Accepted Manuscripts

Nicolas Soria, Mitchell K Colby, Irem Y. Tumer, Christopher Hoyle and Kagan Tumer
J. Comput. Inf. Sci. Eng   doi: 10.1115/1.4038158
In complex engineering systems, complexity may arise by design, or as a by-product of the system’s operation. In either case, the root cause of complexity is the same: the unpredictable manner in which interactions among components modify system behavior. Traditionally, two different approaches are used to handle such complexity: (i) a centralized design approach where the impacts of all potential system states and behaviors resulting from design decisions must be accurately modeled; and (ii) an approach based on externally legislating design decisions, which avoid such difficulties, but at the cost of expensive external mechanisms to determine trade-offs among competing design decisions. Our approach is a hybrid of the two approaches, providing a method in which decisions can be reconciled without the need for either detailed interaction models or external mechanisms. A key insight of this approach is that complex system design, undertaken with respect to a variety of design objectives, is fundamentally similar to the multiagent coordination problem, where component decisions and their interactions lead to global behavior. The results of this paper demonstrate that a team of autonomous agents using a cooperative coevolutionary algorithm can effectively design a complex engineered system. This publication utilized a system model of a Formula SAE racing vehicle to illustrate and simulate the methods and potential results.
TOPICS: Design, Engineering systems and industry applications, Vehicles, Teams, Complex systems, Tradeoffs, Algorithms
Nima Rafibakhsh and Matthew I. Campbell
J. Comput. Inf. Sci. Eng   doi: 10.1115/1.4038144
This paper is arranged in three sections: the first section is a hierarchical method based on clustering and a fuzzy membership system where the tessellated 3D models are classified into their containing primitives: cylinder, cone, sphere and flat. In the second section, Automated Assembly Planning (AAP) is considered as the main application of our novel hierarchical primitive classification approach. The classified primitives obtained from the first section are used to define the removal directions between mating parts in an assembly model. Finally a fuzzification method is used to express the uncertainty of the detected connections between every pair of parts. The acquired uncertainties are used in a user interaction process to approve, deny or modify the connections with higher uncertainties.
TOPICS: Solids, Manufacturing, Cylinders, Three-dimensional models, Uncertainty
Bruno S. Machado, Nilanjan Chakraborty, Mohamed Mamlouk and Prodip K. Das
J. Comput. Inf. Sci. Eng   doi: 10.1115/1.4037942
In this study, a three-dimensional agglomerate model of an anion exchange membrane fuel cell is proposed in order to account the detailed composition of the catalyst layers (CLs). Here, a detailed comparison between the agglomerate and a macro-homogeneous model is provided, elucidating the effects of the first implementation on the overall performance and the individual losses, the effects operating temperature and inlet relative humidity on the cell performance, and the catalyst layer utilisation by the effectiveness factor. The results show that the macro-homogeneous model overestimates the cell performance compared to the agglomerate model due to the resistances associated with the species and ionic transport in the catalyst layers. Consequently the hydration is negatively affected, resulting in a higher ohmic resistance. The activation overpotential is over-predicted by the macro-homogeneous model, as the agglomerate model relates the transportation resistances within the domain with the CL composition. Despite the higher utilisation in the anode CL, the cathode CL utilisation presents significant drop near the membrane-CL interface, due to the higher current density and low oxygen concentration. Additionally, the effects of operating temperature and relative humidity at the flow channel inlet were analysed. Similar to the macro-homogeneous model, the overall cell performance of the agglomerate model is enhanced with increasing operating temperature due to the better electrochemical kinetics. However, as the relative humidity at the inlet is reduced, the overall performance of the cell deteriorates due to the poor hydration of the membrane.
TOPICS: Fuel cells, Membranes, Catalysts, Operating temperature, Current density, Oxygen, Transportation systems, Flow (Dynamics), Anodes, Overvoltage
Zhenjun Ming, Guoxin Wang, Yan Yan, Jitesh H. Panchal, David (Chung Hyun) Goh, Janet K. Allen and Farrokh Mistree
J. Comput. Inf. Sci. Eng   doi: 10.1115/1.4037934
The design of complex engineering systems requires that the problem is decomposed into sub-problems of manageable size. From the perspective of decision-based design, typically this results in a set of hierarchical decisions. It is critically important for computational frameworks for engineering system design to be able to capture and document this hierarchical decision making knowledge for reuse. Ontology is a formal knowledge modeling scheme that provides a means to structure engineering knowledge in a retrievable, computer-interpretable, and reusable manner. In our earlier work, we have created ontologies to represent individual design decisions (selection and compromise). Here we extend the selection and compromise decision ontologies to an ontology for hierarchical decisions. This can be used to represent workflows with multiple decisions coupling together. The core of the proposed ontology includes the coupled Decision Support Problem (DSP) construct, and two key Classes, namely, Process that represents the basic hierarchy building blocks wherein the DSPs are embedded, and Interface to represent the DSP information flows that link different Processes to a hierarchy. The efficacy of the ontology is demonstrated using a portal frame design example. Advantages of this ontology are that it is decomposable and flexible enough to accommodate the dynamic evolution of a process along the design timeline.
