scholarly journals A Hybrid Approach for Noise Reduction in Acoustic Signal of Machining Process Using Neural Networks and ARMA Model

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8023
Author(s):  
Tayyab Zafar ◽  
Khurram Kamal ◽  
Senthan Mathavan ◽  
Ghulam Hussain ◽  
Mohammed Alkahtani ◽  
...  
Keyword(s):  
Neural Network ◽  
Background Noise ◽  
Manufacturing Systems ◽  
Signal Reconstruction ◽  
Research Work ◽  
Hybrid Approach ◽  
Arma Model ◽  
Machining Process ◽  
Depth Of Cut ◽  
Milling Machine

Intelligent machining has become an important part of manufacturing systems because of the increased demand for productivity. Tool condition monitoring is an integral part of these systems. Airborne acoustic emission from the machining process is a vital indicator of tool health, however, it is highly affected by background noise. Reducing the background noise helps in developing a low-cost system. In this research work, a feedforward neural network is used as an adaptive filter to reduce the background noise. Acoustic signals from four different machines in the background are acquired and are introduced to a machining signal at different speeds and feed-rates at a constant depth of cut. These four machines are a three-axis milling machine, a four-axis mini-milling machine, a variable speed DC motor, and a grinding machine. The backpropagation neural network shows an accuracy of 75.82% in classifying the background noise. To reconstruct the filtered signal, a novel autoregressive moving average (ARMA)-based algorithm is proposed. An average increase of 71.3% in signal-to-noise ratio (SNR) is found before and after signal reconstruction. The proposed technique shows promising results for signal reconstruction for the machining process.

scholarly journals OPTIMASI PARAMETER PERMESINAN TERHADAP WAKTU PROSES PADA PEMROGRAMAN CNC MILLING DENGAN BERBASIS CAD/CAM

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
I G.N.K. Yudhyadi ◽  
Tri Rachmanto ◽  
Adnan Dedy Ramadan
Keyword(s):  
Cutting Speed ◽  
Operation Time ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Milling Machine ◽  
Cnc Milling ◽  
Cnc Milling Machine ◽  
Machining Parameter

Milling process is one of many machining processes for manufacturing component. The length of time in the process of milling machining is influenced by selection and design of machining parameters including cutting speed, feedrate and depth of cut. The purpose of this study to know the influence of cutting speed, feedrate and depth of cut as independent variables versus operation time at CNC milling process as dependent variables. Each independent variable consists of three level of factors; low, medium and high.Time machining process is measured from operation time simulation program, feed cut length and rapid traverse length. The results of statistically from software simulation MasterCam X Milling, then do comparison to CNC Milling machine.  The data from experiments was statistical analyzed by Anova and Regression methods by software minitab 16.Results show that the greater feedrate and depth of cut shorten the operation time of machinery, whereas cutting speed is not significant influence. Depth of cut has the most highly contribution with the value of 49.56%, followed by feedrate 43% and cutting speed 0.92%. Optimal time of machining process total is 71.92 minutes, with machining parameter on the condition cutting speed is 75360 mm/minutes, feedrate is 800 mm/minutes and depth of cut = 1 mm. Results of comparison time machining process in software Mastercam X milling with CNC Milling machine indicates there is difference not significant with the value of 0,35%.

scholarly journals Accurate and Efficient Differentiation Between Normal and Epileptic Seizure of Eyes Using 13 Layer Convolution Neural Network

2021 ◽  
Vol 38 (4) ◽  
pp. 1161-1169
Author(s):  
Veeramosu Priyanka Brahmaiah ◽  
Yarlagadda Padma Sai ◽  
Mahendra N. Giri Prasad
Keyword(s):  
Neural Network ◽  
Epileptic Seizure ◽  
Background Noise ◽  
Research Work ◽  
Convolution Neural Network ◽  
Normal Brain ◽  
Improved Outcomes ◽  
Dynamic Time ◽  
Dynamic Time Wrapping ◽  
Optimal Feature

Epileptic seizure is one which affects the normal brain activities of human being and considered to be a risky disease. The eye ball movement signals pattern plays a significant role in determining the epileptic seizure in precise manner. In addition to it, EOG signals has its influence in detecting epileptic seizure through assessment of eye ball movement signals precisely. Detecting Epilepsy using genetical based Convolutional Neural Network plays a major role in the previous research works. Conversely, the existence of background noise on eye ball signals may impact on the outcome failure. Noise aware Epileptic Seizure Detection using Thirteen Layer Convolution Neural Network (NESD-TLCNN) is adopted in this research to mitigate this issue and thereby ensuring the prediction rate more precisely. Furthermore, Hybrid Dynamic Time Wrapping based Hidden Markov Model (HDWT-HMM) is greatly utilized for primary background noise detection and removal by estimating the noise depending on distance metric. Once after the completion of noise estimation, perfect detection of epileptic seizure is accomplished using feature extraction. The peculiar features involved are saccade, fixation and blink features. Subsequently, Particle swarm optimization (PSO) technique is also involved in this research for optimal feature selection. Thirteen Layer Convolution Neural Network (TLCNN) is applied at last for learning and differentiation of epileptic seizure from the normal eyes. This research is being carried out in MATLAB platform which also reveals that the anticipated methodology produces improved outcomes when contrasted with the existing research work.

Optimizing the Machining Parameters in Glass Grinding Operation on the CNC Milling Machine for Best Surface Roughness

2010 ◽  
Vol 154-155 ◽  
pp. 721-726 ◽  
Author(s):  
Mohd Sayuti ◽  
Ahmed Aly Diaa Mohammed Sarhan ◽  
Mohd Hamdi Bin Abd Shukor
Keyword(s):  
Surface Roughness ◽  
Machining Process ◽  
Depth Of Cut ◽  
Response Analysis ◽  
Machining Parameters ◽  
Milling Machine ◽  
Cnc Milling ◽  
Cnc Machine ◽  
Cnc Milling Machine ◽  
Tool Diameter

Glass is one of the most difficult materials to be machined due to its brittle nature and unique structure such that the fracture is often occurred during machining and the surface finish produced is often poor. CNC milling machine is possible to be used with several parameters making the machining process on the glass special compared to other machining process. However, the application of grinding process on the CNC milling machine would be an ideal solution in generating special products with good surface roughness. This paper studies how to optimize the different machining parameters in glass grinding operation on CNC machine seeking for best surface roughness. These parameters include the spindle speed, feed rate, depth of cut, lubrication mode, tool type, tool diameter and tool wear. To optimize these machining parameters in which the most significant parameters affecting the surface roughness can be identified, Taguchi optimization method is used with the orthogonal array of L8(26). However, to obtain the most optimum parameters for best surface roughness, the signal to noise (S/N) response analysis and Pareto analysis of variance (ANOVA) methods are implemented. Finally, the confirmation test is carried out to investigate the improvement of the optimization. The results showed an improvement of 8.91 % in the measured surface roughness.

A neural network based approach for background noise reduction in airborne acoustic emission of a machining process

2017 ◽  
Vol 31 (7) ◽  
pp. 3171-3182 ◽  
Author(s):  
T. Zafar ◽  
K. Kamal ◽  
Z. Sheikh ◽  
S. Mathavan ◽  
U. Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Noise Reduction ◽  
Background Noise ◽  
Machining Process

scholarly journals Optimization of Turning Parameter of Composite Materials Using Response Surface Method in Minitab

2021 ◽  
Author(s):  
S. S Kulkarni ◽  
Sarika Sharma
Keyword(s):  
Surface Roughness ◽  
Composite Material ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Research Work ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Rsm Optimization

This paper represents the optimization method utilized in machining process for figuring out the most advantageous manner design. Typically, the technique layout parameters in machining procedures are noticeably few turning parameters inclusive of reducing velocity, feed and depth. The optimization of speed, feed depth of cut is very tough because of several other elements associated with processing situations and form complexities like surface Roughness, material removal rate (MRR) that are based Parameters. On this task a new fabric glass fibre composite is introduced through which could lessen costing of manufacturing and time and additionally it will boom the technique of productiveness. Composite substances have strength, stiffness, light weight, which gives the large scope to engineering and technology. The proposed research work targets to analyze turning parameters of composite material. The machining parameters are very important in manufacturing industries. The present research work is optimized surface roughness of composite material specifically in turning procedure with the aid of changing parameter including intensity of reduce, slicing velocity and feed price and additionally expect the mechanical houses of composite material. The RSM optimization is important because it evaluates the effects of multiple factors and their interactions on one or more responsive variables. It is observed that the material removal rate increases and surface roughness decreases as per the increase of Spindle speed and feed rate.

Modeling of Kerf Profile Generated in Multi-Layered Laminate Composites with Abrasive Waterjet

2016 ◽  
Vol 874 ◽  
pp. 219-224
Author(s):  
Ngangkham Peter Singh ◽  
D.S. Srinivasu ◽  
N. Ramesh Babu
Keyword(s):  
Analytical Model ◽  
Research Work ◽  
Machining Process ◽  
Abrasive Waterjet ◽  
Depth Of Cut ◽  
Laminate Composites ◽  
Metal Rubber ◽  
Experimental Trials ◽  
First Time

