Optimization of process parameters in plastic injection molding for minimizing the volumetric shrinkage and warpage using radial basis function (RBF) coupled with the k-fold cross validation technique

2019 ◽  
Vol 39 (5) ◽  
pp. 481-492 ◽  
Author(s):  
Behzad Shiroud Heidari ◽  
Amin Hedayati Moghaddam ◽  
Seyed Mohammad Davachi ◽  
Shadi Khamani ◽  
Afshar Alihosseini
Keyword(s):  
Injection Molding ◽  
Radial Basis Function ◽  
Process Parameters ◽  
Basis Function ◽  
Cross Validation ◽  
Volumetric Shrinkage ◽  
Process Conditions ◽  
Radial Basis ◽  
Validation Technique ◽  
Fold Cross Validation

Abstract In this study, a multi-objective design optimization method based on a radial basis function (RBF) model was applied to minimize the volumetric shrinkage and warpage of hip liners as an injection-molded biomedical part. The hip liners included an ultrahigh molecular weight polyethylene (UHMWPE) liner and UHMWPE reinforced with a nano-hydroxyapatite (nHA) liner. The shrinkage and warpage values of the hip liners were generated by simulation of the injection molding process using Autodesk Moldflow. The RBF model was used to build an approximate function relationship between the objectives and the process parameters. The process parameters, including mold temperature, melt temperature, injection time, packing time, packing pressure, coolant temperature, and type of liner, were surveyed to find the interaction effects of them on the shrinkage and warpage of the liners. The results indicated that the addition of nHA helps the liners to obtain more dimensional stability. The model was validated by the k-fold cross validation technique. Finally, the model revealed the optimal process conditions to achieve the minimized shrinkage and warpage simultaneously for various weights.

scholarly journals Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification

2021 ◽  
Vol 10 (1) ◽  
pp. 60
Author(s):  
Glori Stephani Saragih ◽  
Sri Hartini ◽  
Zuherman Rustam
Keyword(s):  
Radial Basis Function ◽  
Kernel Function ◽  
Basis Function ◽  
Cross Validation ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Radial Basis ◽  
Rbf Kernel ◽  
Rbf Kernel Function ◽  
Fold Cross Validation

<span id="docs-internal-guid-10508d4e-7fff-5011-7a0e-441840e858c8"><span>This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indonesia, consisting of 89 hepatitis-B and 31 hepatitis-C samples. The data were analyzed using several cases of k-fold cross-validation, and the performances were compared according to their accuracy, sensitivity, precision, F1-Score, and running time. From the experiments, it was concluded that fuzzy kernel k-medoids using RBF kernel function is better compared to polynomial kernel function with the 6% increment of accuracy, 13% enhancement of sensitivity, and 5% improvement in F1-Score. On the other side, the precision of fuzzy kernel k-medoids using polynomial kernel function is 2% higher than using the RBF kernel function. According to the results, the use of RBF or polynomial kernel function in fuzzy kernel medoids can be considered according to the primary goal of the classification.</span></span>

scholarly journals Radial Basis Function Pseudospectral Method for Solving Standard Fitzhugh-Nagumo Equation

2020 ◽  
Vol 5 (6) ◽  
pp. 1488-1497
Author(s):  
Geeta Arora ◽  
Gurpreet Singh Bhatia
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Shape Parameter ◽  
Cross Validation ◽  
Pseudospectral Method ◽  
Mesh Free ◽  
Radial Basis ◽  
Nagumo Equation ◽  
The Stability ◽  
Leave One Out

In this article, a pseudospectral approach based on radial basis functions is considered for the solution of the standard Fitzhugh-Nagumo equation. The proposed radial basis function pseudospectral approach is truly mesh free. The standard Fitzhugh-Nagumo equation is approximated into ordinary differential equations with the help of radial kernels. An ODE solver is applied to solve the resultant ODEs. Shape parameter which decides the shape of the radial basis function plays a significant role in the solution. A cross-validation technique which is the extension of the statistical approach leave-one-out-cross-validation is used to find the shape parameter value. The presented method is demonstrated with the help of numerical results which shows a good understanding with the exact solution. The stability of the proposed method is demonstrated with the help of the eigenvalues method numerically.