TOPICS: Design, Ontologies, Engineering systems and industry applications, Modeling, Flow (Dynamics), Blocks (Building materials), Gates (Closures), Computers, Decision making, Workflow
Matthew Dering, Conrad Tucker and Soundar Kumara
J. Comput. Inf. Sci. Eng   doi: 10.1115/1.4037434
An important part of the Engineering Design process is prototyping, where designers build and test their designs. Current methods for reducing time spent during the prototyping process have focused primarily on optimizing designer to designer interactions, as opposed to designer to tool interactions. Advancements in commercially-available sensing systems (e.g., the Kinect) and machine learning algorithms have opened the pathway towards real-time observation of designers behavior in engineering workspaces during prototype construction. Towards this end, this work hypothesizes that an object O being used for task i is distinguishable from object O being used for task j, where i is the correct task and j is the incorrect task. The contributions of this work are i) the ability to recognize these objects in a free roaming engineering workshop environment and ii) the ability to distinguish between the correct and incorrect use of objects used during a prototyping task. By distinguishing the difference between correct and incorrect uses, incorrect behavior can be detected and quickly corrected. A case study is presented involving participants in an engineering design workshop to demonstrate the effectiveness of the proposed methodology, by asking participants to perform correct and incorrect tasks with a tool. The participants' movements are analyzed by an unsupervised clustering algorithm to determine if there is a statistical difference between tasks being performed correctly and incorrectly. Clusters which are a plurality incorrect are found to be significantly distinct for each node considered by the methodology, each with p << 0.001.
TOPICS: Machinery, Workshops (Work spaces), Algorithms, Engineering design processes, Construction, Engineering design, Engineering prototypes
Dipanjan Ghosh, Andrew Olewnik and Kemper Lewis
J. Comput. Inf. Sci. Eng   doi: 10.1115/1.4037435
Usage context is considered a critical driving factor for customers' product choices. In addition, physical use of a product (i.e., user-product interaction) dictates a number of customer perceptions (e.g. level of comfort). In the emerging Internet of Things (IoT), this work hypothesizes that it is possible to understand product usage and level of comfort while it is 'in-use' by capturing the user-product interaction data. Mining the data and understanding the usage context along with comfort of the user adds a new dimension to the product design field. There has been tremendous progress in the field of data analytics, but the application in product design is still nascent. In this work, application of feature-learning methods for the identification of product usage context and level of comfort is demonstrated, where usage context is limited to the activity of the user. A novel generic architecture using foundations in Convolution Neural Network has been developed and applied for a walking activity classification using smartphone accelerometer data. Results are compared with feature-based machine learning algorithms (neural network and support vector machines), and demonstrate the benefits of using the feature-learning methods over the feature-based machine-learning algorithms. To demonstrate the generic nature of the architecture, an application towards comfort level prediction is presented using force sensor data of a sensor-integrated shoe.
TOPICS: Machinery, Mining, Sensors, Dimensions, Accelerometers, Algorithms, Artificial neural networks, Force sensors, Internet, Product design, Support vector machines
Soji Yamakawa and Kenji Shimada
J. Comput. Inf. Sci. Eng   doi: 10.1115/1.4037227
This paper presents a new method for extracting feature edges from CAD-generated triangulations. The major advantage of this method is that it tends to extract feature edges along the centroids of the fillets rather than along the edges where fillets are connected to non-fillet surfaces. Typical industrial models include very small-radius fillets between relatively large surfaces. Such narrow fillets are unnecessary details for many types of applications and cause numerous problems in the downstream processes. One solution to the small-radius fillet problem is to divide the fillets along the centroid and then merge each fragment of the fillet with non-fillet surfaces. The proposed method can find such fillet centroids and can substantially reduce the adverse effects of such small-radius fillets. The method takes a triangulated geometry as input and first simplifies the model so that small-radius, or "small," fillets are collapsed into line segments. The simplification is based on the normal errors and therefore is scale-independent. It is particularly effective for a shape that is a mix of small and large features. Then the method creates segmentation in the simplified geometry, which is then transformed back to the original shape while maintaining the segmentation information. The groups of triangles are expanded by applying a region-growing technique to cover all triangles. The feature edges are finally extracted along the boundaries between the groups of triangles.
TOPICS: Computer-aided design, Errors, Geometry, Image segmentation, Shapes

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