Modeling the steel case carburizing quenching process using statistical and machine learning techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Parijat D. Deshpande and Ujjawal Gupta and 
                 B. P. Gautham and Danish Khan",
  booktitle =    "9th International Conference on Industrial and
                 Information Systems (ICIIS 2014)",
  title =        "Modeling the steel case carburizing quenching process
                 using statistical and machine learning techniques",
  year =         "2014",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, surrogate
                 model, simulation, ANN",
  DOI =          "doi:10.1109/ICIINFS.2014.7036589",
  size =         "6 pages",
  abstract =     "Simulation of various manufacturing processes such as
                 heat treatments is rapidly gaining importance in the
                 industry for process optimisation, enhancing efficiency
                 and improving product quality. Case carburisation
                 followed by quenching is one such significant heat
                 treatment process commonly used in the automotive
                 industry. The equations to be solved for simulation of
                 these processes are non-linear differential equations
                 and require the use of computationally intensive
                 numerical techniques e.g. 3D Finite Element Modelling.
                 Using these models for solving optimisation or inverse
                 problems, compounded by the fact that a large number of
                 evaluations need to be carried out becomes
                 computationally expensive. This necessitates a simpler,
                 computationally inexpensive representation of the
                 process, albeit being applicable to a limited range of
                 process parameters and conditions. In this paper, we
                 explore the use of proven statistical techniques such
                 as Linear Regression and machine learning techniques
                 such as Artificial Neural Networks and Genetic
                 Programming to create computationally inexpensive
                 surrogate models of the carburisation quenching
                 processes to predict surface hardness and their results
                 are presented.",
  notes =        "Tata Res., Dev. & Design Centre, Tata Consultancy
                 Services, Pune, India

                 Also known as \cite{7036589}",

Genetic Programming entries for Parijat D Deshpande Ujjawal Gupta B P Gautham Danish Khan