scholarly journals Surface roughness estimation for FDM systems

2021 ◽  
Author(s):  
Behnam Nourghassemi
Keyword(s):  
Surface Roughness ◽  
Hybrid Model ◽  
Least Square ◽  
Experimental Results ◽  
Analytical Models ◽  
Support Vector ◽  
Empirical Estimation ◽  
Standard Format ◽  
Svm Model ◽  
Analytical Estimation

By selecting the optimal build angle, the surface roughness of rapid prototyped parts can be minimized. The objective of this study is to develop a model for estimation of surface roughness as a function of build angle and other build parameters for parts built by Fusion Deposition Modeling (FDM) machines. For that purpose, principles of the FDM technique, along with other rapid prototyping techniques, are reviewed and various standards for surface roughness measurements are introduced. Different analytical models for the estimation of surface roughness for FDM systems, which were proposed in the literature, are reviewed and reformulated in a standard format for comparison reasons. A new hybrid model is proposed for analytical estimation of the surface roughness based on experimental results and comparison of the models. In addition, Least Square Support Vector Machine (LS-SVM) is applied for an empirical estimation of the surface roughness. Robustness of the LS-SVM model is studied and its performance is compared to the hybrid model. The experimental results confirm better results for the LS-SVM model.

scholarly journals Surface roughness estimation for FDM systems

2021 ◽  
Author(s):  
Behnam Nourghassemi
Keyword(s):  
Surface Roughness ◽  
Hybrid Model ◽  
Least Square ◽  
Experimental Results ◽  
Analytical Models ◽  
Support Vector ◽  
Empirical Estimation ◽  
Standard Format ◽  
Svm Model ◽  
Analytical Estimation

By selecting the optimal build angle, the surface roughness of rapid prototyped parts can be minimized. The objective of this study is to develop a model for estimation of surface roughness as a function of build angle and other build parameters for parts built by Fusion Deposition Modeling (FDM) machines. For that purpose, principles of the FDM technique, along with other rapid prototyping techniques, are reviewed and various standards for surface roughness measurements are introduced. Different analytical models for the estimation of surface roughness for FDM systems, which were proposed in the literature, are reviewed and reformulated in a standard format for comparison reasons. A new hybrid model is proposed for analytical estimation of the surface roughness based on experimental results and comparison of the models. In addition, Least Square Support Vector Machine (LS-SVM) is applied for an empirical estimation of the surface roughness. Robustness of the LS-SVM model is studied and its performance is compared to the hybrid model. The experimental results confirm better results for the LS-SVM model.

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.

A COMPARITIVE ANALYSIS AND TIME SERIES FORECASTING OF MONTHLY STREAM FLOW DATA USING HYBRID MODEL

2015 ◽  
Vol 76 (13) ◽  
Author(s):  
Siraj Muhammed Pandhiani ◽  
Ani Shabri
Keyword(s):  
Hybrid Model ◽  
Stream Flow ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Flow Data ◽  
Indus River ◽  
Proposed Model

In this study, new hybrid model is developed by integrating two models, the discrete wavelet transform and least square support vector machine (WLSSVM) model. The hybrid model is then used to measure for monthly stream flow forecasting for two major rivers in Pakistan. The monthly stream flow forecasting results are obtained by applying this model individually to forecast the rivers flow data of the Indus River and Neelum Rivers. The root mean square error (RMSE), mean absolute error (MAE) and the correlation (R) statistics are used for evaluating the accuracy of the WLSSVM, the proposed model. The results are compared with the results obtained through LSSVM. The outcome of such comparison shows that WLSSVM model is more accurate and efficient than LSSVM.

