A modified adaptive random-search algorithm for the design of helical gears has been developed. The proposed methodology allows for the implementation of nonlinear design functions and constraints without the need for linearization. In addition, the technique has the capability of starting from either feasible regions or infeasible regions of the design domain. Application examples demonstrate the feasibility of the proposed technique as a practical method for design optimization. The viability is further illustrated in a performance test against the conjugategradient search method. helical gears, optimization, gear design, random search
Optimization is a branch of applied mathematics that deals with techniques of achieving the 'most favorable' solution for a given problem. With the concepts of modern manufacturing techniques, and the growing awareness about resource conservation and the limitations in the availability of raw materials, it has become essential for design engineers to work with more stringent design constraints for efficient and cost-effective system designs that meet all the required conditions and are still a 'favorable solution'. Mathematical and numerical theories of optimization have been increasingly implemented to address the above issues. Although their primary industrial application was originally aerospace engineering, their application in the computer-aided