Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

Chandrashekarappa, Manjunath Patel Gowdru; Krishna, Pravin; Parappagoudar, Mahesh B.

doi:10.1155/2014/293976

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…To further prove that the fusion model has better a prediction effect, the following is the trend graphs of the RMSE and PLCC changes of the middle layer MFQCs of the fusion model obtained through these three ratios. [17] In the figure, 1 RMSE is the trend of the RMSE in the middle layer during the training of the model. The following can be derived from the above three figures.…”

Section: Results Analysismentioning

confidence: 99%

Prediction of the Cement Grate Cooler Pressure in the Cooling Process Based on a Multi-Model Fusion Neural Network

2020

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The reasonable control of the grate cooler is the key factor to ensure the heat exchange and cement clinker quality during the clinker cooling process. In this paper, the cement grate cooler pressure of the grate cooler is taken as the research object and a cement grate cooler pressure prediction model is proposed based on the analysis of the current status of the automatic control of the grate cooler. This model uses a multi-model fusion neural network algorithm that combines a BP neural network, a support vector machine and classification and regression trees with a neural network structure. Furthermore, the multimodel fusion quality characteristics are proposed, and the root mean square error and Pearson linear correlation coefficient of the multi-model fusion quality characteristics are used as the evaluation indicators for the prediction results of the multi-model fusion neural network. After the analysis of the cooling process of the cement clinker, we select seven input variables, and then complete the data preprocessing and model parameter selection. Finally, we predict the cement grate cooler pressure using a multi-model fusion neural network, a BP neural network, a support vector machine and classification and regression trees with three training sets to test sets ratios. Through the comparison of the root mean square error and the Pearson linear correlation coefficient evaluation indicators and their change trends, as well as the display and analysis of the final modelling results, it is found that the multi-model fusion neural network algorithm can greatly improve the accuracy of the prediction of the grate pressure, and at the same time it has good practicality for the accurate prediction of the cement grate cooler pressure in the industry.

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Section: Results Analysismentioning

confidence: 99%

Prediction of the Cement Grate Cooler Pressure in the Cooling Process Based on a Multi-Model Fusion Neural Network

2020

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“…Discrete modeling has not been used until now for the improvement of casting product quality. Artificial neural network (ANN) used in die casting and in squeeze casting for improving quality of casting product (Zheng et al, 2009;Yarlagadda, 2000;Chandrashekarappa et al, 2014).…”

Section: Simulations Modelingmentioning

confidence: 99%

Critical parameters influencing the quality of metal castings: a systematic literature review

Suthar

Persis

Gupta

2021

IJQRM

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PurposeCasting is one of the well-known manufacturing processes to make durable parts of goods and machinery. However, the quality of the casting parts depends on the proper choice of process variables related to properties of the materials used in making a mold and the product itself; hence, variables related to product/process designs are taken into consideration. Understanding casting techniques considering significant process variables is critical to achieving better quality castings and helps to improve the productivity of the casting processes. This study aims to understand the computational models developed for achieving better quality castings using various casting techniques.Design/methodology/approachA systematic literature review is conducted in the field of casting considering the period 2000–2020. The keyword co-occurrence network and word cloud from the bibliometric analysis and text mining of the articles reveal that optimization and simulation models are extensively developed for various casting techniques, including sand casting, investment casting, die casting and squeeze casting, to improve quality aspects of the casting's product. This study further investigates the optimization and simulation models and has identified various process variables involved in each casting technique that are significantly affecting the outcomes of the processes in terms of defects, mechanical properties, yield, dimensional accuracy and emissions.FindingsThis study has drawn out the need for developing smart casting environments with data-driven modeling that will enable dynamic fine-tuning of the casting processes and help in achieving desired outcomes in today's competitive markets. This study highlights the possible technology interventions across the metal casting processes, which can further enhance the quality of the metal casting products and productivity of the casting processes, which show the future scope of this field.Research limitations/implicationsThis paper investigates the body of literature on the contributions of various researchers in producing high-quality casting parts and performs bibliometric analysis on the articles. However, research articles from high-quality journals are considered for the literature analysis in identifying the critical parameters influencing quality of metal castings.Originality/valueThe systematic literature review reveals the analytical models developed using simulation and optimization techniques and the important quality characteristics of the casting products. Further, the study also explores critical influencing parameters involved in every casting process that significantly affects the quality characteristics of the metal castings.

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“…The details of the developed ANN model for predicting the responses for a given set of WEDM process parameters in forward mapping are given as below. After the training phase using the new 5000 datasets, the developed ANN is employed for forward and backward prediction purposes (Chandrashekarappa et al, 2014). Forward mapping deals with predicting the responses/outputs of the WEDM process for known sets of input conditions.…”

Section: Ann-based Model Development For Wedm Processmentioning

confidence: 99%

Forward and Reverse Mapping for WEDM Process using Artificial Neural Networks

Dey¹,

Chakraborty²

2015

10.5267/j.dsl

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Suitable selection of various machining parameters for wire electrical discharge machining (WEDM) process heavily relies on the operator's experience and manufacturer's technologies because of their numerous and diverse operating ranges. Artificial neural networks have been introduced as an effective tool to predict values of responses and input parameters of different machining processes through forward and reverse modeling approaches respectively. This paper mainly focuses on predicting values of some machining responses, like machining rate, surface roughness, dimensional deviation and wire wear ratio using feed forward back propagation artificial neural network based on six WEDM process parameters, such as pulse on time, pulse off time, peak current, spark gap voltage, wire feed and wire tension. The corresponding reverse model is also developed to recommend the optimal settings of WEDM process parameters for achieving the desired responses according to the requirements of the end users. These modeling approaches are quite efficient to predict the values of machining responses as well as process parameter settings with reduced time and effort which otherwise have to be determined experimentally based on trial and error method. The predicted results are found to be in well congruence with the previously obtained experimental observations. Growing Science Ltd. All rights reserved. 5

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Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches

Cited by 9 publications

References 26 publications

Prediction of the Cement Grate Cooler Pressure in the Cooling Process Based on a Multi-Model Fusion Neural Network

Prediction of the Cement Grate Cooler Pressure in the Cooling Process Based on a Multi-Model Fusion Neural Network

Critical parameters influencing the quality of metal castings: a systematic literature review

Forward and Reverse Mapping for WEDM Process using Artificial Neural Networks

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