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

2020 ◽  
pp. 095440542097106
Author(s):  
Yu Su ◽  
Congbo Li ◽  
Guoyong Zhao ◽  
Chunxiao Li ◽  
Guangxi Zhao
Keyword(s):  
Surface Roughness ◽  
Energy Consumption ◽  
Tool Wear ◽  
Specific Energy ◽  
Machine Tools ◽  
Prediction Models ◽  
Specific Energy Consumption ◽  
Cutting Parameters ◽  
Experimental Results ◽  
Support Vector

The specific energy consumption of machine tools and surface roughness are important indicators for evaluating energy consumption and surface quality in processing. Accurate prediction of them is the basis for realizing processing optimization. Although tool wear is inevitable, the effect of tool wear was seldom considered in the previous prediction models for specific energy consumption of machine tools and surface roughness. In this paper, the prediction models for specific energy consumption of machine tools and surface roughness considering tool wear evolution were developed. The cutting depth, feed rate, spindle speed, and tool flank wear were featured as input variables, and the orthogonal experimental results were used as training points to establish the prediction models based on support vector regression (SVR) algorithm. The proposed models were verified with wet turning AISI 1045 steel experiments. The experimental results indicated that the improved models based on cutting parameters and tool wear have higher prediction accuracy than the prediction models only considering cutting parameters. As such, the proposed models can be significant supplements to the existing specific energy consumption of machine tools and surface roughness modeling, and may provide useful guides on the formulation of cutting parameters.

Fault Diagnosis of Pump Based on a Hybrid HMM/SVM Model

2011 ◽  
Vol 188 ◽  
pp. 629-635
Author(s):  
Xia Yue ◽  
Chun Liang Zhang ◽  
Jian Li ◽  
H.Y. Zhu
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Model ◽  
Large Scale ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Double Layers ◽  
Support Vector ◽  
Practical Applications ◽  
Average Recognition Rate ◽  
Svm Model

A hybrid support vector machine (SVM) and hidden Markov model (HMM) model was introduced into the fault diagnosis of pump. This model had double layers: the first layer used HMM to classify preliminarily in order to get the coverage of possible faults; the second layer utilized this information to activate the corresponding SVMs for improving the recognition accuracy. The structure of this hybrid model was clear and feasible. Especially the model had the potential of large-scale multiclass application in fault diagnosis because of its good scalability. The recognition experiments of 26 statuses on the ZLH600-2 pump showed that the recognition capability of this model was sound in multiclass problems. The recognition rate of one bearing eccentricity increased from SVM’s 84.42% to 89.61% while the average recognition rate of hybrid model reached 95.05%. Although some goals while model constructed did not be fully realized, this model was still very good in practical applications.

Classification of Automobile Lubricant by Near-Infrared Spectroscopy Combined with Machine Classification

2011 ◽  
Vol 460-461 ◽  
pp. 667-672
Author(s):  
Yun Zhao ◽  
Xing Xu ◽  
Yong He
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Svm Model

The main objective of this paper is to classify four kinds of automobile lubricant by near-infrared (NIR) spectral technology and to observe whether NIR spectroscopy could be used for predicting water content. Principle component analysis (PCA) was applied to reduce the information from the spectral data and first two PCs were used to cluster the samples. Partial least square (PLS), least square support vector machine (LS-SVM), and Gaussian processes classification (GPC) were employed to develop prediction models. There were 120 samples for training set and test set. Two LS-SVM models with first five PCs and first six PCs were built, respectively, and accuracy of the model with five PCs is adequate with less calculation. The results from the experiment indicate that the LS-SVM model outperforms the PLS model and GPC model outperforms the LS-SVM model.

scholarly journals Effect of projected climate change on potential evapotranspiration in the semiarid region of central India

