N Amin

A CGI-based approach for remotely executing a large program for integration of design and manufacture over the Internet

This paper proposes an Internet-based system for geographically dispersed teams to collaborate over the Internet for the purpose of integration in design and manufacture. As one of the key issues of the system, a CGI (common gateway interface)-based multi-user method has been developed to remotely execute a large-size software package via the Internet. Agear optimization software package has been chosen as the application area. The package implements genetic algorithms to search the best solution for gear design. The objective is to implement this in the Internet and to collaborate between different locations. The package is located and run on the host server; after the completion of the program, the results are sent back to the client. To accomplish this, a combination of CGI, HTML, JavaScript, Java, multi-user environment and Internet security techniques has been utilized.

Multi‐user Internet environment for gear design optimization

A Web‐based multi‐user system has been developed to remotely execute a large size software package via the Internet. The software implements a genetic algorithm to optimize the design of spur and helical gears. To accomplish this, a combination of HTML, Java servlets, Java applets, Java Script and HTTP protocol has been employed.

Enhancement of speed and efficiency of an internet-based gear design optimisation

An internet-based gear design optimisation program has been developed for geographically dispersed teams to collaborate over the internet. The optimisation program implements genetic algorithm. A novel methodology is presented that improves the speed of execution of the optimisation program by integrating artificial neural networks into the system. The paper also proposes a method that allows an improvement to the performance of the back propagation-learning algorithm. This is done by rescaling the output data patterns to lie slightly below and above the two extreme values of the full range neural activation function. Experimental tests show the reduction of execution time by approximately 50%, as well as an improvement in the training and generalisation errors and the rate of learning of the network.