Welding parameters optimization for economic design using neural approximation and genetic algorithm

Tseng, Huai Hsuan

doi:10.1007/s00170-004-2276-3

Cited by 44 publications

(17 citation statements)

References 11 publications

(19 reference statements)

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“…Chatterjee et al 5) investigated the influence of various control parameters on circularity at entry and thrust forces in drilling of titanium alloy by using face centered central composite design, in which the harmony search algorithm was used to achieve the maximum circularity and minimum thrust force. Tseng 6) applied general regression neural network to approximate the relationship between welding parameters such as welding current, electrode force, welding time, and sheet thickness and the failure load, and the genetic algorithm was used to optimize the welding parameters to obtain the preferred welding quality at the least possible cost. Mansourzadeh et al 7) optimized spot welding parameters to improve the overall quality in a sheet metal assembly by neural networks and genetic algorithm.…”

Section: High Dimensional Data-driven Optimal Design For Hot Strip Romentioning

confidence: 99%

High Dimensional Data-driven Optimal Design for Hot Strip Rolling of C–Mn Steels

Cao

Zhou

et al. 2017

ISIJ Int.

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Recently, hot strip rolling processes are required to be agile and accurate in order to meet more and more diverse market demands. Under such circumstances, the traditional processing optimization by timeconsuming pilot experiments becomes difficult. To realize this target, core models of processing and mechanical properties are often established by neural network methods, which are used to handle nonlinear multi-variant systems. In modeling processing and mechanical properties, we found that the abnormal values in industrial big data could result in wrong predictions for the relationships between processing and properties. In the present work, therefore, data processing has been developed to prevent misleading predictions, which was performed by eliminating the redundant, abnormal, and imbalanced data before modeling. The Bayesian neural network was used to construct the modeling of mechanical properties for hot rolled C-Mn steels, which demonstrated that the accuracies between the measured and predicted values were within ± 10% and ± 5% for strength and elongation, respectively, providing a reliable model for the optimal process design. By applying the multi-objective optimization algorithm named Strength Pareto Evolutionary Algorithm 2 (SPEA2), the hot strip rolling processes for C-Mn steels were optimized in order for either stabilizing variations of properties or upgrading mechanical properties. Industrial trials were extensively carried out, showing good agreements with the optimized processes.

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Section: High Dimensional Data-driven Optimal Design For Hot Strip Romentioning

confidence: 99%

High Dimensional Data-driven Optimal Design for Hot Strip Rolling of C–Mn Steels

Cao

Zhou

et al. 2017

ISIJ Int.

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“…Yang et al [32] used ANN to propose a quality control system for the solder stencil printing process. Similarly, Shi et al [33] used also ANN for modelling nonlinear cause-effect relationships in printed circuit board (PCB) manufacturing, whereas general regression neural networks (GRNNs) were used in [34] for predictions in a spot welding process.…”

Section: Predicting Qualitymentioning

confidence: 99%

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

Rostami

Dantan

Homri

2015

Int. J. Metrol. Qual. Eng.

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. AbstractIn many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in the databases.Due to advances in data collection systems and analysis tools, data mining (DM)has widely been applied for quality assessment (QA) in manufacturing industries.In DM, the choice of technique to use in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs best for a particular type of data set.Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM) to solve QA problems. The review provides a comprehensive analysis of the literature from various points of view as DM preliminaries, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided.

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“…Any non linear regression problem can be modelled with this algorithm. Hsien-Yu Tseng [3] described about implementing the general regression neural network to create approximate models to relate the spot welding parameters , weld joint strength and power required to prepare the weld joint. And implementation of optimisation algorithm on the neural approximation model to find out the economic design.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Generalized Regression Neural Network to Establish a Relation between Vibration Parameters and Time of Vibration for Welded Joints

Kalpana¹

2013

IOSR-JMCE

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This paper presents implementation of Generalized Regression Neural Network to establish a relation between vibration parameters and properties of vibration welded joints. During the welding of metals along with mechanical vibrations, uniform and finer grain structures can be produced. This increases the toughness and hardness of the metals, because of solidification effects at the weld pool surface. So, physical experiments have been conducted on the homogeneous welded joints by providing vibrations during the welding period. The voltage used to generate the vibration and the time of vibration are used as vibration parameters. Hardness of the welded joint is considered as one of the mechanical properties of the welded joint.

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Welding parameters optimization for economic design using neural approximation and genetic algorithm

Cited by 44 publications

References 11 publications

High Dimensional Data-driven Optimal Design for Hot Strip Rolling of C–Mn Steels

High Dimensional Data-driven Optimal Design for Hot Strip Rolling of C–Mn Steels

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

Implementation of Generalized Regression Neural Network to Establish a Relation between Vibration Parameters and Time of Vibration for Welded Joints

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