A dimensional tolerancing knowledge management system using Nested Ripple Down Rules (NRDR)

This paper proposes to use a knowledge acquisition (KA) approach based on Nested Ripple Down Rules(NRDR) to assist in mechanical design focusing on dimensional tolerancing. A knowledge approach to incrementally model expert design processes is implemented. The knowledge is acquired in the context of its use, which substantially supports the KA process. The knowledge is captured which human designers utilize in order to specify dimensional tolerances on shafts and mating holes in order to meet desired classes of fit as set by relevant engineering standards in order to demonstrate the presented approach. The developed dimensional tolerancing knowledge management system would help mechanical designers become more effective in the time-consuming tolerancing process of theirdesigns in the future

Rotary Friction Welding Versus Fusion Butt Welding of Plastic Pipes – Feasibility and Energy Perspective

According to the Plastics Pipe Institute, butt fusion is the most widely used method for joining lengths of PE pipe and pipe to PE fittings “by heat fusion” (https://plasticpipe.org/pdf/chapter09.pdf). However, butt-welding is not energy-cognizant from the point of view of a phase-change fabrication method. This is because the source of heating is external (heater plate). The initial heating and subsequent maintenance at relatively high temperature (above 200 C for welding of high-density polyethylene pipe) is energy intensive. Rotary friction welding, on the other hand focuses the energy where and when as needed because it uses electric motor to generate mechanical (spinning) motion that is converted to heat. This work will make the case for friction heating as energy efficient. An initial feasibility study will also be introduced to demonstrate that the resulting welded pipe joints may be of comparable quality to those produced by butt fusion and to virgin PE material

Three-dimensional printing of mitral valve models using echocardiographic data improves the knowledge of cardiology fellow physicians in training

BackgroundHigh fidelity three-dimensional Mitral valve models (3D MVM) printed from echocardiography are currently being used in preparation for surgical repair.AimWe hypothesize that printed 3DMVM could have relevance to cardiologists in training by improving their understanding of normal anatomy and pathology.MethodsSixteen fellow physicians in pediatric and adult cardiology training were recruited. 3D echocardiography (3DE) video clips of six mitral valves (one normal and five pathological) were displayed and the fellows were asked to name the prolapsing segments in each. Following that, three still images of 3D MVMs in different projections: enface, profile and tilted corresponding to the same MVs seen in the clip were presented on a screen. Participating physicians were presented with a comprehensive questionnaire aimed at assessing whether the 3D MVM has improved their understanding of valvular anatomy. Finally, a printed 3D MVM of each of the valves was handed out, and the same questionnaire was re-administered to identify any further improvement in the participants' perception of the anatomy.ResultsThe correct diagnosis using the echocardiography video clip of the Mitral valve was attained by 45% of the study participants. Both pediatric and adult trainees, regardless of the year of training demonstrated improved understanding of the anatomy of MV after observing the corresponding model image. Significant improvement in their understanding was noted after participants had seen and physically examined the printed model.ConclusionPrinted 3D MVM has a beneficial impact on the cardiology trainees' understanding of MV anatomy and pathology compared to 3DE images

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Automating dimensional tolerancing using Ripple Down Rules (RDR)

We propose to use a knowledge based approach to assist in mechanical design focusing on dimensional tolerancing. To illustrate our approach, we capture the knowledge which human designers utilize in order to specify dimensional tolerances on shafts and mating holes in order to meet desired classes of fit as set by relevant engineering standards. The software system we developed would help mechanical designers become more effective in the time-consuming dimensioning and tolerancing process of their designs in the future. In doing this, the paper makes a twofold contribution to the field of knowledge acquisition: firstly, interface was adjusted to receive mathematical functions with their specifications prior and during the KA process to propose an approach to exploit relationships among several classes with respect to certain numerical features of the cases in order to accelerate the convergence of the RDR knowledge acquisition process by generating artificial cases which are likely to trigger the addition of exception rules. Secondly, it introduces the above problem domain of determining suitable tolerances for mechanical parts in a design as a knowledge acquisition problem

Automatic Detection of Cortical Bones Haversian Osteonal Boundaries

This work aims to automatically detect cement lines in decalcified cortical bone sections stained with H&E. Employed is a methodology developed previously by the authors and proven to successfully count and disambiguate the micro-architectural features (namely Haversian canals, canaliculi, and osteocyte lacunae) present in the secondary osteons/Haversian system (osteon) of cortical bone. This methodology combines methods typically considered separately, namely pulse coupled neural networks (PCNN), particle swarm optimization (PSO), and adaptive threshold (AT). In lieu of human bone, slides (at 20× magnification) from bovid cortical bone are used in this study as proxy of human bone. Having been characterized, features with same orientation are used to detect the cement line viewed as the next coaxial layer adjacent to the outermost lamella of the osteon. Employed for this purpose are three attributes for each and every micro-sized feature identified in the osteon lamellar system: (1) orientation, (2) size (ellipse perimeter) and (3) Euler number (a topological measure). From a training image, automated parameters for the PCNN network are obtained by forming fitness functions extracted from these attributes. It is found that a 3-way combination of these features attributes yields good representations of the overall osteon boundary (cement line). Near-unity values of classical metrics of quality (precision, sensitivity, specificity, accuracy, and dice) suggest that the segments obtained automatically by the optimized artificial intelligent methodology are of high fidelity as compared with manual tracing. For bench marking, cement lines segmented by k-means did not fare as well. An analysis based on the modified Hausdorff distance (MHD) of the segmented cement lines also testified to the quality of the detected cement lines vis-a-vis the k-means method

Analyzing CAD competence with univariate and multivariate learning curve models

Understanding how learning occurs, and what improves or impedes the learning process is of importance to academicians and practitioners; however, empirical research on validating learning curves is sparse. This paper contributes to this line of research by collecting and analyzing CAD (computer-aided design) procedural and cognitive performance data for novice trainees during 16-weeks of training. The declarative performance is measured by time, and the procedural performance by the number of features used to construct a design part. These data were analyzed using declarative or procedural performance separately as predictors (univariate), or a combination of declarative or procedural predictors (multivariate). Furthermore, a method to separate the declarative and procedural components from learning curve data is suggested. (C) 2008 Elsevier Ltd. All rights reserved

Correlating trainee attributes to performance in 3D CAD training

Purpose - The purpose of this exploratory study is to identify trainee attributes relevant for development of skills in 3D computer-aided design (CAD). Design/methodology/approach - Participants were trained to perform cognitive tasks of comparable complexity over time. Performance data were collected on the time needed to construct test models, and the number of features used to construct them. A written questionnaire survey profiled the trainees' technical capabilities, motivation, and dedication. Findings - Correlation analysis between the trainees' attributes/capabilities and performance showed that a mix of the trainees' psychological and technical attributes contributed to CAD competence development. Prior technical knowledge influenced initial performance whereas dedication had a strong influence on the rate of improvement. Practical implications - The methodology serves as basis for developing specific guidelines for constructing questionnaires for trainee profiling and for customizing the training of mechanical 3D CAD. Originality/value - This original research proposes a framework for classifying CAD-training candidates based on their technical and personality profiles, which may lead to more effective training