bluuebunny/crossref_metadata_2025 huggingface dataset:
Learning Coordination in Body Motion Control: A Pattern Based Approach
Authors: Fukuda Shuichi
Published: November 2017

Motion Control is increasing its importance. Although the progress of system dynamics is remarkable, progress of human body motion control is very slow. Most of system dynamics deal with explicit knowledge, but human body motion control belongs to tacit knowledge.

Its difficulty is the number of degrees of freedom is tremendously large and human behaviors change very flexibly to cope with the changing contexts of environments and situations. Further, our body motions vary from person to person, because our bodies, muscles and joints are different. These problems make it very difficult to deal with human body motions.

Although there are many researches using motion capture, EMG, etc., they succeeded only in showing how final successful movements should be. They can show movements at each step toward this goal, but they cannot teach learners how they should coordinate their muscles or joints. Coordination or balancing plays an important role in body motion learning, But, there are very few, in any, researches which help learners learn how to coordinate or balance their muscles and joints to achieve the final successful movement.

In this paper, a solution to how we can help a learner learn to coordinate or balance in motion or motor learning is introduced. Its approach is pattern based and it uses Recognition Taguchi (RT) technique, one of the techniques of Mahalanobis Taguchi Systems. In this approach, Mahalanobis Distance (MD) is used to indicate quantitatively how a learner’s pattern of movement is close to the successful one. MD reduces multi-dimensional information to one-dimensional. RT indicates how a sample pattern matches the ideal pattern quantitatively using MD. In the regular RT approach, Unit Space (Ideal Pattern) is defined and each sample space is compared with Unit Space using MD.

But In this work, Unit Space is updated every time a learner succeeds, such as successfully riding a bicycle. And every trial movement is compared with this updated Unit Space. The primary benefits of RT are it can process large data in a very short time and it is based upon the difference between the ideal pattern and the current pattern. So, learners can understand which joints they should pay attention to in order to coordinate or balance to improve their movements. Thus, step by step, they can coordinate or balance their muscles and joints to get closer to the ideal movement.
Engineering Design Analysis Tool for Early Design Phase With Low-Fidelity Models: A Case of Hydraulic Crane
Authors: Ellman Asko , Pajunen Sami , Laine Ilari , Coatanea Eric
Published: November 2017

Model-based product design using computer simulation has become a standard design practice in most companies in mechanical engineering. However, there is a need for efficient simulation tools that can provide design-supporting information already at early design phase when the most important decisions are made. Design process and design tools need to be agile and enable iterative process where the design and its requirements can effectively be iterated.

Low-fidelity models can be part of the solution for time issue in early design phase. Low-fidelity prototypes are simplified representations of functions and concepts in the virtual prototype. Axiomatic design with low-fidelity modelling approach is a promising concept for achieving design-supporting information in an efficient way. In this method, there is a linear mapping between design parameters and system characteristics. Non-linear models of the system are linearized at the nominal point. An engineering design analysis tool (EDA tool) to enhance EDA is constructed and presented in this paper.

For evaluation of the usefulness of this tool, a case study is presented. The case study deals with a simple hydraulic crane that is manufactured from steel plate. The results of the case study design are compared with results achieved with conventional CAD and FEM tools. Modelling accuracy and required modelling and simulation efforts are compared in both cases.
Automated Graph Grammar Generation for Engineering Design With Frequent Pattern Mining
Authors: Sangelkar Shraddha , McAdams Daniel A.
Published: November 2017

Graph grammars, a technique for formulating new graphs based on a set of rules, is a very powerful tool for computational design synthesis. It is particularly suitable for discrete categorical data where principal component analysis is generally not applicable. Furthermore, this technique utilizes three different programs in conjunction with a design repository, which is opposed to traditional methods that require experts to empirically derive graph grammars. This technique can be separated into three steps. These steps are the creation of the input, graph data mining, and interpretation of the output with the intention of these steps being to automate or assist an expert with the process of extracting engineering graph grammars. Graph grammars that can then serve as guidelines during concept generation. The results of this paper show that this technique is very applicable to computational design synthesis by testing only a small number of products and still producing tangible results that coincide with empirically derived graphs. Fifty electromechanical products from the design repository are used in this study. When comparing, the machine generated grammar rules with expert derived grammar rules, it can be seen that only 14% cannot be developed, 58% cannot be mined with the current setup and 28% were mined with the current set up. However, it is important to keep in mind a few considerations. Specifically, the technique does not replace the expert. Instead, the technique acts as more of an aid than a replacement. Also, while this technique has great potential in regards to computational design synthesis, it is limited to the products in the design repository and the current implementation of the aforementioned programs. Despite these minor considerations, this work proposes application of graph data mining to derive engineering grammars.
Natural Finger Interaction for CAD Assembly Modeling
Authors: Fechter Marius , Wartzack Sandro
Published: November 2017

