Optimization Technique For Surface Roughness Prediction in Turning Operation

Ojha, Gaurav; Yadav, Gyanendra Kumar; Yadav, Pankaj

doi:10.18090/samriddhi.v6i2.1562

Cited by 2 publications

(2 citation statements)

References 28 publications

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“…A lot of work happened to optimize tool life and surface roughness. Govindan P, et.al, [1] and Gaurav Kumar Ojha, et.al [2] performed a detailed literature survey on the use of Taguchi technique for surface quality optimization during turning. M Abbas, et.al., [3] in their research paper, performed optimization of machining parameters namely depth of cut, cutting speed, feed rate and nose radius of the tool for achieving objectives such as maximum material removal rate and maximum surface finishing.…”

Section: Introductionmentioning

confidence: 99%

Low Cost Vibration Measurement and Optimization during Turning Process

Shankar¹,

Chand²,

Rao³

et al. 2018

AMR

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During machining any material, vibrations play a major role in deciding the life of the cutting tool as well as machine tool. The magnitude acceleration of vibrations is directly proportional to the cutting forces. In other words, if we are able to measure the acceleration experienced by the tool during machining, we can get a sense of force. There are many commercially available, pre-calibrated accelerometer sensors available off the shelf. In the current work, an attempt has been made to measure vibrations using ADXL335 accelerometer. This accelerometer is interfaced to computer using Arduino. The measured values are then used to optimize the machining process. Experiments are performed on Brass. During machining, it is better to have lower acceleration values. Thus, the first objective of the work is to minimize the vibrations. Surface roughness is another major factor which criterion “lower is the better” applies. In order to optimize the values, a series of experiments are conducted with three factors, namely, tool type (2 levels), Depth of cut (3 levels) and Feed are considered (3 levels). Mixed level optimization is performed using Taguchi analysis with L18 orthogonal array. Detailed discussion of the parameters shall be given in the article.

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Section: Introductionmentioning

confidence: 99%

Low Cost Vibration Measurement and Optimization during Turning Process

Shankar¹,

Chand²,

Rao³

et al. 2018

AMR

View full text Add to dashboard Cite

show abstract

“…However, due to the complexity of the cutting, the ideal cutting conditions can only be achieved in laboratories or in theoretical analysis, resulting in difficulty in building an effective prediction model for real-world milling. We must obtain the assumed parameters of the model through many experiments and use various optimization techniques to improve the model in order to set the cutting conditions [1].…”

Section: Introductionmentioning

confidence: 99%

Applying a Neural Network to Predict Surface Roughness and Machining Accuracy in the Milling of SUS304

et al. 2023

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Surface roughness and machining accuracy are essential indicators of the quality of parts in milling. With recent advancements in sensor technology and data processing, the cutting force signals collected during the machining process can be used for the prediction and determination of the machining quality. Deep-learning-based artificial neural networks (ANNs) can process large sets of signal data and can make predictions according to the extracted data features. During the final stage of the milling process of SUS304 stainless steel, we selected the cutting speed, feed per tooth, axial depth of cut, and radial depth of cut as the experimental parameters to synchronously measure the cutting force signals with a sensory tool holder. The signals were preprocessed for feature extraction using a Fourier transform technique. Subsequently, three different ANNs (a deep neural network, a convolutional neural network, and a long short-term memory network) were applied for training in order to predict the machining quality under different cutting conditions. Two training methods, namely whole-data training and training by data classification, were adopted. We compared the predictive accuracy and efficiency of the training process of these three models based on the same training data. The training results and the measurements after machining indicated that in predicting the surface roughness based on the feed per tooth classification, all the models had a percentage error within 10%. However, the convolutional neural network (CNN) and long short-term memory (LSTM) models had a percentage error of 20% based on the whole-data training, while that of the deep neural network (DNN) model was over 50%. The percentage error for the machining accuracy prediction based on the whole-data training of the DNN and CNN models was below 10%, while that of the LSTM model was as large as 20%. However, there was no significant improvement in the results of the classification training. In all the training processes, the CNN model had the best analytical efficiency, followed by the LSTM model. The DNN model performed the worst.

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Optimization Technique For Surface Roughness Prediction in Turning Operation

Abstract: ABSTRACT

Cited by 2 publications

References 28 publications

Low Cost Vibration Measurement and Optimization during Turning Process

Low Cost Vibration Measurement and Optimization during Turning Process

Applying a Neural Network to Predict Surface Roughness and Machining Accuracy in the Milling of SUS304

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