A general regression neural network approach for the evaluation of compressive strength of FDM prototypes

Panda, Biranchi; Bahubalendruni, M. V. A. Raju; Biswal, Bibhuti Bhusan

doi:10.1007/s00521-014-1788-5

Cited by 76 publications

(32 citation statements)

References 41 publications

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“…This SI theory was studied mainly under categories of linear-nonlinear [9] and evolutionary SI [10,11] (Table 1). Among these categories, evolutionary SI approach of genetic programming (GP) can be a potential alternative because, unlike linear and non-linear SI, it automates the formulation of diversified model structures based on only the limited information about the FDM process [12][13][14][15]. However, the functioning of the evolutionary SI approach of GP suffers from the problem of generalization due to the inappropriate complexity term and objective function used in its framework.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of warping characteristic of fused deposition modelling process

Panda

Shankhwar

Garg

et al. 2016

Int J Adv Manuf Technol

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In recent years, fused deposition modelling (FDM) is gaining more popularity due to its distinct advantages in terms of cost-effectiveness, lower build times, and flexibility. Compared to other 3-D printing processes such as SLS, this process does not use any kind of high-intensity laser power to build functional parts out of CAD models and, hence, makes the process much simpler, cheaper, and adaptable. Past studies reveal that productivity of the FDM process can be further increased by effectively controlling its process parameters such as layer thickness, part orientation, extrusion temperature, and so on. In this regard, many authors have investigated the optimal parameter settings for improving part strength, surface finish, wear, and fatigue properties of FDM made prototypes. However, warping performance behavior has got very recent attention due to complex heat transfer mechanism involved during this process. Experimental investigations are necessary to understand the deformation behavior of prototypes. In addition, the quantification and optimization of warp deformation along with dimensional error poses a challenging multi-objective optimization problem. Therefore, this work proposes an evolutionary system identification (SI) approach to explicitly quantify the warp deformation and dimensional error based on the four inputs such as line width compensation, extrusion velocity, filling velocity, and layer thickness of FDM prototypes. The two models' performance analysis comprising of error metrics evaluation, cross-validation, and hypothesis tests is performed to validate its robustness. The analysis concluded that the layer thickness and extrusion velocity influence the warp deformation and, while filling velocity and line width compensation, influences the dimensional error the most.

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Section: Introductionmentioning

confidence: 99%

Performance evaluation of warping characteristic of fused deposition modelling process

Panda

Shankhwar

Garg

et al. 2016

Int J Adv Manuf Technol

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“…General regression neural network (GRNN) is a type of radial basis function (RBF) network proposed by D. F. Specht in 1991 [37]. It is a powerful tool to estimate continuous variables [38][39][40][41] even when the training data is few. A general regression neural network consists of 4 layers: an input layer, a pattern layer, a summation layer and an output layer as shown in Fig.2.…”

Section: B General Regression Neural Networkmentioning

confidence: 99%

Quantum Lyapunov control with machine learning

Hou

2019

Quantum Inf Process

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Quantum state engineering is a central task in Lyapunov-based quantum control. Given different initial states, better performance may be achieved if the control parameters, such as the Lyapunov function, are individually optimized for each initial state, however, at the expense of computing resources. To tackle this issue, we propose an initial-state-adaptive Lyapunov control strategy with machine learning, specifically, artificial neural networks trained through supervised learning. Two designs are presented and illustrated where the feedforward neural network and the general regression neural network are used to select control schemes and design Lyapunov functions, respectively. Since the sample generation and the training of neural networks are carried out in advance, the initial-state-adaptive Lyapunov control can be implemented without much increase of computational resources.

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“…These features make GRNN a very efficient tool for constructing predictors for the desired variables. of their flexible network structures, high fault tolerances, and robustness [39][40][41][42][43][44][45][46]. Additionally, the primary benefit of GRNN over a traditional back propagation neural network is its ability to obtain a satisfying fit of the data rapidly, with only a few training samples available, and without additional parameter inputs [39,44].…”

Section: Methods For Extracting the Spatial Distribution Of Winter Flmentioning

confidence: 99%

“…With pairs of feature values (TCTW and TVDI) derived from the Landsat images and θ v measurements were obtained, the TCTW and TVDI indices were introduced as the regressors with θ v as the output in an advanced machine learning algorithm, the general regression neural network (GRNN) [38,39]. Over the past decade, probabilistic neural networks like GRNN have been widely used for solving nonlinear problems and preforming predictions because of their flexible network structures, high fault tolerances, and robustness [39][40][41][42][43][44][45][46]. Additionally, the primary benefit of GRNN over a traditional back propagation neural network is its ability to obtain a satisfying fit of the data rapidly, with only a few training samples available, and without additional parameter inputs [39,44].…”

Section: Methods For Extracting the Spatial Distribution Of Winter Flmentioning

confidence: 99%

Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies

Zhao

et al. 2016

Remote Sensing

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Dynamic monitoring of the spatial pattern of winter continuously flooded paddies (WFP) at regional scales is a challenging but highly necessary process in analyzing trace greenhouse gas emissions, water resource management, and food security. The present study was carried out to demonstrate the feasibility of extracting the spatial distribution of WFP through time series imagery of volumetric surface soil moisture content (θ v ) at the field scale (30 m). A trade-off approach based on the synergistic use of tasseled cap transformation wetness and temperature vegetation dryness index was utilized to obtain paddy θ v . The results showed that the modeled θ v was in good agreement with in situ measurements. The overall correlation coefficient (R) was 0.78, with root-mean-square ranging from 1.96% to 9.96% in terms of different vegetation cover and surface water status. The lowest error of θ v estimates was found to be restricted at the flooded paddy surface with moderate or high fractional vegetation cover. The flooded paddy was then successfully identified using the θ v image with saturated moisture content thresholding, with an overall accuracy of 83.33%. This indicated that the derived geospatial dataset of WFP could be reliably applied to fill gaps in census statistics.

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A general regression neural network approach for the evaluation of compressive strength of FDM prototypes

Cited by 76 publications

References 41 publications

Performance evaluation of warping characteristic of fused deposition modelling process

Performance evaluation of warping characteristic of fused deposition modelling process

Quantum Lyapunov control with machine learning

Estimating Soil Moisture with Landsat Data and Its Application in Extracting the Spatial Distribution of Winter Flooded Paddies

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