In current CAD software the process of assembly modeling is hindered by a large number of separate rotation and translation actions necessary, especially in case of larger assemblies. Additionally matching faces, edges or points must be selected by clicking to define the appropriate constraint. In contrast to that, the process of assembling two normal sized physical parts in the real world seems to be rather simple. That is because we know how to grasp and move objects with our hands intuitively from our everyday experience. The idea behind this contribution is to enable the product developer to assemble CAD parts in a virtual environment through natural finger interaction like in reality. Therefore we present an overall method that combines the natural finger interaction with virtual objects and the insertion of constraints between rotationally symmetric CAD parts. The developed algorithms identify matching surfaces on the basis of the geometry as well as position and orientation of the parts in 3D space. This paper highlights the method to use a combination of real-time physics simulation and a heuristic approach to achieve an intuitive interaction interface. Additionally, we describe the detection algorithms developed to find assembly relationships between rotationally symmetric CAD parts without prior constraint definition. We also present a prototype system to demonstrate the functionality of the overall method. Furthermore, challenges for future research, such as extending the functionality of the detection algorithms on additional part types, like non-rotationally symmetric shapes, are discussed.
A Cyber-Physical Gaming System for Vocational Training
Authors: Sivanathan Aparajithan , Mcgibbon Scott , Lim Theodore , Ritchie James , Abdel-Wahab Mohamed
Published: November 2017

Cyber-physical systems enable new digital ecologies in industrial and workplace lifelong learning. This paper reports on early efforts in delivering a virtual environment and system for vocational education and training (VET), in particular targeting the needs of craft and trade skills. The domain of stone masonry is presented herein, where its underpinning activities are learning through virtual environments, simulation and role play. The challenges are not only the synchronicity between physical and software components but also the in-game mechanics that incorporate building blocks of effective training and skills development strategies. A prototype body-area sensor network in a cyber-physical game environment demonstrates the interaction between virtual objects and the player-learner.
Towards a Constitutive Model That Encapsulates Microstructural Features Induced by Powder Additive Manufacturing
Authors: Achuthan Ajit , Iliopoulos Athanasios P. , Michopoulos John G. , Saunders Robert , Bagchi Amit
Published: November 2017

The microstructural characteristics of materials processed via powder-based additive manufacturing (PAM) methods can be significantly different from those made by conventional manufacturing process using the same material. In an effort to link PAM process parameters with the functional performance of manufactured part, it is necessary to identify the effect of the special microstructural features generated by PAM on the final constitutive response of the relevant materials. In the present study, a microstructure-informed constitutive model is developed to describe the mechanical behavior of solidified material produced by PAM processes. The model is based on crystal plasticity and accounts for the effect of grain size and aspect ratio of the microstructure. The effect of these dominant features is captured by considering a core and mantle configuration for the grain volume, and by introducing a grain boundary influence region. The constitutive model’s ability to capture the grain size and shape effect is demonstrated by simulating the stress-strain behavior under uniaxial loading of a representative volume element (RVE) with columnar microstructure characterized by a range of grain sizes and aspect ratios.
CFD Study of an Autonomous Submarine in Extraterrestrial Seas
Authors: Carberry Mogan S. , Sawicki P. , Bernardo C. J. , Chen D. , Sahin I. , Hartwig J. , Tafuni A.
Published: November 2017

A computational study is conducted to evaluate the performance of an extraterrestrial submarine operating in the liquid hydrocarbon seas of Saturn’s largest moon, Titan. To simulate the flow around the submarine and offer a prediction for thrust and power requirements, Computational Fluid Dynamics tools, ANSYS© FLUENT© and DualSPHysics, are utilized for the deeply submerged and near-surface conditions, respectively. Several operational scenarios are investigated and comparisons are made with other available results with a good qualitative and quantitative agreement.
Towards Multiscale Topology Optimization for Additively Manufactured Components Using Implicit Slicing
Authors: Steuben John C. , Iliopoulos Athanasios P. , Michopoulos John G.
Published: November 2017

Additive Manufacturing (AM) encompasses a set of fabrication technologies that are being used with increasing frequency in a wide variety of scientific and industrial pursuits. These technologies, which operate by successive additions of material to a domain, enable the manufacture of highly complex geometries that would otherwise be unrealizable. However, the material micro and meso-structures generated by AM processes differ remarkably from those that arise from conventional techniques and occasionally introduce unwanted functional features; this has been an obstacle to the use of AM in some applications. In the present work, we propose a multiscale method that utilizes the unique meso-scale structuring capabilities of implicit slicers for AM, in conjunction with existing topology optimization tools for the macro-scale, in order to generate functional components. The use of this method is demonstrated on the example of a hand tool. We discuss the applications of this methodology, its current limitations, and the future work required to enable its widespread use.
A Feasible Sequence Oriented Discrete Particle Swarm Optimization for Operation Sequencing in CAPP
Authors: Dou Jianping , Zhao Xia
Published: November 2017

