A modified multi-gene genetic programming approach for modelling true stress of dynamic strain aging regime of austenitic stainless steel 304

State-of-the-art in empirical modelling of rapid prototyping processes

(2014) A. Garg et al.   RAPID PROTOTYPING JOURNAL

Performance evaluation of microbial fuel cell by artificial intelligence methods

(2013) A. Garg et al.   EXPERT SYSTEMS WITH APPLICATIONS

Classification-driven model selection approach of genetic programming in modelling of turning process

(2013) A. Garg et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Crack detection in a rotating shaft using artificial neural networks and PSD characterisation

(2013) A. A. Mohammed et al.   MECCANICA

Rotor crack identification based on neural networks and modal data

(2013) J. L. Zapico-Valle et al.   MECCANICA

Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations

(2012) Ali R. Yildiz   APPLIED SOFT COMPUTING

Development of constitutive models for dynamic strain aging regime in Austenitic stainless steel 304

(2012) Amit Kumar Gupta et al.   MATERIALS & DESIGN

The worst response of mistuned bladed disk system using neural network and genetic algorithm

(2012) E. Raeisi et al.   MECCANICA

Dynamics of a stainless steel turning process by statistical and recurrence analyses

(2012) Grzegorz Litak et al.   MECCANICA

A critical comparison of various data processing methods in simple uni-axial compression testing

(2011) Dipti Samantaray et al.   MATERIALS & DESIGN

Prediction of flow stress in dynamic strain aging regime of austenitic stainless steel 316 using artificial neural network

(2011) Amit Kumar Gupta et al.   MATERIALS & DESIGN

Constitutive modelling for high temperature behavior of 1Cr12Ni3Mo2VNbN martensitic steel

(2011) Yan-Hong Xiao et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

Regrasping objects during manipulation tasks by combining genetic algorithms and finger gaiting

(2011) A. Fernandez et al.   MECCANICA

Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel

(2010) Ulaş Çaydaş et al.   JOURNAL OF INTELLIGENT MANUFACTURING

A critical review of experimental results and constitutive descriptions for metals and alloys in hot working

(2010) Y.C. Lin et al.   MATERIALS & DESIGN

A comparative study on Johnson Cook, modified Zerilli–Armstrong and Arrhenius-type constitutive models to predict elevated temperature flow behaviour in modified 9Cr–1Mo steel

(2009) Dipti Samantaray et al.   COMPUTATIONAL MATERIALS SCIENCE

Artificial neural networks to optimize the extrusion of an aluminium alloy

(2009) Carmine Lucignano et al.   JOURNAL OF INTELLIGENT MANUFACTURING

A thermo-viscoplastic constitutive model to predict elevated-temperature flow behaviour in a titanium-modified austenitic stainless steel

(2009) Dipti Samantaray et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

A method to predict flow stress considering dynamic recrystallization during hot deformation

(2008) Xiaoming He et al.   COMPUTATIONAL MATERIALS SCIENCE

In-process surface roughness prediction system using cutting vibrations in turning

(2008) D. R. Salgado et al.   INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression

(2008) Amit Kumar Gupta   INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

Model selection approaches for non-linear system identification: a review

(2008) X. Hong et al.   INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE

The influence of temperature on the cyclic behavior of aged and unaged super duplex stainless steels

(2008) A.F. Armas et al.   MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING