scholarly journals Modelling Considerations for Coupled Lines in CMOS Back-End-Of-Line at mm-Wave Frequencies

2021 ◽  
Author(s):  
Johannes Venter ◽  
Tinus Stander
Keyword(s):  
Surface Roughness ◽  
Prediction Accuracy ◽  
Fem Modelling ◽  
Metal Thickness ◽  
Coupled Lines ◽  
Marginal Improvement

<p>We investigate the effect of passivation contouring, surface roughness, and sidewall etch tapering on the FEM modelling accuracy of mm-wave couplers in CMOS BEOL. It is found that accurate passivation contouring leads to a marginal improvement of 0.15 dB in peak coupling prediction accuracy, but introducing a sidewall taper of 104ᵒ can improve the prediction of peak coupling by up to 0.37 dB. Including surface roughness of 5 % and 10 % of metal thickness did not have a notable improvement on prediction of peak coupling.</p>

scholarly journals Modelling Considerations for Coupled Lines in CMOS Back-End-Of-Line at mm-Wave Frequencies

2021 ◽  
Author(s):  
Johannes Venter ◽  
Tinus Stander
Keyword(s):  
Surface Roughness ◽  
Prediction Accuracy ◽  
Fem Modelling ◽  
Metal Thickness ◽  
Coupled Lines ◽  
Marginal Improvement

<p>We investigate the effect of passivation contouring, surface roughness, and sidewall etch tapering on the FEM modelling accuracy of mm-wave couplers in CMOS BEOL. It is found that accurate passivation contouring leads to a marginal improvement of 0.15 dB in peak coupling prediction accuracy, but introducing a sidewall taper of 104ᵒ can improve the prediction of peak coupling by up to 0.37 dB. Including surface roughness of 5 % and 10 % of metal thickness did not have a notable improvement on prediction of peak coupling.</p>

Enhanced Hybrid Model to Predict the Surface Roughness of Honed cylinder Bore

2021 ◽  
pp. 1-25
Author(s):  
Burhan Afzal ◽  
Xueping Zhang ◽  
Anil Srivastava
Keyword(s):  
Surface Roughness ◽  
Hybrid Model ◽  
Surface Texture ◽  
Prediction Accuracy ◽  
Analytical Models ◽  
Superior Performance ◽  
Functional Surface ◽  
Model Framework ◽  
Multiple Variables ◽  
Cylinder Bore

Abstract Cylinder bore honing is a finishing process that generates a crosshatch pattern with alternate valleys and plateaus responsible for enhancing lubrication and preventing gas and oil leakage in the engine cylinder bore. The required functional surface in the cylinder bore is generated by a sequential honing process and is characterized by Rk roughness parameters (Rk, Rvk, Rpk, Mr1, Mr2). Predicting the desired surface roughness relies primarily on two techniques: (i) analytical models (AM) and (ii) machine learning (ML) models. Both of these techniques offer certain advantages and limitations. AM's are interpretable as they indicate distinct mapping relation between input variables and honed surface texture. However, AM's are usually based on simplified assumptions to ensure the traceability of multiple variables. Consequently, their prediction accuracy is adversely impacted when these assumptions are not satisfied. However, ML models accurately predict the surface texture but their prediction mechanism is challenging to interpret. Furthermore, the ML models' performance relies heavily on the representativeness of data employed in developing them. Thus, either prediction accuracy or model interpretability suffers when AM and ML models are implemented independently. This study proposes a hybrid model framework to incorporate the benefits of AM and ML simultaneously. In the hybrid model, an Artificial neural network (ANN) compensates the AM by correcting its error. This retains the physical understanding built into the model while simultaneously enhancing the prediction accuracy. The proposed approach resulted in a hybrid model that significantly improved the prediction accuracy of the AM and additionally provided superior performance compared to independent ANN.