Operation sequencing is one of crucial tasks for process planning in any CAPP system. In this study, a novel discrete particle swarm optimization (DPSO) approach is proposed to solve the operation sequencing problems in CAPP. To find the process plan with lowest machining cost efficiently, the DPSO only searches the feasible operation sequences (FOSs) satisfying precedence constraints among operations. In the DPSO, a FOS is directly represented by a permutation via a particle and the fragment crossover based updating mechanism is developed to evolve the particles. Furthermore, the fragment mutation for altering FOS and the uniform mutation for changing machine, cutting tool and tool access direction for each operation are incorporated into the DPSO to improve exploration ability. A case study involving two prismatic parts are used to verify the performance and efficiency of the DPSO. The comparison between the DPSO and two existing PSOs as well as an existing genetic algorithm shows promising higher performance of the DPSO with respect to solution quality for operation sequencing.
Nested and Simultaneous Solution Strategies for General Combined Plant and Controller Design Problems
Authors: Herber Daniel R. , Allison James T.
Published: November 2017

In this article, general combined plant and controller design or co-design problems are examined. Previous work in co-design theory imposed restrictions on the type of problems that could be posed. This article lifts many of those restrictions. The problem formulations and optimality conditions for both the simultaneous and nested solution strategies are given. Due to a number of challenges associated with the optimality conditions, practical solution considerations are discussed with a focus the motivating reasons for using direct transcription in co-design. This article highlights some of the key concepts in general co-design including general coupling, the differences between the feasible regions for each strategy, general boundary conditions, inequality path constraints, system-level objectives, and the complexity of the closed-form solutions. Three co-design test problems are provided. A number of research directions are proposed to further co-design theory including tailored solution methods for reducing total computational expense, better comparisons between the two solution strategies, and more realistic test problems.
Engineering Procedure for Positive Displacement Pump Performance Analysis Based on 1D and 3D CFD Commercial Codes
Authors: Josifovic Aleksandar , Stickland Matthew , Iannetti Aldo , Corney Jonathan
Published: November 2017

A numerical analysis methodology, which demonstrates how a 1D pipework simulation can be enhanced with the results of a 3D CFD simulation of key components, is used to estimate the performance of multi-cylinder Positive Displacement pumps. The procedure uses of a 1-D lumped fluid dynamics model whose accuracy was improved by incorporating CFD analysis of the PD pump valves. The application describes how valve loss co-efficient resulting from CFD analysis was utilised by the lumped parameter model as an input function. The results suggest that the combination of the CFD and lumped parameter approach exceeds the limitations found by Iannetti [1] in modelling the interaction between the pump chambers of a multi-cylinder pump as the simplified lumped parameter approach makes the entire multi-cylinder model affordable in terms of computational power and time required. The results obtained are validated by means of experimental tests the results of which are presented together with the numerical data. An example of the capability of the procedure developed and the support it is able to provide to designers is also presented.
Comparison of Fatigue Behaviors of Spinal Implants Under Physiological Spinal Loads: A Finite Element Pilot Study
Authors: Xu Ming , Yang James , Lieberman Isador H. , Haddas Ram
Published: November 2017

The fusion surgery is a standard treatment for scoliosis. Fatigue-related failure is one common cause for the fusion surgery implant. Due to the high cost of revision surgery, it is of clinical value to study the fatigue behaviors of the spinal implants under physiological spinal loads. In the literature, biomechanical tests and finite element (FE) methods have been used to study the fatigue of the spinal implants. Compared with biomechanical tests, FE analysis has the advantage of low cost and high efficiency. Due to the high computational cost, no FE study has been modeled the exact geometry of the pedicle screw (including the thread) in the screw-bone connection within the multi-level spine FE model. This study introduced a feasible FE-based method to predict the fatigue behaviors of the spinal implants with exact geometry of pedicle screw. One previously-validated FE spine model was utilized to provide physiological spinal loads and was bilaterally fused with pedicle screws and rods at L3-L4 spine levels. The exact geometry of the pedicle screw was simulated in this study for accurate stress prediction. The fused spine FE model was subjected to six loading directions (flexion/extension, left/right lateral bending, and left/right axial rotation). For each loading direction, a pure bending moment of 10 Nm was tested. First, FE analysis was performed for one loading cycle. Range of motion, maximum von Mises stress values of the spinal implants were recorded and compared for the six tested loading conditions. Then, based on the stress/strain history of the spinal implants for one loading cycle provided by the FE simulation, fatigue life cycles of the spinal implants were calculated using strain-based Smith-Watson-Topper equation. Flexion produced the largest range of motion at the adjacent level. Axial rotation produced the largest von Mises stress in the spinal implants. Except for lateral bending, the von Mises stress predicted in the screws fused at the superior vertebra was larger than that in the screws fused at inferior vertebra. The method introduced in this study will be used to study different screw fixation methods in the future work.
Towards an Analytical, Computational and Experimental Framework for Predicting Aging of Cathodic Surfaces
Authors: Michopoulos John G. , Iliopoulos Athanasios P. , Steuben John C. , DeGiorgi Virginia
Published: November 2017

