Ferruh Öztürk

Neuro-Genetic Design Optimization Framework to Support the Integrated Robust Design Optimization Process in CE

This article describes an integrated and optimized product design framework to support the design optimization applications in concurrent engineering (CE). The significant consideration is given to show the effectiveness of hybrid approaches and how they can be used to improve the performance of integrated design optimization applications. The proposed approach is based on two-stages which are (1) the use of neural networks (NNs) and genetic algorithm (GA) with feature technology for integrated design activities and (2) the use of Taguchi’s method and GA for design parameters optimization. The first stage resulted in better integrated design solutions in terms of computational complexity and later resulted in a solution, which leads to better and more robust parameter values for multi-objective shape design optimization. The effectiveness and validity of the proposed approach are evaluated with examples.

OPTIMAL DESIGN OF VEHICLE COMPONENTS USING TOPOLOGY DESIGN AND OPTIMISATION

An important problem in automotive industry is how to achieve better design concepts by considering structure performance and manufacturing cost in the early stages of product development. The topology design and optimisation provide an initial design concept for downstream applications, which leads to achieving better design by using computer-aided techniques. In this research, engine mount bracket design is presented to illustrate the application of topology optimisation approach for optimal design of vehicle components under dynamic loading conditions. The objective of the proposed research is to create an initial design concept, which has optimal structural layout. The effectiveness and verification of proposed approach are demonstrated with experimental results.

Hybrid neural network and genetic algorithm based machining feature recognition

In this research, neural networks (NNs) and genetic algorithms (GAs) are used together in a hybrid approach to reduce the computational complexity of feature recognition problem. The proposed approach combines the characteristics of evolutionary technique and NN to overcome the shortcomings of feature recognition problem. Consideration is given to reduce the computational complexity of network with specific interest to design the optimum network architecture using GA input selection approach. In order to evaluate the performance of the proposed system, experimental results are compared with previous NN based feature recognition research.

Neural network based non-standard feature recognition to integrate CAD and CAM

Abstract In this paper, a neural network based feature recognition approach which is capable of extracting information from design database is proposed to automate the integration of the design and applications following design. CAD data base is converted to feature based model information which can be used by CAM applications. Multilayer perceptron neural network is provided with Boundary representation (B-rep) information to recognise simple and complex features. B-rep structure is used to process the face-score values in terms of geometry and topology of the solid model. The effectiveness of proposed approach is demonstrated with experimental results which show the validity of this method to recognise complex shape features.

Hybrid approach for genetic algorithm and Taguchi's method based design optimization in the automotive industry

Although genetic algorithm and multi-objective optimization techniques are widely used to solve problems in the design and manufacturing area, further improvements are required to develop more efficient techniques regarding multi-objective optimization problems. The main goal of the present research is to further develop and strengthen the genetic algorithm based multi-objective optimization approach to generate real-world design solutions in the automotive industry. In this research, a new hybrid approach based on Taguchis method and a genetic algorithm is presented to achieve better Pareto-optimal set solutions for multi-objective design optimization problems. In addition, fatigue damage and life are also considered to evaluate the results of the design optimization process. The validity and efficiency of the proposed approach are evaluated and illustrated with test problems taken from the literature. It is then applied to a vehicle component taken from the automotive industry.

Hybrid Taguchi-Harmony Search Approach for Shape Optimization

Harmony search algorithms have recently gained a lot of attention from the optimization research community. In this chater, a new optimization approach based on harmony search algorithm and Taguchi’s method is presented to solve shape optimization problems. The validity and efficiency of the proposed approach are evaluated in an optimum design problem of a vehicle component by illustrating how the present approach can be applied for solving shape optimization problems. The first application of harmony search algorithm to the shape optimization problems in the literature is presented in this chapter. The results of the shape optimization problem indicate that the proposed approach is highly competitive and it can be considered as a viable alternative in solving real-world optimization problems, finding beter solutions compared to other approaches that are representative of the state-of-the-art in the optimization literature.

Multi-objective crashworthiness design optimisation of thin-walled tubes

Thin-walled structural members contribute significantly to the ability of an automobile body to absorb energy during frontal impact. The objective of this paper is to improve the energy absorption performance of crash tubes that are placed behind the bumper in automotive vehicles using shape and size optimisation. The best cross section is determined initially, and then dynamic axial crushing simulation is carried out on thin-walled octagonal tube, which has crush initiators. In order to maximise the absorbed energy, shape and size, optimisation techniques are applied using a response surface approximation technique. The design of experiment method is employed to construct the response surfaces. An explicit finite element code, LS-DYNA, was used to implement the parallel computations.

Optimisation of vehicle engine mount system using simulation-based design approach

The engine mount systems have a significant effect with respect to reducing the vehicle development time and cost regarding NVH characteristics and shortcomings not solved yet. There is still a need to enhance the performance of engine mount systems for vehicle ride comfort. This research presents a simulation-based approach to design optimised engine mount components to isolate the road- and engine-induced vibrations without the need for physical prototypes. Experimental tests are performed to define hyperelastic and viscoelastic models for non-linear finite element simulations of rubber components. After the correlation between the results of simulations and the product tests, a virtual engine mount model is presented to design optimum engine mount systems with the desired static and dynamic characteristics. The stiffness curves of different shapes of designs are investigated, and the best curve is chosen as an optimum design solution using the design of experiments approach.

Computationally Efficient Approach for the Integration of Design and Manufacturing in CE

Today, companies are faced with fierce competition which is characterized by the necessity to bring the higher quality prod ucts and lower priced products to the market in shorter times than their competitors. The key to the success of organizations is the effec tive integration of design and applications following design such as machining, process planning, analysis, assembly, inspection etc. It was seen that effectiveness of the traditional CIM systems is not satisfactory to ensure competitiveness and high productivity. Recently, the concept of CE has been proposed to overcome the problems exist in integration. However, it has been recognized by both academic and industrial environments that efficient application of CE is still not achieved. In this research, STEP based feature recognition using neural networks is presented to develop feature based model and to enhance the integration of production activities in CE.

Feature-based environmental issues: neural network-based feature recognition

Today, the automotive industry is faced with more pressure to realise higher quality products, lower production costs and shorter lead times. The key to the success of organisations is the effective integration of design and other downstream applications such as machining, process planning, analysis, assembly, inspection etc. This paper reviews the current research in feature recognition and neural network applications in feature-based environments. The central issues associated with feature-based environments and drawbacks of the current approaches are addressed. The future research directions and proposals to overcome the shortcomings are presented.