Intelligent Monitoring and Prediction of Surface Roughness in Ball-End Milling Process

2011 ◽  
Vol 121-126 ◽  
pp. 2059-2063 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Angsumalin Senjuntichai
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Prediction Accuracy ◽  
End Milling ◽  
Milling Process ◽  
Ball End Milling ◽  
Roughness Model ◽  
Force Ratio ◽  
Tool Diameter ◽  
End Milling Process

In order to realize the intelligent machines, the practical model is proposed to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The ratio of cutting force is proposed to be generalized and non-scaled to estimate the surface roughness regardless of the cutting conditions. The proposed in-process surface roughness model is developed based on the experimentally obtained data by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the cutting force ratio. The prediction accuracy and the prediction interval of the in-process surface roughness model at 95% confident level are calculated and proposed to predict the distribution of individually predicted points in which the in-process predicted surface roughness will fall. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.

Surface Roughness Prediction Based on Acoustic Emission Signals in High-Precision Diamond Turning of Rapidly Solidified Optical Aluminum Grade (RSA443)

2020 ◽  
Vol 841 ◽  
pp. 363-368
Author(s):  
Zvikomborero Hweju ◽  
Khaled Abou-El-Hossein
Keyword(s):  
Surface Roughness ◽  
Acoustic Emission ◽  
Prediction Accuracy ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Surface Roughness Prediction ◽  
Peak Rate ◽  
Signal Parameters ◽  
Roughness Prediction ◽  
Ae Signal

Acoustic emission signal-based prediction of surface roughness has been utilized widely, yet little work has been done in this regard on RSA443. This paper seeks to study the correlation between acoustic emission (AE) signal parameters and surface roughness. Estimation of surface roughness using AE signal parameters and subsequent examination of the influence of AE signal parameters (root mean square, peak rate and prominent frequency) on the accuracy of the RSM model in surface roughness prediction are carried out. The experiment is designed using the Taguchi L9 orthogonal array to minimize the number of experiments. Emitted acoustic signals are captured using a Piezotron sensor. Three RSM models are formulated and compared in this study: a model that uses only critical machining parameters (cutting speed, depth of cut and feed rate), a model that uses only AE signal parameters (root mean square, peak rate and prominent frequency) and a model that uses both critical machining parameters and AE signal parameters. An assessment based on the models’ mean absolute percentage error (MAPE) is made to see if AE signal parameters have any contribution towards surface roughness prediction accuracy. The order of parameter significance in the most accurate model is investigated in this paper. The mean absolute percentage error results for the models indicate that the model in which AE signal parameters are utilized in conjunction with critical machining parameters has the highest prediction accuracy of 97.32%. The model that utilizes only critical machining parameters has a prediction accuracy of 96.35% while the one that utilizes only AE signal parameters has a prediction accuracy of 84.43%. It is observed that the order of parameter significance from the most to the least significant is as follows: feed rate, cutting speed, peak rate, AErms, depth of cut and prominent frequency.

Prediction of Surface Quality for Silicon Carbide Wheel Grinding of Silicon Nitride

2006 ◽  
Vol 532-533 ◽  
pp. 297-300
Author(s):  
Li Ming Xu ◽  
Albert J. Shih ◽  
Bin Shen ◽  
Chun Xiang Ma ◽  
De Jin Hu
Keyword(s):  
Surface Roughness ◽  
Silicon Carbide ◽  
Silicon Nitride ◽  
Surface Quality ◽  
Prediction Accuracy ◽  
Surface Damage ◽  
Small Samples ◽  
Support Vector ◽  
Grinding Parameters ◽  
Grinding Efficiency

The experimental results for silicon carbide (SiC) wheel with fine grit size grinding of silicon carbide (Si3N4) revealed that the grinding parameters affect not only the ground silicon nitride surface roughness, but also the degree of surface damage. There exists complex non-linear relationship between the grinding parameters and surface quality. Better surface roughness doesn’t surely mean less surface damage. A method of prediction of grinding quality based on support vector regression is then presented according to the condition of small samples. The result shows the prediction accuracy based on this method is obviously higher than neural network, which provides an effective way for optimizing the grinding parameters to ensure the grinding quality as well as grinding efficiency while grinding of silicon carbide using conventional abrasive.

