Robust Design of Automotive Component Using Optimization Analysis in Non-Linear Domain

Wan Mansor Wan MuhamadMechanical and Manufacturing SectionUniKL-MFI (Malaysia France Institute)

Nurul Farehah KaharMechanical and Manufacturing SectionUniKL-MFI (Malaysia France Institute)

Endra SujatmikaMechanical and Manufacturing SectionUniKL-MFI (Malaysia France Institute)

Aws Abid AliMechanical and Manufacturing SectionUniKL-MFI (Malaysia France Institute)

Abstract

Robust design is a design that satisfies functional requirements and is insensitive to changes in parameters or environment while optimal design is a design that achieves a desired objective while satisfying specified design requirement through modifications of the analysis model parameters. The existing optimization techniques used in the automotive company where this research is going to undertake are based on linear codes. To put the numerical simulation into the mainstream cost, weight and time reduction perspective, a more representative optimization techniques based on nonlinear codes must be established. It serves as an accurate integration tool of optimization in yielding a higher reliability output to the automotive component design and requires computationally-intensive finite-element analysis. Among the challenges is to integrate a quality engineering aspect into the algorithm where stochastic analysis can be incorporated into the non-linear domain seamlessly.

Key Words: Robust Design, Nonlinear Optimization, Finite Element Analysis

1. Introduction

Robust design has been gaining wide attention, and its applications have been extended to making reliable decisions when designing complex engineering systems [1].

This research project main objective is to develop algorithm/codes and software to facilitate the robust design of a component considering nonlinearity and also to contribute towards weight reduction of components.

Since most industrial companies are very keen on reducing the time for the manufacturing process with the consequent saving of costs and resources. It can be stated that expected role of finite element simulation is to meet this requirement [2].Hence, this paper will discuss more on the integration of finite element simulation between the manufacturing process optimization and design process optimization to fulfill the main specific goal

The existing optimization techniques are based on linear code (topography, topology, shape, design sensitivity etc). To put the numerical simulation into the mainstream cost, weight and time reduction perspective, a more representative optimization techniques based on non-linear codes must be established. It serves as an accurate integration tool of load, material and geometry optimization in yielding a higher reliability output to the component design. The challenge is to integrate a quality engineering aspect into the algorithm where stochastic analysis can be incorporated into the non-linear domain seamlessly.

2. Background

In order to meet such stringent fuel saving scheme, car manufacturers around the world are looking for robust and optimal designs in every aspect thinkable. A robust design is a design that satisfies functional requirements and is insensitive to changes in parameters or environment while optimal design is a design that achieves a desired objective while satisfying specified design requirement through modifications of the analysis model parameters [3].

In the automotive manufacturing company where this research is going to undertake, the existing optimization techniques are based on linear codes (topography, topology, shape, design sensitivity etc). To put the numerical simulation into the mainstream cost, weight and time reduction perspective, a more representative optimization techniques based on nonlinear codes must be established. It serves as an accurate integration tool of load, material and geometry optimization in yielding a higher reliability output to the automotive component design. Analysis in nonlinearity domain requires the resolution of a computation-intensive finite-element analysis performed by powerful commercial structural mechanics software [4] such as Abacus, Ansys, MSC/Nastran, and LS Dyna. Meanwhile, optimization process itself is a daunting task and therefore there is a need to employ certain optimization technique-based software such as ModeFrontier, Insight and Optimum to facilitate the analysis.

Among the challenges is to integrate a quality engineering aspect into the algorithm where stochastic analysis can be incorporated into the non-linear domain seamlessly. Uncertainties are a central element in structural analysis and design, and they can be quantified by statistical and stochastic procedures [5]. In this respect, methods based on the traditional Monte Carlo simulation method can be employed to deal with probabilistically modelled uncertainties [6].

3. Methodology

In detailing out the objective and identifying the development, several parameters have been identified. The responsibility to deliver specific objectives is shown as in Figure 1.

Figure 1 Design Strategy

There are two parts of code development activity for process design optimization tools. First part is code for product design optimization (left side of the flowchart) and second one is for manufacturing process optimization (right side of the flowchart).