2020 ◽  
Author(s):  
Darshana Duhan ◽  
Dharmendra Singh ◽  
Sandeep Arya
Keyword(s):  
Potential Evapotranspiration ◽  
Central India ◽  
Least Square ◽  
Cmip5 Models ◽  
Semiarid Region ◽  
Support Vector ◽  
Pan Evaporation ◽  
Empirical Formulas ◽  
Svm Model ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract The performance of potential evapotranspiration (PET) methods such as pan evaporation (physical measurement), empirical formulas (Penman–Monteith (PM), Hargreaves and Thornthwaite) and satellite-derived PET (MOD16) were assessed in a semiarid region of central India. The satellite-based PET was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS). The comparisons between different methods were made with observed pan evaporation (ETpan) data to identify the best method for the semiarid region. Further, the future projection of PET was carried out using RCP4.5 emission scenario of seven CMIP5 models. Two approaches were applied for the projection of PET. In the first approach, RCP4.5 scenario data are directly used in the PM method, and in the second approach, these variables are used as a predictor in the calibrated and validated least square support vector machine (LS-SVM) model. The projection of PET was made using the best-identified model among PM and LS-SVM from the years 2006–2100. The results show that MOD16 and Hargreaves overestimate the PET; however, PM and Thornwaite underestimate the PET. PM based PET is closely related with ETpan and is a good indicator of ETpan in a semiarid region. GFDL-ESM2M is identified as the most skillful CMIP5 model, and results show that PET is projected to increase in future using the LS-SVM model.

Nondestructive Determination of Citric Acid Using Successive Projections Algorithm and Spectroscopic Techniques

2011 ◽  
Vol 460-461 ◽  
pp. 9-14
Author(s):  
Fei Liu ◽  
Yong He
Keyword(s):  
Citric Acid ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Square ◽  
Support Vector ◽  
Spectroscopic Techniques ◽  
Optimal Prediction ◽  
Successive Projections Algorithm ◽  
Svm Model ◽  
Successive Projections

Successive projections algorithm (SPA) combined with least square-support vector machine (LS-SVM) was investigated to determine the citric acid of lemon vinegar by 13 wavelengths within visible/near infrared (Vis/NIR) spectral region. Five concentration levels (100%, 80%, 60%, 40% and 20%) of lemon vinegar were prepared, and the calibration set consisted of 150 samples, validation set consisted of 75 samples and the remaining 75 samples for prediction set. After the comparison of different preprocessing such as smoothing, standard normal variate and derivative, SPA was applied to extract the effective wavelengths to reduce the redundancies and collinearity of variables, and the multiple linear regression (MLR) models were developed compared with partial least squares (PLS) models. Simultaneously, the selected wavelengths were used as the inputs of LS-SVM, and a new proposed combination of SPA-LS-SVM model was developed. The results indicated that SPA-LS-SVM achieved the optimal prediction performance, and the correlation coefficient (r) and root mean square error of prediction (RMSEP) were 0.9894 and 0.0623, respectively. An excellent prediction precision was obtained. The overall results demonstrated that it was feasible to utilize Vis/NIR spectroscopy to predict the citric acid of lemon vinegar, and SPA-LS-SVM model achieved the optimal prediction precision. This study supplied a feasible method for the process monitoring of fruit vinegar manufacture and fermentation.

Outlier Detection for Observational Data of Automatic Meteorological Station Based on Least Square Support Vector Machine

2013 ◽  
Vol 295-298 ◽  
pp. 945-949
Author(s):  
Zhen Meng ◽  
Shi Chang Zhang ◽  
Zeng Lin Huang
Keyword(s):  
Support Vector Machine ◽  
Observational Data ◽  
Outlier Detection ◽  
Meteorological Data ◽  
Meteorological Factor ◽  
Meteorological Station ◽  
Temperature Data ◽  
Least Square ◽  
Support Vector ◽  
Svm Model

Automatic meteorological station is important for meteorological observation and the existence of outliers in the observational data is inevitable. The paper proposes outlier detection for observational data of automatic meteorological station based on least square support vector machine (LS-SVM). The method establishes the LS-SVM model for the meteorological factor and uses the model to evaluate the observational data. If the observational data deviate from the model, they would be seemed as outliers. The ground temperature data observed by two real automatic meteorological stations are used in experiments. Experiments results verify that the proposed method realize outlier detection for observational data of automatic meteorological station effectively and ensures subsequent process and analysis of the meteorological data.

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