The Experts below are selected from a list of 6 Experts worldwide ranked by ideXlab platform
Joshua D Summers - One of the best experts on this subject based on the ideXlab platform.
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Assembly time estimation Assembly Mate based structural complexity metric predictive modeling
Journal of Computing and Information Science in Engineering, 2014Co-Authors: Joseph E Owensby, Joshua D SummersAbstract:This paper presents an autoMated tool for estimating Assembly times of products based on a three step process: connectivity graph generation from Assembly Mate information, structural complexity metric analysis of the graph, and application of the complexity metric vector to predictive artificial neural network models. The tool has been evaluated against different training set cases, suggesting that partially defined Assembly models and training product variety are critical characteristics. Moreover, the tool is shown to be robust and insensitive to different modeling engineers. The tool has been implemented in a commercial CAD system and shown to yield results of within ±25% of predicted values. Additional extensions and experiments are recommended to improve the tool.
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Comparison of Graph Generation Methods for Structural Complexity Based Assembly Time Estimation
Journal of Computing and Information Science in Engineering, 2014Co-Authors: Essam Z. Namouz, Joshua D SummersAbstract:This paper compares two different methods of graph generation for input into the complexity connectivity method to estiMate the Assembly time of a product. The complexity connectivity method builds predictive models for Assembly time based on twenty-nine complexity metrics applied to the product graphs. Previously the part connection graph was manually created, but recently the Assembly Mate Method and the Interference Detection Method have introduced new autoMated tools for creating the part connectivity graphs. These graph generation methods are compared on their ability to predict the Assembly time of multiple products. For this research, eleven consumers products are used to train an artificial neural network and three products are reserved for testing. The results indicate that both the Assembly Mate Method and the Interference Detection Method can create connectivity graphs that predict the Assembly time of a product to within 45% of the target time. The Interference Detection Method showed less variability than the Assembly Mate Method in the time estimations. The Assembly Mate Method is limited to only SolidWorks Assembly files, while the Interference Detection Method is more flexible and can operate on different file formats including IGES, STEP, and Parasolid. Overall, both of the graph generation methods provide a suitable autoMated tool to form the connectivity graph, but the Interference Detection Method provides less variance in predicting the Assembly time and is more flexible in terms of file types that can be used.
Joseph E Owensby - One of the best experts on this subject based on the ideXlab platform.
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Assembly time estimation Assembly Mate based structural complexity metric predictive modeling
Journal of Computing and Information Science in Engineering, 2014Co-Authors: Joseph E Owensby, Joshua D SummersAbstract:This paper presents an autoMated tool for estimating Assembly times of products based on a three step process: connectivity graph generation from Assembly Mate information, structural complexity metric analysis of the graph, and application of the complexity metric vector to predictive artificial neural network models. The tool has been evaluated against different training set cases, suggesting that partially defined Assembly models and training product variety are critical characteristics. Moreover, the tool is shown to be robust and insensitive to different modeling engineers. The tool has been implemented in a commercial CAD system and shown to yield results of within ±25% of predicted values. Additional extensions and experiments are recommended to improve the tool.
Essam Z. Namouz - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Graph Generation Methods for Structural Complexity Based Assembly Time Estimation
Journal of Computing and Information Science in Engineering, 2014Co-Authors: Essam Z. Namouz, Joshua D SummersAbstract:This paper compares two different methods of graph generation for input into the complexity connectivity method to estiMate the Assembly time of a product. The complexity connectivity method builds predictive models for Assembly time based on twenty-nine complexity metrics applied to the product graphs. Previously the part connection graph was manually created, but recently the Assembly Mate Method and the Interference Detection Method have introduced new autoMated tools for creating the part connectivity graphs. These graph generation methods are compared on their ability to predict the Assembly time of multiple products. For this research, eleven consumers products are used to train an artificial neural network and three products are reserved for testing. The results indicate that both the Assembly Mate Method and the Interference Detection Method can create connectivity graphs that predict the Assembly time of a product to within 45% of the target time. The Interference Detection Method showed less variability than the Assembly Mate Method in the time estimations. The Assembly Mate Method is limited to only SolidWorks Assembly files, while the Interference Detection Method is more flexible and can operate on different file formats including IGES, STEP, and Parasolid. Overall, both of the graph generation methods provide a suitable autoMated tool to form the connectivity graph, but the Interference Detection Method provides less variance in predicting the Assembly time and is more flexible in terms of file types that can be used.