The present work introduces the motivation, architecture and preliminary analytical and computational framework that will eventually lead to aging predictions of cathodic surfaces along with its implications on impressed current cathodic protection (ICCP) systems. This is necessary for adjusting ICCP systems in a manner that reflects the dissipative nature of cathodic surface assemblies while at the same time enabling potential electric far field requirements. We describe various approaches for developing Cathodic Surface Aging Models (CSAMs) based on both data-driven and first principles based methodologies. A computational ICCP framework is implemented to account for cathodic aging in a manner that allows the utilization of various CSAMs. An application of this framework demonstrates the applicability of the implications of the variability of the polarization curves as it is associated with cathodic surface aging. In addition to a data-driven CSAM based on a loft-surface approximation we also introduce a first principles thermodynamic theory for aging and the design of a systematic experimental task for validating and calibrating this theory.
Automated Quantitative Analysis of Terminal Tree Branch Similarity by 3D Registration
Authors: Brucculeri Joseph A. , Evans Lance , Shahbazi Zahra
Published: November 2017

A popular, but unsubstantiated view is that tree branch morphologies are similar (self-similarity) and are of an iterative nature. To date there are no studies that document plant branch self-similarities. The purpose of this research is to develop a program (3D Simquant) that estimated self-similarities among paired branch terminals quantitatively. After 3D Simquant was written, the program was verified and sensitivity analysis performed, eighty-five terminal branch pair-wise comparisons from five different tree species were analyzed. Only two branch geometries (Y and Y+1 terminals) were compared. Simple Y terminals are terminal main stems with one side branch while Y+1 terminals are main steams with two side branches. Similarities among paired branch terminals were quantified with Root-Mean-Square-Error (RMSE) after registration of images. For the five species tested, all Y terminals had RMSE values less than 1.5 which indicates they were similar. For most Y+1 terminals, RMSE values were twice that of Y terminals indicating the Y+1 samples were more dissimilar than Y terminals. Overall, the programs were accurate and rapid for an analysis of branch similarities.
Leveraging Logged Intermediate Design Attributes for Improved Knowledge Discovery in Engineering Design
Authors: Bang Hyunseung , Selva Daniel
Published: November 2017

Despite years of research efforts developing methods and decision support tools, architecting complex engineered systems remains a challenging task. Improvements in computational power and optimization algorithms have made it possible to explore large design spaces, but making sense of such datasets is difficult due to their scale and complexity. Various knowledge discovery tools and data-driven methods have been developed in the past to help system designers analyze and make use of such complex data. However, most of the currently available tools do not fully exploit the data that is generated during design space exploration and instead consider the mapping between design decisions (inputs) and objectives (outputs) as a blackbox function. In this paper, we introduce a new method that utilizes not only the design inputs and outputs, but also intermediate variables that are generated during the evaluation of each design. The tool stores all intermediate variables in a database, and then feeds them into a data mining algorithm to extract useful and human understandable features in the form of if-then rules. We show how the use of intermediate variables leads to new insights that could not be discovered with the blackbox approach, and improved knowledge discovery in the sense of features that are more compact and/or with higher predictive power. The method is demonstrated on a real-world system architecting problem of a constellation of Earth observing satellites.
A Data Science Approach for Analysis of Multi-Pass Wire Drawing
Authors: Sardeshmukh Avadhut , Reddy Sreedhar , Gautham B. P. , Joshi Amol , Panchal Jitesh
Published: November 2017

The process of reducing the cross-section of a wire by pulling the wire through a series of dies belongs to a special class of manufacturing processes that have a sequential nature. In each pass, the diameter of incoming wire is reduced by a certain factor. Determining number of passes and their configurations required to achieve the desired reduction while optimizing properties such as tensile strength, strain distribution, energy consumption, etc. is an optimization problem. An essential building block of this optimization problem is a model of a drawing pass that can predict the output properties for a given parameter configuration of the pass (forward inference), or its inverse, i.e. predict the configuration parameters required to achieve the desired properties.