An Active Learning Approach For Radial Basis Function Neural Networks

2012 ◽  
Author(s):  
S. S. Abdullah ◽  
J. C. Allwright
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Active Learning ◽  
Radial Basis Function ◽  
Model Reduction ◽  
Basis Function ◽  
Cross Validation ◽  
Experiment Design ◽  
New Approach ◽  
Radial Basis

Kertas kerja ini membentangkan satu kaedah Pembelajaran Aktif yang baru untuk melatih Jaringan Saraf Buatan ( JSB) yang berasaskan Fungsi Asas Jejarian (FAJ) apabila JSB tersebut digunakan untuk menyelesaikan masalah Penurunan Model. Kaedah baru ini berasaskan andaian bahawa data yang diperlukan, y, pada input x, berada dalam sebuah set di mana F(x) boleh dibentuk menggunakan pengalaman atau pengetahuan awal tentang satu masalah. Kaedah baru ini akan mendapatkan lokasi data baru dengan meminimumkan ralat kes paling buruk antara keluaran JSB dengan had data seperti yang telah ditakrifkan oleh set F(x). Adalah didapati bahawa kaedah yang dicadangkan ini mampu memberikan kedudukan data baru yang baik pada kes-kes tertentu, berbanding dengan data yang diperolehi daripada kaedah sedia ada. Hasil kajian perbandingan antara kaedah yang dicadangkan dengan kaedah yang sedia ada juga disertakan dalam kertas kerja ini yang menunjukkan bahawa kaedah pembelajaran aktif yang dicadangkan merupakan satu penambahan yang baik kepada kaedah pembelajaran aktif yang sedia ada seperti kaedah reka bentuk maksimum minimum atau kaedah cross validation. Kata kunci: Jaringan saraf buatan, fungsi asas jejarian, penurunan model, kaedah pembelajaran aktif, reka bentuk eksperimen, metamodel This paper presents a new Active Learning algorithm to train Radial Basis Function (RBF) Artificial Neural Networks (ANN) for model reduction problems. The new approach is based on the assumption that the unobserved training data y at input x, lies within a set where F(x) is known from experience or past simulations. The new approach finds the location of the new sample such that the worst case error between the output of the resulting RBF ANN and the bounds of the unknown data as specified by F(x) is minimized. This paper illustrates the new approach for the case when . It was found that it is possible to find a good location for the new data sample by using the suggested approach in certain cases. A comparative study was also done indicating that the new experiment design approach is a good complement to the existing ones such as cross validation design and maximum minimum design. Key words: Artificial neural networks, radial basis functions, model reduction, active learning, experiment design, metamodeling

scholarly journals Parameter estimation of support vector machine with radial basis function kernel using grid search with leave-p-out cross validation for classification of motion patterns of subviral particles

2021 ◽  
Vol 7 (2) ◽  
pp. 121-124
Author(s):  
Ricardo M. Schuhmann ◽  
Andreas Rausch ◽  
Thomas Schanze
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Particle Motion ◽  
Cross Validation ◽  
Support Vector ◽  
Grid Search ◽  
Motion Patterns ◽  
Radial Basis ◽  
Subviral Particle

Abstract The classification of subviral particle motion in fluorescence microscopy video sequences is relevant to drug development. This work introduces a method for estimating parameters for support vector machines (SVMs) with radial basis function (RBF) kernels using grid search with leave-pout cross-validation for classification of subviral particle motion patterns. RBF-SVM was trained and tested with a large number of combinations of expert-evaluated training and test data sets for different RBF-SVM parameters using grid search. For each subtest, the mean and standard deviation of the accuracy of the RBF-SVM were calculated. The RBF-SVM parameters are selected according to the optimal accuracy. For the optimal parameters, the accuracy is 89% +- 13% for N = 100. Using the introduced computer intensive machine learning parameter adjustment method, an RBF-SVM has been successfully trained to classify the motion patterns of subviral particles into chaotic, moderate and linear movements.

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