A committee machine approach to multiple response optimization

Multiple response optimization (MRO) problems are usually solved in three phases that include experiment design, modeling, and optimization. Committee machine (CM) as a set of some experts such as some artificial neural networks (ANNs) is used for modeling phase. Also, the optimization phase is done with different optimization techniques such as genetic algorithm (GA). The current paper is a development of recent authors' work on application of CM in MRO problem solving. In the modeling phase, the CM weights are determined with GA in which its fitness function is minimizing the RMSE. Then, in the optimization phase, the GA specifies the final response with the object to maximize the global desirability. Due to the fact that GA has a stochastic nature, it usually finds the response points near to optimum. Therefore, the performance the algorithm for several times will yield different responses with different GD values. This study includes a committee machine with four different ANNs. The algorithm was implemented on five case studies and the results represent for selected cases, when number of performances is equal to five, increasing in maximum GD with respect to average value of GD will be eleven percent. Increasing repeat number from five to forty-five will raise the maximum GD by only about three percent more. Consequently, the economic run number of the algorithm is five.

show abstract

Minimization of Residual Stresses in Submerged Arc Welding Process of Oil and Gas Steel Pipes by Committee Machine

Golestaneh

Ariffin

Tang

et al. 2014

AMM

View full text Add to dashboard Cite

Submerged arc welding (SAW) is a well-known method to weld seam in manufacturing of large diameters steel pipes in oil and gas industry. The main subject of SAW design is selection of the optimum combination of input variables for achieving the desired output variables of weld. Input variables include voltage, amperage and speed of welding and output variables include residual stresses due to welding. On the other hand, main target in multi response optimization (MRO) problem is to find input variables values to achieve to desired output variables. Current study is a combination and modification of some works of authors in MRO and SAW subjects. This study utilizes an experiment design according to Taguchi arrays. Also a committee machine (CM) modeling the problem by CM using two approaches. The first CM consists eight experts with traditional approach in computation and second CM includes elite experts. Genetic algorithm was applied to find CM weights and desired responses. Results show that proposed approach in CM has a smaller root mean squire error (RMSE) than traditional approach. The validation of CM model is done by comparison of results with simulation of SAW process and residual stresses in a finite element environment. Finally, the results show few differences between the real case responses and the proposed algorithm responses.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A committee machine approach to multiple response optimization

Cited by 3 publications

References 29 publications

An interactive artificial neural networks approach to multiresponse optimization

An interactive artificial neural networks approach to multiresponse optimization

Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems

Minimization of Residual Stresses in Submerged Arc Welding Process of Oil and Gas Steel Pipes by Committee Machine

Contact Info

Product

Resources

About