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• W1533820392 abstract "In today's industry, it is imperative that a thorough knowledge of the mechanical properties of materials be known to the designer in order to come up with a design of parts, tools, or instruments that will meet the highly competitive industrial requirements. It is well known that mechanical properties of various materials are in turn highly affected by the manner in which they are subjected to loadings of both static and fatigue types, and by its manufacturing process, in particular the heat treatment the material receives during its manufacturing. This further makes it required to perform the proper experiments and laboratory tests with regard to fatigue in the field of fatigue mechanics in order to obtain the necessary knowledge for the material properties for design purposes. It is emphasized that such properties obtained from monotonic tests are of no value and by no means recommended. To this end, on one hand metallurgical engineers often attempt to obtain their desired material properties and efficiencies by making variations in the parameters governing the manufacturing process. On the other hand, yet, the high costs of fatigue tests as compared with those of the simple monotonic tests, as well as the need for complex testing equipment are the major drawbacks in the way of such tests, encouraging the use of approximate and empirical mathematical models based on the data obtained from the monotonic tests. This has been quite evident among researchers and industry alike, as indeed indicated by the variety of ongoing articles published in the field. In the area of materials engineering as well, the knowledge of the effect of different manufacturing processing parameters on the material properties in view of the highly expensive nature of the tests are also of particular interest. Use of Artificial Neural Network (ANN) models is considered as a less expensive, less tedious, more efficient, and highly reliable alternative means for the estimation of the material fatigue properties using the data obtained from the monotonic tests. In addition, the ANN methodology was also employed for the parameter estimation related to the manufacturing process of materials. The method was also used to investigate and infer the manner in which such material properties are affected by variations in the parameters that are the main governing elements of these properties. Many researchers have indeed pursued such applications in their studies (Bucar et al., 2006; Genel, 2004; Han, 1995; Lee et al., 1999; Liao et al., 2008; Malinov et al., 2001; Mathew et al., 2007; Mathur et al. 2007; Park & Kang, 2007; Pleune & Chopra, 2000; Srinivasan et al., 2003;" @default.
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• W1533820392 date "2011-04-04" @default.
• W1533820392 modified "2023-10-18" @default.
• W1533820392 title "Application of Artificial Neural Networks in the Estimation of Mechanical Properties of Materials" @default.
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• W1533820392 doi "https://doi.org/10.5772/16094" @default.
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