In-Process Monitoring and Prediction of Surface Roughness in CNC Turning Process

2011 ◽  
Vol 199-200 ◽  
pp. 1958-1966 ◽  
Author(s):  
Somkiat Tangjitsitcharoen
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Prediction Accuracy ◽  
Cutting Speed ◽  
Least Square ◽  
Depth Of Cut ◽  
Turning Process ◽  
Roughness Model ◽  
Force Ratio ◽  
Cnc Turning Process

The objective of this research is to propose a practical model to predict the in-process surface roughness during the turning process by using the cutting force ratio. The proposed in-process surface roughness model is developed based on the experimentally obtain result by employing the exponential function with six factors of the cutting speed, the feed rate, the rank angle the tool nose radius, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction accuracy of the in-process surface roughness model has been verified to monitor the in-process predicted surface roughness at 95% confident level. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It has been proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.

The Prediction of Shot Peening’s Surface Roughness with Premixed Water Jet Based on Neural Network

2010 ◽  
Vol 136 ◽  
pp. 172-175
Author(s):  
Rui Hong Wang ◽  
Chong Wang ◽  
Xiao Mei Zhang
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Surface Roughness ◽  
Aluminum Alloy ◽  
Prediction Accuracy ◽  
Scanning Speed ◽  
Water Jet ◽  
Average Error ◽  
Affecting Factors ◽  
Roughness Prediction

Shot peening’s surface roughness is an important factor affecting the effect of shot-peening. The paper selects blasting pressure, scanning speed and target distance as affecting factors in the process parameters, the shot penning test which aims at 2A11 aluminum alloy materials through applying the premixed water jet, according to test data, the paper establishes mathematical model of shot peening’s surface roughness applying neural network, and applies this model to predict shot peening’s surface roughness. The results show that the training average error of this model is small, the predicted effect is good, it can meet the requirements of shot peening’s surface roughness prediction accuracy in the industrial production, it has greater practical value.

Monitoring and Prediction of Surface Roughness in Ball End Milling with Air Blow Application

2012 ◽  
Vol 538-541 ◽  
pp. 1332-1337 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Suthas Ratanakuakangwan
Keyword(s):  
Surface Roughness ◽  
Regression Analysis ◽  
Cutting Force ◽  
Multiple Regression Analysis ◽  
Prediction Accuracy ◽  
Prediction Interval ◽  
End Milling ◽  
Cutting Condition ◽  
Ball End Milling ◽  
Roughness Model

This paper presents the additional work of the previous research in order to investigate the relations of the cutting conditions and the various air blow applications which affect the surface roughness. The suitable cutting condition is determined for the aluminum (Al6063) with the ball end milling by utilizing the response surface analysis referring to the minimum surface roughness. The cutting force is monitored during the cutting to analyze the surface roughness. The dynamometer is employed and installed on the table of 5-axis CNC maching center to measure the in-process cutting force. The models of surface roughness and cutting force are calculated by using the multiple regression analysis with the least squared method at 95% significant level. The experimentally obtained results showed that the surface roughness can be well explained by the in-process cutting force. The prediction accuracy and the prediction interval have been presented to verify the obtained surface roughness model at 95% confident level.

scholarly journals Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study

2011 ◽  
Vol 2011 ◽  
pp. 1-18 ◽  
Author(s):  
Abdel Badie Sharkawy
Keyword(s):  
Surface Roughness ◽  
Prediction Accuracy ◽  
End Milling ◽  
Milling Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Validation Data ◽  
Genetic Tuning ◽  
Inference Systems ◽  
End Milling Process

A study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered. They are (i) radial basis function neural networks (RBFNs), (ii) adaptive neurofuzzy inference systems (ANFISs), and (iii) genetically evolved fuzzy inference systems (G-FISs). The machining parameters, namely, the spindle speed, feed rate, and depth of cut have been used as inputs to model the workpiece surface roughness. The goal is to get the best prediction accuracy. The procedure is illustrated using experimental data of end milling 6061 aluminum alloy. The three networks have been trained using experimental training data. After training, they have been examined using another set of data, that is, validation data. Results are compared with previously published results. It is concluded that ANFIS networks may suffer the local minima problem, and genetic tuning of fuzzy networks cannot insureperfectoptimality unless suitable parameter setting (population size, number of generations etc.) and tuning range for the FIS, parameters are used which can be hardly satisfied. It is shown that the RBFN model has the best performance (prediction accuracy) in this particular case.

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