Neural networks are being used in areas of prediction and classification, the areas where statistical methods have traditionally been used. Both the traditional statistical methods and neural networks are looked upon as competing model-building techniques in literature. This paper carries out a comprehensive review of articles that involve a comparative study of feed forward neural networks and statistical techniques used for prediction and classification problems in various areas of applications. Tabular presentations highlighting the important features of these articles are also provided. This study aims to give useful insight into the capabilities of neural networks and statistical methods used in different kinds of applications.

Review: Neural networks and statistical techniques: A review of applications

http://doras.dcu.ie/17608/1/Final_Version%2C_Review_paper.pdf

Optimization of different welding processes using statistical and numerical approaches - A reference guide

Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals

This paper explains an integrated method with a new approach using experimental design matrix of experimental designs technique on the experimental data available from conventional experimentation, application of neural network for predicting the weld bead geometric descriptors and use of genetic algorithm for optimization of process parameters. The properties of the welded joints are affected by a large number of welding parameters. Modeling of weld bead shape is important for predicting the quality of welds. In an attempt to model the welding process for predicting the bead shape parameters (also known as bead geometry parameters) of welded joints, modeling and optimization of bead shape parameters in tungsten inert gas (TIG) welding process has been tried in the present work. Multiple linear regression technique has been used to develop mathematical models for weld bead shape parameters of TIG welding process, considering the effects of main variables as well as two factor interactions. Also by using the same experimental data, an attempt has been made to predict the bead shape parameters using back-propagation neural network. To optimize the process parameters for the desired front height to front width ratio and back height to back width ratio, genetic algorithmic approach has been applied.

Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process

An automotive engine cradle supports many crucial components and systems, such as an engine, transmission, and suspension. Important performance measures for the design of an engine cradle include stiffness, natural frequency, and durability, while minimizing weight is of primary concern. This paper presents an effective and efficient methodology for engine cradle design from conceptual design to detailed design using design optimization. First, topology optimization was applied on a solid model which only contains the possible engine cradle design space, and an optimum conceptual design was determined which minimizes weight while satisfying all stiffness constraints. Based on topology optimization results, a design review was conducted, and a revised model was created which addresses all structural and manufacturability concerns. Shape and size optimization was then performed in the detailed design stage to further minimize the mass while meeting the stiffness and natural frequency targets. Lastly, the final design was validated for durability. The initial design domain had the mass of 82.6 kg; topology optimization in conceptual design reduced the mass to 26.7 kg; and the detailed design task involving shape and size optimization further reduced the mass to 21.4 kg.

Conceptual and detailed design of an automotive engine cradle by using topology, shape, and size optimization

A comparison in a back-bead prediction of gas metal arc welding using multiple regression analysis and artificial neural network

Integrated process for structural-topological configuration design of weight-reduced vehicle components

The development of a mechanical structure needs to be started from the conceptual design with low cost, high performance and quality. In this regard, the structural and topological shape of...

Jeong-ick Lee

Papers

A comparison in a back-bead prediction of gas metal arc welding using multiple regression analysis and artificial neural network

Integrated process for structural-topological configuration design of weight-reduced vehicle components

Structural optimization concept for the design of an aluminium control arm

Structural optimization design for large mirror

An integrated design for double-layered structures