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In this paper, development of a MATLAB-based computer environment for optimization of EMS calibration is presented. The objective is to eliminate the complicated and tedious calibration process on chassis dynamometer or at least reduce the time required. In this way, first a black-box model of engine is developed. Then this model is linked to ADVISOR -the renowned vehicle simulation software- to obtain an integrated engine-driveline model. This model is used in the optimization process, which includes different optimization techniques.

This paper implements a derivative free optimization method called ‘Genetic Algorithm’ to estimate the parameters of a four wheel three degrees of freedom vehicle handling model. At first the model is developed containing a non-linear tire model called ‘Fiala’. Then an error function is defined and ‘Genetic Algorithm’ optimization method is introduced and applied to minimize the error, Finally verification of parameter estimation is checked.

The process capability indices are widely used to measure the capability of the process to manufacture objects within the required tolerance. Fit quality is mainly dominated by the distribution of fit dimensions, i.e., a gap dimension. As the fit dimensions are very difficult to be measured in mass production, they are not to be considered as a direct inspection objective. The quality inspection and evaluation relative to fit quality are focused on whether the processes of assembly requirements are conformed with their specification limits respectively. Fit quality specification can be indicated by the process capability indices of mating parts. In this paper, the statistical-based process capability analysis method is presented to estimate ability of manufacturing process for considering of assembly requirements and fit quality in a mechanical assembly with asymmetric tolerances.

A mechanical system inherently affected by the conditions, factors, and parameters of uncertainties. Without including the uncertainty effects in the design procedure, the designs may not be robust and reliable. Robust design optimization (RDO) method is a procedure to find the insensitive design with respect to the variations. On the other hand, reliability is measured by the probability of satisfying a specific design criterion. Therefore, a reliable design is a design that satisfies the specified criteria even with some uncertainties in variables and parameters. Reliability-based design optimization (RBDO) is an optimization procedure that incorporates reliability requirements to find the proper design. Since RDO and RBDO are usually the expensive computational approaches, the Reliability-Based Robust Design Optimization (RBRDO) may be difficult to apply. In this paper, a new model for the reliability based robust design optimization is introduced.

Tolerance allocation is a key tool to reach a product with the minimum cost and the maximum performance. Since the thermal effects can cause the dimensional and geometrical variations in the components of mechanical assemblies, the tolerance allocation may be inefficient in the optimal tolerance design at the nominal conditions without including the thermal impacts. In this paper, a new optimal tolerance design of mechanical assemblies with the thermal effects is proposed. According to the proposed method, the tolerance allocation procedure is modeled as a multi-objective optimization problem. The functional objective, the manufacturing cost, and the quality loss function are considered as the corresponding objectives multi-objective optimal tolerance design problem. Using the computational results from the finite element simulations and based on the Artificial Neural Network (ANN) method, the design function as functional objective can be modeled.

In this research, the handling behavior of an intermediate class passenger car has been optimized by altering its front suspension parameters. For this purpose, a validated virtual model of the car, constructed by Adams/Car software, has been used. The utilized objective function is a combination of eight criteria indicating handling characteristics of the car. To reduce the amount of optimization parameters, a sensitivity analysis has been done by implementing the Design of Experiments method capabilities. Optimization has been done using the Response Surface Method. The obtained optimization results show a considerable improvement in the system response.

The design process always has some known or unknown uncertainties in the design variables and parameters. The aim of robust design is minimization of performance sensitivity to uncertainties. Tolerance allocation process can significantly affect quality and robustness of the product. In this paper, a methodology to minimize a product's sensitivity to uncertainties by allocating manufacturing tolerances is presented. The robust tolerance design problem is formulated as a multi-objective optimization based on the combined function-uncertainty-cost model. Genetic algorithm is utilized to solve the multi-objective optimization and a case study is presented to illustrate the methodology.

This paper describes an integrated engineering approach for a mathematical modeling of an automotive air-conditioner system and method for performing heating and cooling load calculations of a vehicle shell assembly for designing an HVAC system for a passenger vehicle. In addition, there is presented a physical optimization technique based on minimization of the total entropy generation for the vehicle air-conditioner and HVAC system. Simultaneous solution of the automotive air-conditioning, air handling and the passenger cabin thermal model provide the transient temperature history for consideration of passenger comfort conditions. The validity of the mathematical model was confirmed using experimental data. Optimization of the developed model is performed based on minimization of the total entropy generation of the air-conditioning air-handling system.

In this research, important parameters of a rack and pinion steering system in dynamic steady state and transient responses have been investigated. For this purpose, virtual model of a medium passenger car in ADAMS/Car has been used. The model has up to 121 kinematic degree of freedom and includes all components of the rack and pinion steering system. Several different experimental test results have confirmed the validity of the model. Sensitivity analysis have been done based on design of experiments (DOE) method. Two level fractional factorial designs have been selected for this purpose. Steady state cornering and step steer input are the analysis that used for this research. Understeering coefficient, steering wheel torque and steering sensitivity are obtained from the steady state cornering analysis, while the step steer analysis yields yaw velocity overshoot, yaw velocity rise time, lateral acceleration overshoot, lateral acceleration rise time and roll angle overshoot.

The production with high quality at the lowest production time can be a key means to success in the competitive environment of manufacturing companies. Therefore, in recent years, the need for extra precise and high-speed machine tools has been impressively increased in manufacturing applications. One of the main sources of errors in the motion of high-speed spindles is the occurrence of non-repetitive runouts (NRRO) in the bearing. The NRRO can be caused by some factors such as the form of balls, the waviness of rings, the number of balls, and the permutation of one or two balls in the ball bearing. In this paper, a Taguchi-based approach is proposed for the optimal design of high-speed spindle bearings by minimizing the NRRO in the machine tools compatible with corresponding standards. First, the optimal design of the high-speed spindle bearings to minimize the NRRO is formulated.

The desired milling process with high material removal rate (MRR) and low surface roughness of the product can be achieved only if machining chatter is absent. Incorporating chatter into the optimal selection of the machining parameters leads to a complex problem. Therefore, the approach of selecting conservative intervals for the machining parameters is usually employed instead. In this paper, a practical approach is proposed to specify the optimal machining parameters (depth of cut and spindle speed) in order to maximize MRR and minimize forced vibrations by considering machining chatter. Firstly, the worst-case scenario-based optimization problem in terms of the surface quality is solved to find the critical time at which maximal amplitude vibrations occur. Then, the time dependency of the problem is eliminated. Secondly, the multi-objective optimization is conducted to achieve the Pareto Optimal Front (POF).