling shafts and other fields due to its excellent mechanical properties and ability to resist corrosion [1,2]. However, SCM440 steel is classified as difficult-to-machine steel, causing vibrations and rapid tool wear in the machining process [3,4]. SCM440 steel processing shows the effects of different technological parameters on surface quality and vibration [5,6].…”

Section: Introductionmentioning

confidence: 99%

Combined analysis of acoustic emission and vibration signals in monitoring tool wear, surface quality and chip formation when turning SCM440 steel using MQL

Dung¹,

Thien²,

Pham³

et al. 2023

Eureka: PE

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With modern production, Minimum Quantity Lubricant (MQL) technology has emerged as an alternative to conventional liquid cooling. The MQLs is an environmentally friendly lubricant method with low cost while meeting the requirements of machining conditions. In this study, the experimental and analytical results show that the obtained acoustic emission (AE) and vibration signal components can effectively monitor various circumstances in the SCM440 steel turning process with MQL, such as surface quality and chip formation as cutting tool conditions. The AE signals showed a significant response to the tool wear processes. In contrast, the vibration signal showed an excellent ability to reflect the surface roughness during turning with MQL. The chip formation process through the cutting mode parameters (cutting speed, feed and depth of cut) was detected through analysis amplitude of the vibration components Ax, Ay and Az and the AE signal. Finally, Gaussian process regression and adaptive neuro-fuzzy inference systems (GPR-ANFIS) algorithms were combined to predict the surface quality and tool wear parameters of the MQL turning process. Tool condition monitoring devices assist the operator in monitoring tool wear and surface quality limits, stopping the machine in case of imminent tool breakage or lower surface quality. With the unique combination of AE and vibration analysis model and the training and testing samples established by the experimental data, the corresponding average prediction accuracy is 97.57 %. The highest prediction error is not more than 3.8 %, with a confidence percentage of 98 %. The proposed model can be used in industry to predict surface roughness and wear of the tools directly during turning

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Section: Introductionmentioning

confidence: 99%

Combined analysis of acoustic emission and vibration signals in monitoring tool wear, surface quality and chip formation when turning SCM440 steel using MQL

Dung¹,

Thien²,

Pham³

et al. 2023

Eureka: PE

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“…In [10], both the Box-Cox and Johnson transformations were concurrently utilized to refine the accuracy of the cutting force regression model during the milling of SCM440 steel. This investigation further illustrated that employing the Box-Cox transformation facilitated the development of the cutting force regression model with the highest accuracy, followed by the model utilizing the Johnson transformation.…”

Section: Introductionmentioning

confidence: 99%

“…and(10) forms the surface roughness regression model in the case of using the inverse transformation as shown in (13):…”

mentioning

confidence: 99%

Towards enhanced surface roughness modeling in machining: an analysis of data transformation techniques

Thinh,

Khiem,

Giang

2024

Eureka: PE

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Data transformation methods are utilized to convert datasets into non-integer formats, potentially altering their distribution patterns. This implies that the variance and standard deviation of the dataset may be altered after the dataset undergoes data transformation operations. Improving model accuracy is a primary application of these methods. This study compares the efficacy of three data transformation techniques: square root transformation, logarithmic transformation, and inverse transformation. The comparison is conducted within the context of developing a surface roughness model for a turning process. Eighteen experiments are performed using the Box-Behnken method, with surface roughness chosen as the response variable. The surface roughness dataset undergoes transformation using the mentioned methods. Four surface roughness regression models are then built: one without transformation, one with square root transformation, one with logarithmic transformation, and one with inverse transformation. Evaluation metrics include coefficient of determination (R-Sq), adjusted coefficient of determination (R-Sq(adj)), Mean Absolute Error (%MAE), and Mean Squared Error (%MSE). Results indicate logarithmic transformation as the most effective, followed by square root transformation, in enhancing model accuracy. The surface roughness model utilizing data transformation exhibits high R-Sq and R-Sq(adj) values, at 0.8792 and 0.7434 respectively. On the other hand, this model has %MAE and %MSE values of only 10.33 and 2.05 respectively. Conversely, inverse transformation exhibits the least effectiveness among the three methods

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“…In [15], it was concluded that the model employing the Johnson transformation had the highest accuracy, whereas the model without data transformation had the lowest one. In [16], both Box-Cox and Johnson transformations were identified to be equally effective in improving the accuracy of cutting force regression models when milling SCM440 steel.…”

Section: Introductionmentioning

confidence: 99%

Optimal Surface Grinding Regression Model Determination with the SRP Method

Thinh,

Dua

2024

Eng. Technol. Appl. Sci. Res.

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The construction of the regression models used to control machining processes is the objective of many experimental studies. Therefore, the effectiveness of the machining process control largely depends on the regression model’s accuracy. This study was conducted to determine the optimal regression model of surface grinding. Accordingly, eight different surface grinding regression models were constructed, including one model without data transformation and seven models that utilized various data transformations. The seven data transformations employed entailed square root transformation, logarithmic transformation, inverse transformation, exponential transformation, asinh transformation, Box-Cox transformation, and Johnson transformation. The process of determining the optimal model was carried out considering five parameters: R2, R2(adj), R2(pred) (predicted R2), MAE (Mean Absolute Error), and MSE (Mean Squared Error). SRP (Simple Ranking Process) was the optimization method followed to identify the best regression model. The Box-Cox transformation was recognized as the most accurate surface grinding regression model.

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Study on model for cutting force when milling SCM440 steel

Cited by 7 publications

References 30 publications

Combined analysis of acoustic emission and vibration signals in monitoring tool wear, surface quality and chip formation when turning SCM440 steel using MQL

Combined analysis of acoustic emission and vibration signals in monitoring tool wear, surface quality and chip formation when turning SCM440 steel using MQL

Towards enhanced surface roughness modeling in machining: an analysis of data transformation techniques

Optimal Surface Grinding Regression Model Determination with the SRP Method

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