In this paper, we present a case study on the application of Bayesian networks to address the problem of inference in multi-pass wire drawing. Also we explore the building of a generic model that can be used for any pass and compare its effectiveness with pass-specific models. Our findings so far are as follows: For forward inference, the generic model has prediction accuracy close enough to pass-specific models. Given that it can be used to solve a problem with arbitrary number of passes, this model is clearly more useful. Further, this being a generative model, it can be used for inverse inference as well.
An Experimental Parametric Study of Air-Based Battery Thermal Management System for Electric Vehicles
Authors: Liu Yuanzhi , Li Mao , Zhang Jie
Published: November 2017

This paper develops an experimental platform and performs a parametric study of an air-based battery thermal management system (BTMS) for electric vehicles. A flexible experimental platform with ten battery cells is built up to investigate how key BTMS design parameters affect the battery thermal performance. Three design parameters are studied in this paper, including the mass flow rate of cooling air, the heat flux from the battery cells to the cooling air, and the passage spacing size. To evaluate the thermal performance of the battery system, two metrics (i.e., the maximum temperature rise and the maximum temperature Uniformity) are used. A design of experiments (here 30 groups) are conducted to analyze how the three key design parameters affect the thermal performance of the BTMS. A computational fluid dynamics (CFD) of the BTMS is also performed to compare and help explain the experimental results. Both the experimental and CFD simulation results shows that: (i) decreasing the mass flow rate may deteriorate the thermal performance of the battery module; (ii) increasing the heat flux and enlarging the passage spacing size also deteriorate the battery thermal performance.
Driving Simulator System to Evaluate Driver’s Workload Using ADAS in Different Driving Contexts
Authors: Caruso Giandomenico , Ruscio Daniele , Ariansyah Dedy , Bordegoni Monica
Published: November 2017

The advancement of in-vehicle technology for driving safety has considerably improved. Current Advanced Driver-Assistance Systems (ADAS) make road safer by alerting the driver, through visual, auditory, and haptic signals about dangerous driving situations, and consequently, preventing possible collisions. However, in some circumstances the driver can fail to properly respond to the alert since human cognition systems can be influenced by the driving context.

Driving simulation can help evaluating this aspect since it is possible to reproduce different ADAS in safe driving conditions. However, driving simulation alone does not provide information about how the change in driver’s workload affects the interaction of the driver with ADAS.

This paper presents a driving simulator system integrating physiological sensors that acquire heart’s activity, blood volume pulse, respiration rate, and skin conductance parameters. Through a specific processing of these measurements, it is possible to measure different cognitive processes that contribute to the change of driver’s workload while using ADAS, in different driving contexts.

The preliminary studies conducted in this research show the effectiveness of this system and provide guidelines for the future acquisition and the treatment of the physiological data to assess ADAS workload.
Analysis of Patients With Spinal Cord Injury Using Motion Capture
Authors: Regazzoni Daniele , Vitali Andrea , Rizzi Caterina
Published: November 2017

The way a person moves, either in a plain walk or performing a specific task, tells a huge quantity of information about his/her physical and, eventually, neurologic condition. A large part of a physiotherapist work of assessment is based on the qualitative evaluation, mainly visual, of a person’s movements, in terms of balance, speed, control, force and other parameters. This research work aims at providing personnel involved in the rehab process with a quantitative method to assess the way movements are performed. A numerical measure of the performance, actually, allows easier and more precise assessment, eliminating bias due to subjectivity. To accomplish this goal two steps are required: 3D acquisition of the movement using a Motion Capture (Mocap) system, and analysis of collected data to extract or elaborate the final outcome in the form requested by the medical staff. The paper shows the way Mocap acquisition are performed and data are analyzed in the application with people having a complete spinal cord injury and using a wheelchair. The method has been tested with eight volunteers in the rehabilitation department of the Hospital Papa Giovanni XXIII in Bergamo, Italy.
A Decentralized Approach for Multi-Subsystem Co-Design Optimization Using Direct Collocation Method
Authors: Liu Tianchen , Azarm Shapour , Chopra Nikhil
Published: November 2017

Multi-subsystem co-design refers to the simultaneous optimization of physical plant and controller of a system decomposed into multiple interconnected co-design subsystems. In this paper, a new decentralized approach based on the direct collocation and decomposition-based optimization methods is formulated to solve multi-subsystem co-design problems. First, the problem is decomposed into physical plant and control parts. In the control part, the entire time horizon is discretized into subintervals and grid points. In this way, a continuous optimal control problem is converted into a finite dimensional nonlinear programming (NLP) problem. The optimality condition decomposition (OCD) method is employed to decompose and solve the converted NLP problem in a decentralized manner. Next, the dual decomposition method is used to optimize the plant part. Finally, the plant and control parts are connected by the gradients of Hamiltonian with respect to the plant variables. The proposed approach is applied to two examples. First, a numerical example is presented to illustrate the approach step-by-step. Then in the second example, a spring-mass-damper system is solved. For both examples, the solutions obtained by the proposed decentralized approach are compared against a centralized (all-in-one) approach.
Pattern Mining for Learning Typical Turbine Response During Dynamic Wind Turbine Events
Authors: Feremans Len , Cule Boris , Devriendt Christof , Goethals Bart , Helsen Jan
Published: November 2017

