ANN Modelling to Optimize Manufacturing Process

Abstract

Neural network (NN) model is an efficient and accurate tool for simulating manufacturing processes. Various authors adopted artificial neural networks (ANNs) to optimize multiresponse parameters in manufacturing processes. In most cases the adoption of ANN allows to predict the mechanical proprieties of processed products on the basis of given technological parameters. Therefore the implementation of ANN is hugely beneficial in industrial applications in order to save cost and material resources. In this chapter, following an introduction on the application of the ANN to the manufacturing process, it will be described an important study that has been published on international journals and that has investigated the use of the ANNs for the monitoring, controlling and optimization of the process. Experimental observations were collected in order to train the network and establish numerical relationships between process-related factors and mechanical features of the welded joints. Finally, an evaluation of time-costs parameters of the process, using the control of the ANN model, is conducted in order to identify the costs and the benefits of the prediction model adopted.

Assessing the correlation between fatty acid composition of biodiesel with the fuel property using artificial intelligence and optimization

“…The non‐parametric estimator, ANNs are used for a large number of real world problems because of its ability to address complex nonlinear relationships between the independent (input) and dependent (output) variables to an arbitrary degree of accuracy through a highly interconnected system of simple processing elements (called neurons or nodes ) 30 . General ANN model consists of “training” and “validation” data set where the training data set is used to create the model and validation data set is used to qualify the performance 31 . In the present work, a global exhaustive search in neural model and activation function was done to find out the best model in the universe.…”

Modelling of cutting parameters for Nilo 36 superalloy with machine learning methods and developing an interactive interface

“…When the literature is examined, there are examples where cutting forces and cutting parameters are modelled with an artificial neural network [21][22][23][24]. However, none of these examples mention the development of the graphical user interface included in this study.…”

A modeling method for the development of a bioprocess to optimally produce umqombothi (a South African traditional beer)

“…The technical application of these approaches, especially in a biologically complex product such as traditional beer has been minimal 4 . Furthermore, variable microbial growth kinetics, process constraints, biochemical reactions, dynamic food matrices, and difficult bioprocessing requirements amplify complexities in bioprocess development and optimization 3 , 5 . As a result, the combination of linear and non-linear techniques is an effective approach to describe, analyze, and predict bioprocess responses that impact the outcomes of the final product 3 , 6 .…”

“…The use of a single technique may not be adequate in ascertaining the relationship between process input variables and the quality of the product 5 . Nonetheless, standalone mathematical and statistical models have been previously successful in describing the linear, interactive, and quadratic effects of selected parameters in beer bioprocessing 6 , 7 .…”