Machining Performance Evaluation for &lt;I&gt;B&lt;/I&gt;-Type Five-Axis Machine Tool

Lee, Jeng Nan; She, Chen Hua; Chen, Shang-Liang

doi:10.1166/asl.2012.2481

Cited by 5 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the cutting process, the four sides in the XY plane of the hexahedron were milled using the side edge, and the cutting depth was in the Z direction. The processing path was generated using the Siemens NX, as displayed in Figure 2 [19,20]. A Ø 10 mm tungsten steel end mill from Chin Ming Precision Tools Co. Tainan, Taiwan, was used for the side milling.…”

Section: Experiments Proceduresmentioning

confidence: 99%

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Experiments Proceduresmentioning

confidence: 99%

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this study, we used NX software to plan the tool path and Tongtai CT-350 with a Siemens Sinumerik 840D sl controller B-Type five-axis machine tool for milling experiments. (14) The cutting tool used was a four-flute carbide end mill with a diameter of 10 mm. A Pro-micron GmbH & Co. KG sensory tool holder was used to collect the cutting force signals during machining.…”

Section: Experiments Proceduresmentioning

confidence: 99%

Intelligent Performance Prediction of Flank Milling of Ti6Al4V Using Sensory Tool Holder

Chen¹,

Lee²,

Shie³

et al. 2022

Sensors and Materials

Self Cite

View full text Add to dashboard Cite

In this study, we explore the process performance of flank-end milling of Ti-6Al-4V titanium alloy. Experiments and convolutional neural networks are used to establish a predictive model of machining quality. Sensory tool holders are used to capture the cutting force signals during machining and to perform feature extraction. The neural network model utilizes feature data as input with surface roughness and dimensional accuracy as outputs. The experimental framework can be divided into several stages: machining, cutting data collection, surface roughness and machining accuracy measurement, and neural network parameter setting. The experimental parameters consisted of cutting speed, feed per tooth, axial cutting depth, and radial cutting depth. Each parameter has three levels. Therefore, for a full-factor experiment, 81 sets of experimental data are obtained. Furthermore, 162 sets of data are obtained by performing each experiment twice. In the neural network prediction results, the minimum average percentage for surface roughness prediction error is below 10% when grouping the feed per tooth. This result was considered favorable compared with the error percentage of 18% obtained from predictions through training with all data. On the other hand, the machining accuracy prediction results were better when training with all data, with the error percentage being approximately 20%.

show abstract

“…She et al 17 divided into 10 processes. However, our design concept preferred to receive more than three groups of data through one sampling process, as it is easier for statistical computing and shows the influence of errors relevant for the analyzed manufacturing strategy (e.g.…”

Section: Configuration Of Five-axis Machine Toolmentioning

confidence: 99%

Using the direct cutting paths approach on aluminum alloy cone frustum part for evaluating a five-axis machine tool with Taguchi method

Chang

Chen

Lee

2017

Proceedings of the Institution of Mechanical Engineers, Part B:

Self Cite

View full text Add to dashboard Cite

It is difficult to compare five-axis machine tools complex analysis against independent motion of multi-type machines; more specifically, there has been no breakthrough involving the interaction effect factors from cutting analysis. Therefore, the defining and quantifying of data are important in assessing the overall performance of five-axis machine tools, and allow for the evaluation of each subsequent interaction motion analysis. Among various cutting test models, the machining of a cone frustum as specified in National Aerospace Standard 979 and International Standard Organization 10791-7 is widely accepted as a performance test standard for five-axis machining centers. Although it gives a demonstration of the machine’s machining performance, it is generally difficult to identify the overall effects of kinematic interaction within the profile of the finished workpiece. This is new approach to a previously defined 10 direct cutting paths method to evaluate the performance of each cone frustum motion, and it is not limited to only evaluating single direction or displacement on a five-axis machine tool. Among the 10 cutting paths, four of them are real five-axis cutting and the remaining paths are non-real five-axis cutting. This allowed the test of four to five mixed external forces at the same time, which the instrument is not able to measure. This article further proposes to calculate the factorial effect of interaction, based on the Taguchi method signal-to-noise ratio, mechanical advantage, and Variables separable model, which allows for the comparison of the performance of different five-axis machine tool types.

show abstract

Machining Performance Evaluation for <I>B</I>-Type Five-Axis Machine Tool

Cited by 5 publications

References 0 publications

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

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

Intelligent Performance Prediction of Flank Milling of Ti6Al4V Using Sensory Tool Holder

Using the direct cutting paths approach on aluminum alloy cone frustum part for evaluating a five-axis machine tool with Taguchi method

Contact Info

Product

Resources

About