Maintenance costs are a main cost driver for offshore wind energy. Prediction of failure and particularly failure understanding can help to bring these costs down significantly. Since the wind turbine is subjected to a large number of dynamic events it is important to fully understand the turbine response to these events. Pattern mining has been used successfully for different applications. We believe it to have large potential for understanding turbine behavior based on turbine status logs. These logs record all turbine actions and can be used as input for pattern mining algorithms. This paper proposes the use of a multi-level pattern mining approach in order to minimize the number of uninteresting patterns and facilitate response understanding. The paper mainly focuses on the extraction of patterns and association rules linked to certain alarms and how they can be annotated for further use in the multi-level pattern mining approach. Several years of wind turbine data is used. The use of the approach is illustrated by detecting the characteristic pattern linked to turbine response to an Extremely High Wind Speed Alert.
Binary Image Recognition Utilizing Computer Generated Templates
Authors: Bielecki Dustin , Jaiswal Prakhar , Rai Rahul
Published: November 2017

This paper covers a method of taking images of physical parts which are then preprocessed and compared against CAD generated templates. A pseudo milling operation was performed on discretized points along CAD generated mill paths to create binary image templates. The computer-generated images were then tested against one another as a preliminarily sorting technique. This was done to reduce the number of sorting approaches used, by selecting the most reliable and discerning ones, and discarding the others. To apply the selected sorting methods for comparing CAD generated images and the images of physical parts, a translational and scaling normalization technique was implemented. Rotational variation occurs while scanning physical parts and it was addressed using two different techniques: first by determination of best rotation based on modified-Hausdorff distance (MHD); and second by comparing against all CAD based images for all template rotations. The proposed approach for automated sorting of physical parts was demonstrated by categorizing multiple geometries.
Foot-Ground Interaction for 2D Gait Using Concurrent Optimization
Authors: Xiang Yujiang , Ramirez Benjamin , Hoffman Sarah , Chowdhury Tonoy
Published: November 2017

Foot-ground interaction is modeled for a human gait simulation by using a 2D skeletal model with 12 degrees of freedom (DOF). Three contacting elements are attached to the heel, phalangeal, and toe sections respectively. The contacting process is modeled using an inverse optimization approach, in which the contacting force due to the penetration deformation and velocity is equal to the balanced ground reaction force (GRF). This is set as an equality constraint in the walking optimization formulation. A predictive dynamics approach is used to predict the walking motion and to optimize the contacting process. The results indicated that the contacting model can realistically match the GRF, and the resulting gait motion, contacting penetration, and contacting parameters are all optimized simultaneously. The optimal solution is obtained in seconds. This demonstrates an efficient way to model the foot-ground contacting deformation process using an inverse optimization method and eliminates the need for integrating equations of motion (EOM).
Towards a Distributed Multiagent Learning-Based Design Optimization Method
Authors: Hulse Daniel , Gigous Brandon , Tumer Kagan , Hoyle Christopher , Tumer Irem Y.
Published: November 2017

Complex engineered systems create many design challenges for engineers and organizations because of the interactions between subsystems and desire for optimality. In some conceptual-level optimizations, the design problem is simplified to consider the most important variables in an all-in-one optimization framework. This work introduces a stochastic optimization method which uses a distributed multiagent design method in which action-value based learning agents make individual design choices for each component. These agents use a probabilistic action-selection strategy based on the learned objective values of each action. This distributed multiagent system is applied to a simple quadrotor optimization problem in an all-in-one optimization framework, and compared with the performance of centralized methods. Results show the multiagent system is capable of finding comparable designs to centralized methods in a similar amount of computational time. This demonstrates the potential merit of a multiagent approach for complex systems design.
Mesh Independent Modeling of Essential, Interface and Periodic Boundary Conditions Using Step Boundary Method
Authors: Manogaran Aswath , Kumar Ashok V.
Published: November 2017