Product design optimization code is used to perform optimization process based on nonlinear analysis to obtain optimum material layout (topology optimization) as well as size and shape. Meanwhile, manufacturing process code is developed for finding optimum manufacturing process parameters.

Later on, both parts will be integrated into one module/software package to support design manufacturing engineer in performing their duties.

Figure 2 below, indicates the proposed methodology for robust design of automotive component using nonlinear domain. The following are the summarized optimization process (1) Importing the CAD file to Hypermesh software, (2) Perform the preprocessing stage which include, geometry clean up, meshing, apply loading, boundary conditions and material. (3) Define the constraints; to this each design has constraints which depends on the actual situation for example the limitation of the dimensions which must be in according to the design template, (4) Design variables; this input should match the parameters that affects the design (5) Objective function; it is obvious that any optimization process should have an objective function, for example minimize the weight or maximize the surface area…etc.

After we specify all the required inputs for the optimization process, Optistruct solver will handle the job of the topology optimization which will be accompany by the shape and size optimization to give the real optimum shape. Farther more nonlinear analysis will perform after the optimization process to investigate the stress level for the optimum model.

In order to validate the obtained results, comparison must be performed using optimization software such as modeFrontier or use the technical literature to validate the optimization method.

Figure 2: Appendix A- Robust Design of Automotive Component Using Optimization in Nonlinear Domain

Appendix A

Figure 3: Appendix B - Manufacturing Process Optimization

Figure 3 shows the manufacturing process optimization where code/module is divided into two parts. Both of them have almost the same objective which is to find optimum manufacturing process parameters but the difference is Response Surface Method (left side) using regression to find optimum response while Robust Design (right side) using Taguchi’s Method to find optimum configuration of process parameters that will cause process insensitive (robust).

4. Research in Progress

Before analysis and optimization process took place, an initial simulation works for rear spindle has been performed. The model is shown to be over design and the safety factor is very high due to the high material strength. This is one of the reasons contributes to the importance of the optimization process as it is important to lower down the material cost with acceptable safety factor.

Using Hyperworks Process Manager, a process automation module for simple finite element modeling tasks also has been created. Several tasks from importing model to defining load steps are included in the modules. Each step has its own graphical user interface (GUI) that involves some codes within itself. Through module development, implementation of the standardized process is possible and best practices through giving some requirements in each process can be obtained.

Furthermore, to find optimum material layout of spindle, topology optimization is required. Shape and size optimization in the non-linear analysis domain will be conducted to find optimum geometry and to avoid over design rather than linear analysis. These processes will be captured in the next codes development so that it will become tools that can be used to facilitate the processes.

Appendix B

5. Conclusion

In this research project the main objective is to develop computer programs and codes for optimization in topology, topography, shape, design sensitivity, non-linear stochastic and fatigue optimization and also to contribute towards weight reduction of automotive components. Initial simulation works and codes development has been carried out as part of the activities that will contributes towards the end result. Further research will be done in line with the design strategy to ensure that the final result will reflect the entire project purpose.

6. References

[1] Ful-Chiang Wu, 2009, Robust design of nonlinear multiple dynamic quality characteristics

[2] M. Tisza, 2004, Numerical modelling and simulation in sheet metal forming

[3] Shankar, Ravi S., Robust Design, Product Development Conference, Huntington Beach, California, 2004.

[4] Sonia Calvel and Marcel Mongeau, Black-box structural optimization of a mechanical component, Computers & Industrial Engineering, Volume 53, Issue 3, October 2007, pages 514-530

[5] Schuëller, G.I., Developments in Stochastic Structural mechanics, Journal of Archive of Applied Mechanics (Ingenieur Archiv), Volume 75, Numbers 10-12 / October, 2006, pages 755-773.

[6] Charmpis, Dimos C. and Schueller, Gerhart I., Using Monte Carlo Simulation to Treat Physical Uncertainties, Volume 581 of Lecture Notes in Economics & Mathematical Sciences (LNEMS), pages 67–83, Laxenburg, Proceedings of CwU Workshop, December 13-16, 2006.