**ANN Modelling to Optimize Manufacturing Process**

**ANN Modelling to Optimize Manufacturing Process**

DOI: 10.5772/intechopen.71237

Luigi Alberto Ciro De Filippis, Livia Maria Serio, Francesco Facchini and Giovanni Mummolo Francesco Facchini and Giovanni Mummolo Additional information is available at the end of the chapter

Luigi Alberto Ciro De Filippis, Livia Maria Serio,

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.71237

#### **Abstract**

[29] EN 14214. Liquid Petroleum Products—Fatty Acid Methyl Esters (FAME) for Use in Diesel Engines and Heating Applications—Requirements and Test Methods. Comité

[30] RANP 45/2014. Resolução ANP n 45, de 25 de agosto de 2014 - DOU 26.08.2014. Agência

[31] ASTM D6751. Standard Specification for Biodiesel Fuel Blend Stock (B100) for Middle

[32] Galvão RKH, Araujo MCU, José GE, Pontes MJC, Silva EC, Saldanha TCB. A method for calibration and validation subset partitioning. Talanta. 2005;67:736-740. DOI: 10.1016/j.

[33] EN 14112. Fat and Oil Derivatives—Fatty Acid Methyl Esters (FAME) - Determination of Oxidation Stability (Accelerated Oxidation Test). Comité Européen de Normalisation.

[34] Oliveira JS, Montalvão R, Daher L, Suarez PAZ, Rubim JC. Determination of methyl ester contents in biodiesel blends by FTIR-ATR and FTNIR spectroscopies. Talanta. 2006;69:

[35] De Lira LFB, de Albuquerque MS, Pacheco JGA; Fonseca TM, Cavalcanti EHS, Stragevitch L, Pimentel MF. Infrared spectroscopy and multivariate calibration to monitor stability quality parameters of biodiesel. Microchemical Journal 2010;96:126-131. DOI:

[36] Savitzky A, Golay MJE. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry. 1964;36:1627-1639. DOI: 10.1021/ac60214a047

Nacional do Petróleo, Gás Natural e Biocombustíveis. p. 2014

Distillate Fuels. American Society for Testing and Materials. 2012.

Européen de Normalisation. 2012.

200 Advanced Applications for Artificial Neural Networks

1278-1284. DOI: 10.1016/j.talanta.2006.01.002

10.1016/j.microc.2010.02.014

talanta.2005.03.025

2003.

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.

**Keywords:** modelling, simulation, control and monitoring of manufacturing processes, simulation technologies