Recently there has been increased interest in developing finite element modeling schemes where the geometry is independent of the mesh and is immersed in a uniform structured background mesh. The main motivation of this approach has been to avoid the difficulties of generating a conforming mesh for complex geometry. For this type of mesh independent analysis, modeling of assemblies, composite microstructures, and interaction at the interfaces is studied. In this paper, we present step boundary method as a well-suited method for applying essential boundary conditions, contact at interfaces as well as imposing periodic boundary conditions. This method uses step functions to construct trial solutions that satisfy boundary conditions as well as interface contact conditions and periodic boundary conditions. A generalized version of this approach is presented that allows arbitrary coordinate system for imposing boundary conditions and permits contact with sliding. Several test examples are used to validate this approach.
Towards Computational Synthesis of Microstructural Crystalline Morphologies for Additive Manufacturing Applications
Authors: Michopoulos John G. , Iliopoulos Athanasios P. , Steuben John C. , Birnbaum Andrew J. , Fu Yao , Song Jeong-Hoon
Published: November 2017

Powder-based additive manufacturing technologies introduce severe variations in microstructure in terms of grain size and aspect ratio that, coupled with porosity, can result in dramatic effects on the functional (mechanical, thermal, fatigue, fracture etc.) performance of as-produced parts. In the context of Integrated Computational Materials Engineering (ICME), it is essential develop a computationally efficient approach for generating synthetic microstructural morphologies that reflect these process-induced features. In the present paper, we employ two methodologies for computing the evolution of metal solidification at the microstructural level as a function of process parameters associated with additive manufacturing. The first method is the Continuum Diffuse Interface Model (CDM) applied to an arbitrary material system, and the second, the Multi-Phase Field Model (MPFM) applied to pure nickel (Ni). We present examples of microstructures generated by these methods within the context of additive manufacturing.
Modeling the Dynamics of Innovation in Engineered Systems
Authors: Naim Aziz Michel , Lewis Kemper
Published: November 2017

Innovation in engineering design remains a topic that lacks a formal and consistent framework. It is difficult developing a consistent definition of innovation since it is a phenomenon that is multidisciplinary in nature and present in every facet of our lives from engineering products to the performing and culinary arts. It is thus important to distinguish the general definition of innovation with its applicability in the engineering domain. Furthermore, the dynamic nature of innovation is oftentimes lost when designers try to model innovation in a complex system. The reality is that the perceived engineering innovation in a complex system can be influenced at various levels. It is thus important to provide a tool for designers that provides a clear understanding of the effects of technological progress on the overall performance capability of a complex system. This paper provides a framework to model engineering innovation in engineered systems by introducing an N-Dimensional Growth Model and applying it in the early stages of a design process in order to assist the designer in the selection of technologically innovative concepts.
ConvNet-Based Optical Recognition for Engineering Drawings
Authors: Brock Andrew , Lim Theodore , Ritchie J. M. , Weston Nick
Published: November 2017

End-to-end machine analysis of engineering document drawings requires a reliable and precise vision frontend capable of localizing and classifying various characters in context. We develop an object detection framework, based on convolutional networks, designed specifically for optical character recognition in engineering drawings. Our approach enables classification and localization on a 10-fold cross-validation of an internal dataset for which other techniques prove unsuitable.
Bayesian Network Learning for Uncertainty Quantification
Authors: Hu Zhen , Mahadevan Sankaran
Published: November 2017

Bayesian Networks (BNs) are being studied in recent years for system diagnosis, reliability analysis, and design of complex engineered systems. In several practical applications, BNs need to be learned from available data before being used for design or other purposes. Current BN learning algorithms are mainly developed for networks with only discrete variables. Engineering design problems often consist of both discrete and continuous variables. This paper develops a framework to handle continuous variables in BN learning by integrating learning algorithms of discrete BNs with Gaussian mixture models (GMMs). We first make the topology learning more robust by optimizing the number of Gaussian components in the univariate GMMs currently available in the literature. Based on the BN topology learning, a new Multivariate Gaussian Mixture (MGM) strategy is developed to improve the accuracy of conditional probability learning in the BN. A method is proposed to address this difficulty of MGM modeling with data of mixed discrete and continuous variables by mapping the data for discrete variables into data for a standard normal variable. The proposed framework is capable of learning BNs without discretizing the continuous variables or making assumptions about their CPDs. The applications of the learned BN to uncertainty quantification and model calibration are also investigated. The results of a mathematical example and an engineering application example demonstrate the effectiveness of the proposed framework.
Why Open Source?: Exploring the Motivations of Using an Open Model for Hardware Development
Authors: Li Zhuoxuan , Seering Warren , Ramos Joshua David , Yang Maria , Wallace David Robert
Published: November 2017

