On the Optimisation of a Double Cone Synchroniser for Improved Manual Transmission Shiftability

2002 ◽  
Author(s):  
Massimiliano Gobbi ◽  
Gianpiero Mastinu ◽  
Augusto D’Orazio ◽  
Massimo Caudano ◽  
Giorgio Faustini
Keyword(s):  
Dynamic Behaviour ◽  
Programming Approach ◽  
Approximation Procedure ◽  
Pareto Optimal ◽  
Performance Indices ◽  
Global Approximation ◽  
Multi Objective Programming ◽  
Design Variables ◽  
Design Solutions ◽  
Sensitivity Method

The paper presents a method to optimise the synchroniser of a road vehicle gearbox in order to improve shiftability and driver comfort. A multi-body physical model of the synchroniser has been developed and validated experimentally. The optimisation method is based on a Multi-objective Programming approach, and it allows to tune the thirty-two parameters of the synchroniser in order to achieve the desired dynamic behaviour of the system during a reference shift action, defined by seven performance indices. A Global Approximation procedure has been followed to solve numerically the optimisation problem. A special study has been performed and implemented in order to explore all of the feasible design solutions within the design variables domain. A global sensitivity method has been applied in order to analyse the relationships among the thirty-two design variables and the seven performance indices. Pareto-optimal design solutions have been computed in a very short time. These Pareto-optimal solutions have been checked for robustness by applying the minimum sensitivity method. The optimisation method has been applied with successful results. A number of optimised synchronisers have been defined, all of them featuring relevant improvements in the dynamic behaviour (shiftability) with respect to the reference synchroniser, aleady effective and under production.

Global Approximation: Performance Comparison of Different Methods, With an Application to Road Vehicle System Engineering

1999 ◽  
Author(s):  
Massimiliano Gobbi ◽  
Giampiero Mastinu
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Linear Interpolation ◽  
Performance Comparison ◽  
Pareto Optimal ◽  
Approximation Methods ◽  
Global Approximation ◽  
Road Vehicle ◽  
Pareto Optimal Set ◽  
Design Variables

Abstract Optimisation of complex mechanical systems has often to be performed by resorting to global approximation. In usual global approximation practice, the original mathematical model is substituted by another mathematical model which gives approximately the same relationships between design variables and performance indexes. This is made to ensure much faster simulations which are of crucial importance to find optimal solutions. In this paper the performances of four global approximation methods (Neural Networks, Kriging, Quadratic Approximation, Linear Interpolation) are compared, with reference to an actual optimal design problem. The performances of a road vehicle suspension system are optimised by varying the system’s design variables. The Pareto-optimal set is derived symbolically. The performances of the different approximation methods taken into consideration are assessed by comparing the numerical- and the analytical-Pareto-optimal results. It is found that Neural Networks obtain the best accuracy.

Stochastic Multi-Objective Optimisation of a Gearbox Synchroniser and Selector Mechanism

2003 ◽  
Author(s):  
Massimiliano Gobbi ◽  
Giampiero Mastinu ◽  
Massimo Caudano
Keyword(s):  
System Model ◽  
Objective Functions ◽  
Performance Indices ◽  
Global Approximation ◽  
Stochastic Perturbations ◽  
New Approach ◽  
Multi Objective ◽  
Design Solutions ◽  
Shift Action ◽  
Multi Body

In the paper a new approach is presented for the design of the synchroniser and selector mechanism of a road vehicle gearbox. The main aim is to improve shiftability and driver comfort. The new approach is based not only on the theory of multi-objective optimisation but also on robust design. A multi-body physical system model of the synchroniser and selector mechanism has been developed and vaildated experimentally. The physical model is stochastic being many of the system parameters defined by stochastic processes. The fifty-eight parameters of the system model have been tuned in order to achieve the desired dynamic behaviour of the synchroniser and selector mechanism during a reference shift action, defined by nine performance indices. The new approach is characterised both by the optimisation of the objective functions (corresponding to system performance indices) and by the reduction (or minimisation) of the sensitivity (variance) of the performance indices to stochastic perturbations. Such variances are computed (very quickly) by means of an original procedure based on the global approximation of the objective functions. Additionally, with respect to the mentioned features, the new approach is based on both a special study to explore all of the feasible design solutions, and on a global sensitivity procedure to analyse (in a stochastic context) the influence of each design variable on each objective function. Pareto-optimal design solutions for different levels of “robustness” have been computed in a very short time. The optimisation method has been applied with successful results. A number of optimised synchronisers and selector mechanisms have been defined, all of them featuring relevant improvements in terms of performance and robustness with respect to the reference system, already effective and under production.

