Multiscale Design for Solid Freeform Fabrication

One of the advantages of solid freeform fabrication is the ability to fabricate complex structures on multiple scales, from the macroscale features of an overall part to the mesoscale topology of its internal architecture and even the microstructure or composition of the constituent material. This manufacturing freedom poses the challenge of designing across these scales, especially when a part with designed mesostructure is part of a larger system with changing requirements that propagate across scales. A setbased multiscale design method is presented for coordinating design across scales and reducing iterative redesign of SFF parts and their mesostructures. The method is applied to design a miniature unmanned aerial vehicle system. The system is decomposed into disciplinary subsystems and constituent parts, including wings with honeycomb mesostructures that are topologically tailored for stiffness and strength and fabricated with selective laser sintering. The application illustrates how the design of freeform parts can be coordinated more efficiently with the design of parent systems.Mechanical Engineerin

Optimizing electric adjustment mechanism using the combination of multi-body dynamics and control

Abstract : Optimization was carried out on the electric adjustment mechanism for transplanter by using the multidisciplinary design with weight, transmission efficiency, vibration frequency, and control error as the optimization goals. Then, a collaborative optimization model for the multidisciplinary design of a mechanism system was constructed. Based on ISIGHT software, the multidisciplinary design integration platform for the electric adjustment mechanism was built. A hybrid algorithm comprising the dual sequential quadratic programming method and the multi-island genetic algorithm was used to calculate the model. Optimization results show that the weight of the electric adjustment mechanism drops by 13.10%, its vibration frequency decreases by 27.71%, its transmission efficiency increases by 20.26%, and the control error decreases by 36.98%. Under the mutual coordination and balance of all discipline goals, the optimal values of the design variables of the electric adjustment mechanism indicate overall optimal performance

APPROXIMATION ASSISTED MULTIOBJECTIVE AND COLLABORATIVE ROBUST OPTIMIZATION UNDER INTERVAL UNCERTAINTY

Optimization of engineering systems under uncertainty often involves problems that have multiple objectives, constraints and subsystems. The main goal in these problems is to obtain solutions that are optimum and relatively insensitive to uncertainty. Such solutions are called robust optimum solutions. Two classes of such problems are considered in this dissertation. The first class involves Multi-Objective Robust Optimization (MORO) problems under interval uncertainty. In this class, an entire system optimization problem, which has multiple nonlinear objectives and constraints, is solved by a multiobjective optimizer at one level while robustness of trial alternatives generated by the optimizer is evaluated at the other level. This bi-level (or nested) MORO approach can become computationally prohibitive as the size of the problem grows. To address this difficulty, a new and improved MORO approach under interval uncertainty is developed. Unlike the previously reported bi-level MORO methods, the improved MORO performs robustness evaluation only for optimum solutions and uses this information to iteratively shrink the feasible domain and find the location of robust optimum solutions. Compared to the previous bi-level approach, the improved MORO significantly reduces the number of function calls needed to arrive at the solutions. To further improve the computational cost, the improved MORO is combined with an online approximation approach. This new approach is called Approximation-Assisted MORO or AA-MORO. The second class involves Multiobjective collaborative Robust Optimization (McRO) problems. In this class, an entire system optimization problem is decomposed hierarchically along user-defined domain specific boundaries into system optimization problem and several subsystem optimization subproblems. The dissertation presents a new Approximation-Assisted McRO (AA-McRO) approach under interval uncertainty. AA-McRO uses a single-objective optimization problem to coordinate all system and subsystem optimization problems in a Collaborative Optimization (CO) framework. The approach converts the consistency constraints of CO into penalty terms which are integrated into the subsystem objective functions. In this way, AA-McRO is able to explore the design space and obtain optimum design solutions more efficiently compared to a previously reported McRO. Both AA-MORO and AA-McRO approaches are demonstrated with a variety of numerical and engineering optimization examples. It is found that the solutions from both approaches compare well with the previously reported approaches but require a significantly less computational cost. Finally, the AA-MORO has been used in the development of a decision support system for a refinery case study in order to facilitate the integration of engineering and business decisions using an agent-based approach

Network Target Coordination for Design Optimization of Decomposed Systems

A complex engineered system is often decomposed into a number of different subsystems that interact on one another and together produce results not obtainable by the subsystems alone. Effective coordination of the interdependencies shared among these subsystems is critical to fulfill the stakeholder expectations and technical requirements of the original system. The past research has shown that various coordination methods obtain different solution accuracies and exhibit different computational efficiencies when solving a decomposed system. Addressing these coordination decisions may lead to improved complex system design. This dissertation studies coordination methods through two types of decomposition structures, hierarchical, and nonhierarchical. For coordinating hierarchically decomposed systems, linear and proximal cutting plane methods are applied based on augmented Lagrangian relaxation and analytical target cascading (ATC). Three nonconvex, nonlinear design problems are used to verify the numerical performance of the proposed coordination method and the obtained results are compared to traditional update schemes of subgradient-based algorithm. The results suggest that the cutting plane methods can significantly improve the solution accuracy and computational efficiency of the hierarchically decomposed systems. In addition, a biobjective optimization method is also used to capture optimality and feasibility. The numerical performance of the biobjective algorithm is verified by solving an analytical mass allocation problem. For coordinating nonhierarchically decomposed complex systems, network target coordination (NTC) is developed by modeling the distributed subsystems as different agents in a network. To realize parallel computing of the subsystems, NTC via a consensus alternating direction method of multipliers is applied to eliminate the use of the master problem, which is required by most distributed coordination methods. In NTC, the consensus is computed using a locally update scheme, providing the potential to realize an asynchronous solution process. The numerical performance of NTC is verified using a geometrical programming problem and two engineering problems

A set-based approach for coordination of multi-level collaborative design studies

Presented in this paper is a framework for design coordination of hierarchical (multi-level) design studies. The proposed framework utilizes margin management and set-based design principles for handling the challenges associated with vertical and horizontal design coordination. The former is based on flexible constraints/margins, while the latter is handled by intersecting feasible design spaces across different teams. The framework is demonstrated with an industrial test-case from the UK ATI APPROCONE (Advanced PROduct CONcept analysis Environment) project