Following the successful adoption of the open source model in the software realm, open source is becoming a new design paradigm in hardware development. Open source models for tangible products are still in its infancy, and many studies are required to demonstrate its application to for-profit product development. It is an alluring question why entrepreneurs decide to use an open model to develop their products under risks and unknowns, such as infringement and community management. The goal of this paper is to investigate the motivations of entrepreneurs of open source hardware companies. The leaders and founders of twenty-three companies were interviewed to understand their motivation and experiences in creating a company based on open source hardware. Based on these interviews, we generated a hierarchical framework to explain these motivations, where each level of the framework has been defined, explained and illustrated with representative quotes. The motivations of open source action are framed by two categories in the paper: 1) Intrinsic Motivation, which describes the motivations of an entrepreneur as an individual, who needs personal satisfaction, enjoyment as well as altruism and reciprocity; 2) Extrinsic Motivation, which describes motivations of an entrepreneur whose identity is as a for-profit company leader.
Expediting Build Time, Material, and Cost Estimation for Material Extrusion Processes to Enable Mobile Applications
Authors: Dinda Shantanab , Modi Devansh , Simpson Timothy W. , Tedia Saish , Williams Christopher B.
Published: November 2017

Development of Material Extrusion systems for 3D printing and their increase in accessibility has been instrumental in the rapid growth of individuals with hands-on experience in additive manufacturing. Fabricating parts using Material Extrusion has progressed from being a novel and expensive ordeal to a cheap, easy, and relatively fast method. However, this necessitates that the user understands the circumstances under which 3D printing is appropriate for a given part. This paper introduces software algorithms and an “App” to help users gauge the feasibility of fabricating parts using material extrusion. The algorithm uses novel voxel-based manufacturability analyses, which takes the desired STL file as input and generates a voxelized model with supports. The software estimates build time, weight of material required for the part and supports, and the cost of 3D printing based on specified machine parameters, allowing users to make an informed decision on process selection. The visuals generated by the algorithm also allow the user to determine the appropriate build orientation. The algorithm’s predictions are compared to experimental results for validation, with the final version of the code serving as the backbone for a mobile “App” built for rapid visual evaluation of parts in terms of orientation, build time, and cost of printing. Ongoing and future work is also discussed.
Examining the Fidelity of On-the-Fly Assembly Inspection With a Low-Cost RGB-D Camera
Authors: Radkowski Rafael , Ingebrand Jarid
Published: November 2017

This paper examines the fidelity of a commodity range camera for assembly inspection in use cases such as augmented reality-based assembly assistance. The objective of inspection is to determine whether a part is present and correctly aligned. In our scenario, shortly after the mechanics assembled the part, which is denoted as on-the-fly inspection. Our approach is based on object tracking and a subsequent discrepancy analysis. Object tracking determines the presence, position, and orientation of parts. The discrepancy analysis facilitates to determine whether the parts are correctly aligned. In comparison to a naive position and orientation difference approach, the discrepancy analysis incorporates the dimensions of parts, which increases the alignment fidelity. To assess this, an experiment was conducted in order to determine the accuracy range. The results indicate a sufficient accuracy for larger parts a noticeable improvement in comparison to the naive approach.
Optimal Sensor Placement Methods for Active Power Electronic Systems
Authors: Peddada Satya R. T. , Tannous Pamela J. , Alleyne Andrew G. , Allison James T.
Published: November 2017

Accurate temperature estimation of high density active power electronic systems is vital for dynamic thermal management. Accurate and reliable estimation is especially important in regions that are close to failure, either due to high temperature or significant materials or component sensitivity. Improved estimation can support lower safety factors and enhanced system performance. An investigation of optimal temperature sensor placement methods is presented here, focusing primarily on methods utilizing information-based metrics. In addition, physics-based metrics are explored in an initial study that may have the potential to be more closely aligned with overall system utility. Studies are based on a 2 kW, single-phase, seven-level, GaN-based inverter. A lumped-parameter reduced-order thermal model, developed in previous work, is used for real-time temperature estimation. A continuous relaxation of a 2D placement domain led to a novel linear programming formulation that supports solution of finely-discretized sensor placement problems with minimal computational expense. Improved sensor placement performance metrics account for multiple loading conditions and estimation accuracy with respect to failure prevention.
System State Monitoring to Facilitate Safe and Efficient Human-Robot Collaboration in Hybrid Assembly Cells
Authors: Morato Carlos W. , Kaipa Krishnanand N. , Gupta Satyandra K.
Published: November 2017

Hybrid assembly cells allow humans and robots to collaborate on assembly tasks. We consider a model of the hybrid cell in which a human and a robot asynchronously collaborate to assemble a product. The human retrieves parts from a bin and places them in the robot’s workspace, while the robot picks up the placed parts and assembles them into the product. Realizing hybrid cells requires -automated plan generation, system state monitoring, and contingency handling. In this paper we describe system state monitoring and present a characterization of the part matching algorithm. Finally, we report results from human-robot collaboration experiments using a KUKA robot and a 3D-printed mockup of a simplified jet-engine assembly to illustrate our approach.