Kerf profile generated by abrasive waterjet (AWJ) machining process has always been an interesting area as it dictates the quality of the part. However, due to the non-deterministic nature of the process, it is a challenging task to predict it. On the other hand, understanding and controlling the kerf profile in multi-layered structures (MLSs) is a further difficult task as various layers made of different materials respond to erosion in a different manner and results in a completely different kerf shape (barrel or x-shaped kerf profile) due to the material removal mechanisms dependency on the material property of the specific layers, jet divergence and position of specific layer. Therefore, it is important to understand and develop predictive models of resulting kerf profile in MLSs so that they can be used in controlling the accuracy of the resulting kerf which in turn dictates the final part accuracy. The attempts in this direction are very limited although some modeling efforts are reported in homogeneous materials (metals, ceramics). For the first time, an analytical model for predicting the kerf profile generated in MLS machining with AWJ was presented in this research work. Discretized form of Hashish model was used for determining depth of cut. The effect of jet divergence from the experimentally obtained values, upon passing through the upper layer has been considered. The developed predictive model was validated by the kerf shapes obtained from the experimental trials on metal-adhesive-rubber MLS. Kerf profiles obtained from the simulations have captured the resulted convergent-divergent (X-shaped) profile, while cutting metal-rubber laminate composite, effectively. Furthermore, the effectiveness of the proposed analytical model was demonstrated by generating the various kerf shapes generated at various jet traverse rates.

scholarly journals Influence of Cutting Parameters to Surface Area Roughness in Dimple Machining Using Milling Process

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
M.A. Hanafiah ◽  
A.A. Aziz ◽  
A.R. Yusoff
Keyword(s):  
Surface Area ◽  
Feed Rate ◽  
Research Work ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
End Mill ◽  
Ball End Mill

Surface quality is among the predominant criterion in measuring machining process performance, including milling. It is extremely dependent on the process variable, such as cutting parameters and cutting tool conditions. The main intention of this research work is to study the effect of the milling machining parameters, including depth of cut, spindle speed, feed rate as well as machining pattern to the final surface area roughness of the fabricated dimple structure. The concave profile of the dimple is machined at the right angle to a flat Al6061 specimen using a ball end mill attached to a 3-axis CNC milling machine, and the surface area of the concave profile is measured using 3D measuring laser microscope. It is observed that surface area roughness reacts with the spindle speed and feed rate with different tool sizes. Based on the result gained, the work has successfully characterised the influence of studied milling parameters on the dimple surface area roughness, where within the range of the studied parameter, the surface area roughness varies only less than 2.2 μm. The research work will be continued further on the incline milling technique and micro size ball end mill.

Optimization of milling process parameters for energy saving and surface roughness

2019 ◽  
Vol 70 (3) ◽  
pp. 173-183
Author(s):  
Pham, Quoc-Hoang ◽  
Dang Xuan-Phuong ◽  
Doan, Tat-Khoa ◽  
Le Xuan-Hung ◽  
Thi Lan-Huong Luong ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
H13 Steel ◽  
Technical Parameters ◽  
Desirability Approach ◽  
Aisi H13

Improving the technical parameters of the machining process is an effective solution to save manufacturing costs. The purpose of this work is to decrease energy consumption (EC) and average surface roughness(ASR) for the milling process of AISI H13 steel. The spindle speed (S), depth of cut (a), and feed rate (f) were the processing inputs. The milling runs were performed using the experimental plan generated by the Box-Behnken method approach. The relationships between inputs and outputs were established using the response surface models (RSM). The desirability approach (DA) was used to observe the optimal values. The results showed that the reductions of EC and ASR are approximately 33.75% and 40.58%, respectively, as compared to the initial parameter setting. In addition, a hybrid approach using RSM and DA can be considered as a powerful solution to model the milling process and obtain a reliable optimal solution.

scholarly journals Optimization of milling process parameters for energy saving and surface roughness

2019 ◽  
Vol 70 (3) ◽  
pp. 173-183
Author(s):  
Pham, Quoc-Hoang ◽  
Dang Xuan-Phuong ◽  
Doan, Tat-Khoa ◽  
Le Xuan-Hung ◽  
Thi Lan-Huong Luong ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
H13 Steel ◽  
Technical Parameters ◽  
Desirability Approach ◽  
Aisi H13

Improving the technical parameters of the machining process is an effective solution to save manufacturing costs. The purpose of this work is to decrease energy consumption (EC) and average surface roughness(ASR) for the milling process of AISI H13 steel. The spindle speed (S), depth of cut (a), and feed rate (f) were the processing inputs. The milling runs were performed using the experimental plan generated by the Box-Behnken method approach. The relationships between inputs and outputs were established using the response surface models (RSM). The desirability approach (DA) was used to observe the optimal values. The results showed that the reductions of EC and ASR are approximately 33.75% and 40.58%, respectively, as compared to the initial parameter setting. In addition, a hybrid approach using RSM and DA can be considered as a powerful solution to model the milling process and obtain a reliable optimal solution.

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