Optimal and Robust Design of Ground Vehicle Systems

2006 ◽  
Author(s):  
Massimiliano Gobbi
Keyword(s):  
Robust Design ◽  
Simple Problem ◽  
Objective Functions ◽  
Performance Indices ◽  
Global Approximation ◽  
Stochastic Perturbations ◽  
New Approach ◽  
Complex Design ◽  
Design Solutions ◽  
Short Time

A new approach for the design of vehicle subsystems is addressed in the paper. The new approach is based not only on the theory of multi-objective optimisation but also on robust design. The method is characterised both by the optimisation of the objective functions (corresponding to system performance indices) and by the reduction (or minimisation) of the sensitivity (variance) of the performance indices to stochastic perturbations. Such variances are computed (very quickly) by means of an original procedure based on the global approximation of the objective functions. Additionally, with respect to the mentioned features, the new approach is based on both a special study to explore all of the feasible design solutions, and on a global sensitivity procedure to analyse (in a stochastic context) the influence of each design variable on each objective function. Pareto-optimal design solutions for different levels of “robustness” can be computed in a very short time. The optimisation method has been tested on a relatively simple problem and applied with successful results to a complex design problem related to vehicle design.

A Hybrid Expert System–Nonlinear Programming Approach to Optimal Gear Design

1989 ◽  
Author(s):  
K.-C. Lin ◽  
G. E. Johnson
Keyword(s):  
Mathematical Model ◽  
Expert System ◽  
Spur Gear ◽  
Design Guidelines ◽  
Geometric Design ◽  
Geometric Constraints ◽  
Programming Approach ◽  
Gear Design ◽  
Design Variables ◽  
Short Period

Abstract An expert system is developed for optimal spur gear design. Design automation is accomplished by dividing the design variables into different categories, i.e. geometric design variables and non-geometric design variables. The geometric variables are further divided into terms that are related to the gear mathematical model and terms that are determined according to the designer’s experience. By properly developing the mathematical model, numerical optimisation can be used to seek the best solution for a given set of geometric constraints. The process of determining the non-geometric design variables is automated by using symbolic computation. This gear design expert system is built according to the AGMA standards and a survey of gear design experts. The recommendations of gear designers and the information provided by AGMA standards are integrated into knowledge bases and data bases. By providing fast information retrieval and design guidelines, this expert system greatly streamlines the spur gear design process and makes it possible for a novice designer to achieve a reliable design in a short period of time.

Multiobjective Weighting Selection for Optimization-Based Control Design

1997 ◽  
Vol 122 (3) ◽  
pp. 567-569 ◽  
Author(s):  
Ricardo H. C. Takahashi ◽  
Juan F. Camino and ◽  
Douglas E. Zampieri ◽  
Pedro L. D. Peres
Keyword(s):  
Control Design ◽  
Active Suspension ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Cost Functional ◽  
Design Variables ◽  
Optimal Controllers ◽  
Selection For ◽  
Multiobjective Design

A methodology for the multiobjective design of controllers is presented, motivated by the problem of designing an active suspension controller. This problem has, as a particular feature, the possibility of being defined with two design variables only. The multiobjective controller is searched inside the space of “optimal controllers” defined by a weighted cost functional. The weightings are taken as the optimization variables for the multiobjective design. The method leads to (local) Pareto-optimal solutions and allows the direct specification of controller constraints in terms of some primary objectives which are taken into account in the multiobjective search. [S0022-0434(00)01403-9]

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