Thomas Maier

Development of an engine crankshaft in a framework of computer-aided innovation

This paper describes the conceptual framework of a general strategy for developing an engine crankshaft based on computer-aided innovation, together with an introduction to the methodologies from which our strategy evolves. It begins with a description of two already popular disciplines, which have their roots in computer science and natural evolution: evolutionary design (ED) and genetic algorithms (GAs). A description of some optimization processes in the field of mechanical design is also presented. We explain our approach to multi-objective optimization and show how tools like the Pareto diagram can help in identifying conflicts. The concepts presented here are exemplified through the optimization of a combustion engine crankshaft. The main premise of the paper is the possibility to optimize the imbalance of a crankshaft using tools developed in this methodology. This study brings together techniques that have their origins in the fields of optimization and new tools for innovation. We reflect on how computers can have an active role in the conceptual design process, and explain how TRIZ (Theory of Inventive Problem Solving) can enrich the discipline of ED. The aim of our research is to extend the search for solutions with GAs and present creative, innovative alternatives to the designer. Similarities between GAs and TRIZ regarding ideality and evolution are presented. We also explain how geometric optimization systems (size, shape, topology and topography) offer hints about the next generation of optimization tools. The role of splines in this context is found to be closely integrated with GAs in enabling this development on a computer-aided design and engineering (CAD&CAE) software interface, and in enabling integration with Java programming language for automation of the development.

Comparison of Strategies for the Optimization/Innovation of Crankshaft Balance

Engine crankshafts are required to be balanced. The balance of a crankshaft is one of several parameters to be analyzed during the design of an engine, but certainly a poor balance leads to a low life time of the whole system. It is possible to optimize the balance of a crankshaft using CAD and CAE software, thanks to the new optimization tools based on Genetic Algorithms (GA) and tools for the integration of the CAD-CAE software. GAs have been used in various applications, one of which is the optimization of geometric shapes, a relatively recent area with high research potential. This paper describes a general strategy to optimize the balance of a crankshaft. A comparison is made among different tools used for the sustaining of this strategy. This paper is an extension of a previous paper by the authors [1] but now different tools are being included to improve the performance of the strategy. The analyzed crankshaft is modeled in commercial 3D parametric software. A Java interface included in the CAD software is used for evaluating the fitness function (the balance). Two GAs from different sources and platforms are used and then they are compared and discussed.

Optimization with Genetic Algorithms and Splines as a way for Computer Aided Innovation

This paper describes the conceptual foundations to construct a method on Computer Aided Innovation for product development. It begins with a brief recap of the different methodologies and disciplines that build its bases. Evolutionary Design is presented and explained how the first activities in Genetic Algorithms (GAs) helped to produce computer shapes that resembled a creative behavior. A description of optimization processes based on Genetic Algorithms is presented, and some of the genetic operators are explained as a background of the creative operators that are intended to be developed. A summary of some Design Optimization Systems is also explained and its use of splined profiles to optimize mechanical structures. The approach to multi-objective optimization with Genetic Algorithms is analyzed from the point of view of Pareto diagrams. It is discussed how the transition from a multi-objective optimization conflict to a solution with the aim of an ideal result can be developed means the help of TRIZ (Theory of Inventive Problem Solving), complementing the discipline of Evolutionary Design. Similarities between Genetic Algorithms and TRIZ regarding ideality and evolution are identified and presented. Finally, a brief presentation of a case study about the design of engine crankshafts is used to explain the concepts and methods deployed. The authors have been working on strategies to optimize the balance of a crankshaft using CAD and CAE software, splines, Genetic Algorithms, and tools for its integration [1] [2].

Multi-Objective System Optimization of Engine Crankshafts Using an Integration Approach

The ever increasing computer capabilities allow faster analysis in the field of Computer Aided Design and Engineering (CAD & CAE). CAD and CAE systems are currently used in Parametric and Structural Optimization to find optimal topologies and shapes of given parts under certain conditions. This paper describes a general strategy to optimize the balance of a crankshaft, using CAD and CAE software integrated with Genetic Algorithms (GAs) via programming in Java. An introduction to the groundings of this strategy is made among different tools used for its implementation. The analyzed crankshaft is modeled in commercial parametric 3D CAD software. CAD is used for evaluating the fitness function (the balance) and to make geometric modifications. CAE is used for evaluating dynamic restrictions (the eigen-frequencies). A Java interface is programmed to link the CAD model to the CAE software and to the genetic algorithms. In order to make geometry modifications to our case study, it was decided to substitute the profile of the counterweights with splines from its original “arc-shaped” design. The variation of the splined profile via control points results in an imbalance response. The imbalance of the crankshaft was defined as an independent objective function during a first approach, followed by a Pareto optimization of the imbalance from both correction planes, plus the curvature of the profile of the counterweights as restrictions for material flow during forging. The natural frequency was considered as an additional objective function during a second approach. The optimization process runs fully automated and the CAD program is on hold waiting for new set of parameters to receive and process, saving computing time, which is otherwise lost during the repeated startup of the cad application.Copyright

Academic Engineering Design Education in a Realistic Environment

The objective of academic education for mechanical design engineers is to convey qualifications which are necessary for product development in an industrial environment. The goal of the work described here is to improve engineering design education and to provide a more active learning experience. Design students should be familiarized with modern methods and technologies which they will most likely encounter during their future career. Design cannot be taught sufficiently in lectures alone [1, 2] and requirements on graduates in product development are continuously increasing. Not only professional skills but also social skills as well as proficiency with new technologies and methodologies become increasingly important [3]. For meeting these requirements the Karlsruhe Education Model for Product Development (KaLeP) [4] was developed at the Institute of Product Development (IPEK) at the University of Karlsruhe in Germany. In this contribution we present KaLeP, the role of modern design tools like CAD/PDM and wikis in education, the course projects for Machine Design and Integrated Product Development including training concept as well as the technical and organizational environment in which these courses take place.Copyright