Material Characterization of Additively Manufactured Part via Multi-Level Stochastic Upscaling Method

An integrated multiscale modeling framework that incorporates a simulation-based upscaling technique is developed and implemented for the material characterization of additively manufactured cellular structures in this paper. The proposed upscaling procedure enables the determination of homogenized parameters at multiple levels by matching the probabilistic performances between fine and coarse scale models. Polynomial chaos expansion is employed in upscaling procedure to handle the computational burden caused by the input uncertainties. Efficient uncertainty quantification is achieved at the mesocale level by utilizing the developed upscaling technique. The homogenized parameters of mesostructures are utilized again at the macroscale level in the upscaling procedure to accurately obtain the overall material properties of the target cellular structure. Actual experimental results of additively manufactured parts are integrated into the developed procedure to demonstrate the efficacy of the method.

Trade-Off Analysis of System Architecture Modularity Using Design Structure Matrix

Product modularity has been the subject of considerable research and debate in last decade. Various metrics have been proposed in design community to measure the level of modularity and various procedures have been developed to search for ideal modular architectures. These procedures are based on either manual heuristics or computer clustering algorithms. Both approaches are aimed at finding more ideal architectures by optimizing a definition of modularity. However, different desirable criteria are often in conflict with each other and improving one criteria is not feasible without a compromising effect on another. Here, we propose a procedure to find non-dominated optimal architectures where our criteria of interest are intra-cluster and extra-cluster costs. We demonstrate an approach where a designer can consider the architecture that minimizes total cost of interactions, but also allows visualization of the trade-off in increased and decreased costs when considering nearby architectures with more or less modules. An alternative approach has been to consider granularity and hierarchical clustering schemes. We also show through an example that cost optimal architectures for any choice of number of modules are not necessarily obtainable via dividing or aggregating modules, and restricting to hierarchical clustering algorithms produces non-optimal solutions at different numbers of modules.

Resource Allocation for Reduction of Epistemic Uncertainty in Simulation-Based Multidisciplinary Design

Epistemic model uncertainty is a significant source of uncertainty that affects a multidisciplinary system. In order to achieve a reliable design, it is critical to ensure that the disciplinary/subsystem simulation models are trustworthy, so that the aggregated uncertainty of system quantities of interest (QOIs) is acceptable. Uncertainty reduction can be achieved by gathering additional experiments and simulations data; however resource allocation for multidisciplinary design optimization (MDO) remains a challenging task due to the complex structure of a multidisciplinary system. In this paper, we develop a novel approach by integrating multidisciplinary uncertainty analysis (MUA) and multidisciplinary statistical sensitivity analysis (MSSA) to answer the questions about where (sampling locations), what (disciplinary responses), and which (simulations versus experiments) for allocating more resources. To manage the complexity in making the above decisions, a sequential procedure is proposed. First, the input space of a multidiscipline system is explored to identify the locations with unacceptable amounts of uncertainty with respect to the system QOIs. Next, these input locations are selected through a correlation check so that they are sparsely located in the input space, and their corresponding critical responses are identified based on MSSA. Finally, using a preposterior analysis, decisions are made about what type of resources (experimental or computational) should be allocated to the critical responses at the chosen input locations. The proposed method is applied to a benchmark electronic packaging problem to demonstrate how epistemic uncertainty is gradually reduced via gathering more data.

Value-Driven Modeling of Tactical and Operational Decisions in Support of Aerospace Product-Service Systems Design

Competitive markets and complex business-to-business environments compel manufacturers to provide innovative service offerings along with their products. This necessitates effective methodologires for developing and implementing sucessful new business strategies. This article presents an approach to model tactical and operational decisions to support the design and development of Product-Service Systems (PSSs). A combination of Quality Function Deployment and Design-to-Cost techniques is proposed as the first step of a PSS design framework that aids design engineers to determine the relations among value to customer, functional requirements, design variables and cost. The objective is to identify PSS design alternatives that deliver value to customer while respecting cost targets. An aerospace software case study is conducted to demonstrate the proposed approach.

