Multi-objective optimization for optimum tolerance synthesis with process and machine selection using a genetic algorithm

This paper presents a new approach to the tolerance synthesis of the component parts of assemblies by simultaneously optimizing three manufacturing parameters: manufacturing cost, including tolerance cost and quality loss cost; machining time; and machine overhead/idle time cost. A methodology has been developed using the Genetic Algorithm (GA) technique to solve this multi-objective optimization problem. The effectiveness of the proposed methodology has been demonstrated by solving a wheel mounting assembly problem consisting of five components, two subassemblies, two critical dimensions, two functional tolerances, and eight operations. Significant cost saving can be achieved by employing this methodology

A dimensioning and tolerancing methodology for concurrent engineering applications II: comprehensive solution strategy

Dimensioning and tolerancing (D&T) is a multidisciplinary problem which requires the fulfillment of a large number of dimensional requirements. However, almost all of the currently available D&T tools are only intended for use by the designer. In addition, they typically provide solutions for the requirements one at time. This paper presents a methodology for determining the dimensional specifications of the component parts and sub-assemblies of a product by satisfying all of its requirements. The comprehensive solution strategy presented here includes: a strategy for separating D&T problems into groups, the determination of an optimum solution order for coupled functional equations, a generic tolerance allocation strategy, and strategies for solving different types of D&T problems. A number of commonly used cost minimization strategies, such as the use of standard parts, preferred sizes, preferred fits, and preferred tolerances, have also been incorporated into the proposed methodology. The methodology is interactive and intended for use in a concurrent engineering environment by members of a product development team

Numerical simulation of crashworthiness parameters for design optimization of an automotive crash-box

Automotive manufacturers rely on rigorous testing and simulations to construct their vehicles durable and safe in all aspects. One such vital factor is crash safety, otherwise known as crashworthiness. Crash tests are conventional forms of non-destructive methods to validate the vehicle for its crashworthiness and compatibility based on different operating conditions. The frontal impact test is the most primary form of crash test, which focuses on improving passenger's safety and comfort. According to NHTSA, a vehicle is rated based on these safety criteria, for which automobile manufacturers conduct a plethora of crash-related studies. Numerical simulation aids them in cutting down testing time and overall cost endured by providing a reliable amount of insights into the process. The current study is aimed at improving the crashworthiness of a crash box in a lightweight passenger car, such that it becomes more energy absorbent in terms of frontal impacts. All necessary parameters such as energy absorption, mean crush force, specific energy absorption, crush force efficiencies are evaluated based on analytical and finite element methods. There was a decent agreement between the analytical and simulation results, with an accuracy of 97%. The crashworthiness of the crash box was improved with the help of DOE-based response surface methodology (RSM). The RSM approach helped in improving the design of the crash box with enhanced EA & CFE by 30% and 8.8% respectively. The investigation of design variables on the energy absorption capacity of the thin-walled structure was also done. For the axial impact simulations, finite element solver Virtual Performance Solution − Pam Crash from the ESI group is used