Zuomin Dong

Trends, features, and tests of common and recently introduced global optimization methods

Global optimization techniques have been used extensively due to their capability in handling complex engineering problems. In addition to a number of well known global optimization techniques, many new methods have been introduced recently for various optimal design applications. In this work, a number of representative, well known and recently introduced global optimization techniques are closely examined and compared. The historical development, special features and trends on the development of global optimization algorithms are reviewed. Special attention is devoted to the recent developments of multidisciplinary design optimization algorithms based on effective metamodelling techniques. Commonly used benchmark optimization problems are used as test examples to reveal the pros and cons of these global optimization methods. A new meta-model based global optimization search method, introduced and improved recently by the authors, is also included in the tests and comparison.

Optimal fuel cell system design considering functional performance and production costs

In this work the optimization-based, integrated concurrent design method is applied to the modelling, analysis, and design of a transportation fuel cell system. A general optimal design model considering both functional performance and production costs is first introduced. Using the Ballard Mark V Transit Bus fuel cell system as an example, the study explores the intrinsic relations among various fuel cell system performance and cost aspects to provide insights for new cost-effective designs. A joint performance and cost optimization is carried out to demonstrate this new approach. This approach breaks the traditional barrier between design (concerning functional performance) and manufacturing (concerning production costs), allowing both functional performance and production costs to be fed into design phase and to be jointly optimized.

Automated surface subdivision and tool path generation for 3 ½½-axis CNC machining of sculptured parts

As an innovative and cost-effective method for carrying out multiple-axis CNC machining, 3½½-axis CNC machining technique adds an automatic indexing/rotary table with two additional discrete rotations to a regular 3-axis CNC machine, to improve its ability and efficiency for machining complex sculptured parts. In this work, a new tool path generation method to automatically subdivide a complex sculptured surface into a number of easy-to-machine surface patches; identify the favorable machining set-up/orientation for each patch; and generate effective 3-axis CNC tool paths for each patch is introduced. The method and its advantages are illustrated using an example of sculptured surface machining. The work contributes to automated multiple-axis CNC tool path generation for sculptured part machining and forms a foundation for further research.

Optimal toolpath pattern identification for single island, sculptured part rough machining using fuzzy pattern analysis

Abstract In the manufacture of sculptured parts from prismatic stock, rough machining dominates the machining time due to the significant shape difference between the stock and part. For the sculptured part rough machining using 2.5D milling or contour-map machining, the appropriate selection of a toolpath pattern for each cutting layer can significantly improve productivity and lead to lower production costs. In the paper, various feasible toolpath patterns are investigated. An intelligent approach for automatically identifying the most productive toolpath pattern for a given cutting layer is introduced. The study is focused on sculptured parts with a single island and no seriously nonconvex shape. The method is based on fuzzy cutting layer shape pattern clustering and recognition. An example is used to illustrate the method.

Hybrid and adaptive meta-model-based global optimization

As an efficient and robust technique for global optimization, meta-model-based search methods have been increasingly used in solving complex and computation intensive design optimization problems. In this work, a hybrid and adaptive meta-model-based global optimization method that can automatically select appropriate meta-modelling techniques during the search process to improve search efficiency is introduced. The search initially applies three representative meta-models concurrently. Progress towards a better performing model is then introduced by selecting sample data points adaptively according to the calculated values of the three meta-models to improve modelling accuracy and search efficiency. To demonstrate the superior performance of the new algorithm over existing search methods, the new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization example involving vehicle crash simulation. The method is particularly suitable for desig...

Coding and clustering of design and manufacturing features for concurrent design

Abstract A feature modeling system using two types of features, design features and manufacturing features, is introduced for modeling these two product life-cycle aspects. Design features, represented as mechanical components and mechanisms, are used for modeling design candidates to satisfy design functions. A design feature coding system is developed based on the analysis of design functions. A fuzzy pattern clustering algorithm is employed to organize the large design feature library into hierarchical feature groups. Required design features are identified using graph-based search. A manufacturing feature is a geometric element to be produced. A manufacturing feature coding system is developed based on the analysis of product geometry and production operations. A group-technology-like approach is introduced to organize components into groups according to their manufacturing feature codes using a fuzzy clustering algorithm. Production operations are optimized by a special optimization module. The two coding systems have been implemented in a feature-based, integrated concurrent design system for generating design candidates and planning production processes.

Optimal process sequence identification and optimal process tolerance assignment in computer-aided process planning

Abstract An approach for generating the optimal process tolerances and for evaluating alternative process sequences in process planning is presented. The formulations for evaluating production costs of a process sequence and its production operations are established. Several new production cost-tolerance models for production operations, which can significantly improve the modelling accuracy of empirical data of typical production processes, are introduced. Constrained nonlinear optimization is applied to identify the optimal process sequence and to determine the optimal process tolerances with least production costs. The method can improve present CAPP methods by introducing quantitative analysis. It can be combined with the knowledge-based generative CAPP approach to automatically generate the optimal sequence of production operations and the optimal process tolerances for a given design feature. An example is used to illustrate the method.

An intelligent contraflow control method for real-time optimal traffic scheduling using artificial neural network, fuzzy pattern recognition, and optimization

Contraflow operation is frequently used for reducing traffic congestion near tunnels and bridges where traffic demands from the opposite directions vary periodically. In this work, a generic real-time optimal contraflow control method has been introduced. The introduced method integrates two important functional components: 1) an intelligent system with artificial neural network and fuzzy pattern recognition to accurately estimate the current traffic demands and predict the coming traffic demands, and 2) a mixed-variable, multilevel, constrained optimization to identify the optimal control parameters. Application of the developed method to a case study-dynamic contraflow traffic operation at the George Massey Tunnel in Vancouver, BC, Canada-has significantly reduced traffic delay and congestion.

Feature Modeling Incorporating Tolerance and Production Process for Concurrent Design

This paper presents a new feature modeling approach incorporating tolerance and production process for concurrent design The three perspectives of mechanical features, namely design, manufacturing, and geometry, are examined. The feature definitions based upon design function, manufacturing method, as well as geometric shape and accuracy are specified, and an integrated feature-based design model is proposed.

Metamodelling and search using space exploration and unimodal region elimination for design optimization

Metamodelling based search, space exploration, and region reduction/elimination methods are effective optimization schemes for computation intensive global design optimization problems. In this work a new metamodelling, space exploration and region reduction search algorithm is introduced. This algorithm, namely Space Exploration and Unimodal Region Elimination (SEUMRE), divides the design space into key unimodal regions using design experiment data; identifies the regions that most likely contain the global minimum; fits Kriging models with additional design experiments using Latin Hypercube designs over these regions; identifies their local minima, and then the global optimum. By identifying promising unimodal regions of the objective and reducing searching space, the method can find the global optimum effectively and efficiently, particularly suited for optimization problems that require extensive computation through engineering analyses and simulations. Comparisons with existing space exploration and region elimination/reduction methods using benchmark test problems have been carried out to demonstrate the advantages of the new method. More robust and problem independent metamodelling improvements are under study.