Sensitivity and Correlation Analysis and Implications in Scalable Product Family Design for Plug-In Hybrid Electric Vehicles

Plug-in Hybrid Electric Vehicles (PHEVs) bear great promises for increasing fuel economy and decreasing greenhouse gas emissions by the use of advanced battery technologies and green energy resources. The design of a PHEV highly depends on several factors such as the selected powertrain configuration, control strategy, sizes of drivetrain components, expected range for propulsion purely by electric energy, known as AER, and the assumed driving conditions. Accordingly, design of PHEV powertrains for diverse customer segments requires thorough consideration of the market needs and the specific performance expectations of each segment. From the manufacturing perspective, these parameters provide the opportunity of mass customization because of the high degree of freedom, especially when the component sizes and control parameters are simultaneously assessed. Based on a nonconventional sensitivity and correlation analysis performed on a simulation model for power-split PHEVs in this study, the product family design (PFD) concept and its implications will be investigated, and limitations of PFD for such a complex product along with directions for efficient family design of PHEVs will be discussed.

Organizing Cells Within Non-Periodic Microarchitectured Materials That Achieve Graded Thermal Expansions

The aim of this paper is to introduce an approach for optimally organizing a variety of different unit cell designs within a large lattice such that the bulk behavior of the lattice exhibits a desired Young’s modulus with a graded change in thermal expansion over its geometry. This lattice, called a graded microarchitectured material, can be sandwiched between two other materials with different thermal expansion coefficients to accommodate their different expansions or contractions caused by changing temperature while achieving a desired uniform stiffness. First, this paper provides the theory necessary to calculate the thermal expansion and Young’s modulus of large multi-material lattices that consist of periodic (i.e., repeating) unit cells of the same design. Then it introduces the theory for calculating the graded thermal expansions of a large multimaterial lattice that consists of non-periodic unit cells of different designs. An approach is then provided for optimally designing and organizing different unit cells within a lattice such that both of its ends achieve the same thermal expansion as the two materials between which the lattice is sandwiched. A MATLAB tool is used to generate images of the undeformed and deformed lattices to verify their behavior and various examples are provided as case studies. The theory provided is also verified and validated using finite element analysis and experimentation.

Assessing and Generating Modules for Product Recovery

As technology pushes customers to buy new released products, especially mobile phone, high product replacement from the customers plays a role in increasing production rate for new products and rate of abandoned products. It accelerates environmental degradation like natural resource usage for the new products and pollutions generated by disposing the abandoned products. In this respect, product recovery is needed to reduce landfill rates, and resource usages, and prolong product lifecycle. Modular drivers such as interface design, material type, and components’ lifespan are applied to design modules for product recovery. The objective of this research is to support designers to assess initial modules and then reorganize modules for product recovery. First, according to conventional modular product design, the initial modules are generated. Then, since it is difficult to estimate how much the modules have negative effects on environment, the environmental impacts of a product are assessed by Eco-Indicator 99 based on used materials. Also, the complexity of the interface design is measured to understand how the modules are easily disassembled for upgrading and maintaining end-of-life products by using weighted-modular complexity score (wMCS). After assessing the product based on the Eco-Indicator 99 and wMCS, we apply new design guidelines to improve sustainability of a product in the end of life stage. Consequently, we compare the extent to design for sustainability before and after redesigning a product based on the design guideline. To demonstrate the effectiveness of the modular product design, we carry out a case study with a coffee maker.

Extreme Value Metamodeling for System Reliability With Time-Dependent Functions

The reliability of a system is usually measured by the probability that the system performs its intended function in a given period of time. Estimating such reliability is a challenging task when the probability of failure is rare and the responses are nonlinear and time variant. The evaluation of the system reliability defined in a period of time requires the extreme values of the responses in the predefined period of time during which the system is supposed to function. This work builds surrogate models for the extreme values of responses with the Kriging method. For the sake of computational efficiency, the method creates Kriging models with high accuracy only in the region that has high contributions to the system failure; training points of random variables and time are sampled simultaneously so that their interactions could be considered automatically. The example of a mechanism system shows the effectiveness of the proposed method.

Improving Multiobjective Multidisciplinary Optimization With a Data Mining-Based Hybrid Method

Multiobjective, multidisciplinary design optimization (MDO) of complex system is challenging due to the long computational time needed for evaluating new designs’ performances. Heuristic optimization algorithms are widely employed to overcome the local optimums, but the inherent randomness of such algorithms leads to another disadvantage: the need for a large number of design evaluations. To accelerate the product design process, a data mining-based hybrid strategy is developed to improve the search efficiency. Based on the historical information of the optimization search, clustering and classification techniques are employed to detect low quality designs and repetitive designs, and which are then replaced by promising designs. In addition, the metamodel with bias correction is integrated into the proposed strategy to further increase the probability of finding promising design regions within a limited number of design evaluations. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted to demonstrate the effectiveness of the proposed method in improving the searching efficiency.