Adaptive adjustment of plastic injection processes based on neural network

Liao, Xiaoping; Xie, Hong-Xia; Zhou, Yongquan; Xia, Wei

doi:10.1016/j.jmatprotec.2006.11.068

Cited by 12 publications

(13 citation statements)

References 5 publications

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“…11 Considering the difficulties involved in biotechnological processes, the main difficulty in model-based techniques for definition of operational strategies and optimization in the enzymatic hydrolysis of sugarcane bagasse is the problem of obtaining an accurate model to aid in the decision making process. Although DOEs provide understanding about the process and a reliable measurement of its parameters, 12 practical experience has shown that the behavior of enzymatic reactions with varying enzymes loading is extremely complex and hence difficult to handle statistically. 13,14 Artificial intelligence, such as artificial neural networks (ANN), has been used successfully for solving biotechnological complex problems related to the field of modeling and optimization in order to achieve high operational performance.…”

Section: Introductionmentioning

confidence: 99%

Enzymatic hydrolysis of sugarcane bagasse for bioethanol production: determining optimal enzyme loading using neural networks

Rivera

Rabelo

Garcia

et al. 2010

J of Chemical Tech & Biotech

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BACKGROUND: The efficient production of a fermentable hydrolyzate is an immensely important requirement in the utilization of lignocellulosic biomass as a feedstock in bioethanol production processes. The identification of the optimal enzyme loading is of particular importance to maximize the amount of glucose produced from lignocellulosic materials while maintaining low costs. This requirement can only be achieved by incorporating reliable methodologies to properly address the optimization problem.

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Section: Introductionmentioning

confidence: 99%

Enzymatic hydrolysis of sugarcane bagasse for bioethanol production: determining optimal enzyme loading using neural networks

Rivera

Rabelo

Garcia

et al. 2010

J of Chemical Tech & Biotech

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“…Artificial neural networks (ANNs) are widely used artificial intelligent algorithms in many fields owing to their excellent capability of modeling nonlinear systems and superior data fitting performance [21]. e feed-forward ANN in Figure 1 consists of an input layer, a hidden layer, and an output layer and can simulate nonlinear mapping from an M-dimensional space to an Ndimensional space [22].…”

Section: Basicmentioning

confidence: 99%

Stochastic Inversion Method for Concrete Dams on the Basis of Bayesian Back Analysis Theory

Cao

2019

Advances in Civil Engineering

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Inverse analysis is necessary for concrete dams in normal operation to overcome the discrepancy between the true mechanical parameters and test results. In view of the uncertain characteristics of concrete dams, a stochastic inverse model is proposed in this study to solve the undetermined mechanical parameters with sequential and spatial randomness using measured displacement data and Bayesian back analysis theory. An inversion method for the mechanical parameters of concrete dams is proposed. Fast Fourier transform algorithm is introduced to generate random fields for SFEM analysis. The case study shows that the proposed inversion method can reflect the random characteristics of concrete dams, the mechanical parameters obtained are reasonable, and the inverse model is feasible.

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“…Part weight control is one of them [2]. However, such method has some limitations, for instance there is no 1:1 mapping between part weight and part quality features.…”

Section: Introductionmentioning

confidence: 99%

Inspection model and correlation functions to assist in the correction of qualitative defects of injected parts

Chaves

Vizán

Márquez

et al. 2010

Polymer Engineering & Sci

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To perform quality inspection in the injection process is a complex task due to high number of defects that could occur in an injected part and the high number of process parameters that could produce them. Injection defects, in particular qualitative ones, are not a clear reference to determine correct process parameter value setting to produce good quality parts. Research results show that the occurrence of each injection defect could be caused by specific parameters with values above or below an optimal one. Although this information is a guide for the defect correction, the effective correction of qualitative defects with parameter modifications is very complex. This is due to the problems that arise when transforming a qualitative defect into a quantitative inspection. This article shows an inspection model to assist the qualitative defect intensity classification using defect behavior tendency curves. These curves have been deduced from generic analytical relationships established between injection defects and injection process parameters. Conducted tests allow validating the approach and its initial effectiveness.

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Adaptive adjustment of plastic injection processes based on neural network

Cited by 12 publications

References 5 publications

Enzymatic hydrolysis of sugarcane bagasse for bioethanol production: determining optimal enzyme loading using neural networks

Enzymatic hydrolysis of sugarcane bagasse for bioethanol production: determining optimal enzyme loading using neural networks

Stochastic Inversion Method for Concrete Dams on the Basis of Bayesian Back Analysis Theory

Inspection model and correlation functions to assist in the correction of qualitative defects of injected parts

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