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Accepted Manuscripts

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research-article  
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
research-article  
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
research-article  
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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