Ramsey F. Hamade

Learning Theory as Applied to Mechanical CAD Training of Novices

Learning associated with mechanical computer-aided design (CAD) poses challenges for both trainers and trainees. This article presents findings that relate to the acquisition of skills in utilizing a modern mechanical CAD tool, Pro/ENGINEER version 2000i2, to design models of low complexity. Empirical learning curves were generated and broken into declarative and procedural components, which were analyzed in an attempt to measure how and how fast trainees developed cognitive and motor skills. Results showed that there is an inverse relationship between the amount of improvement in performance time and the number of build features used in building a solid model. If production time is an overriding criterion, then efforts should be focused on teaching CAD trainees how to build mechanical solid models using small number of complex, more time-efficient features.

Cathodic weakening of elastomer-to-metal adhesive bonds: accelerated testing and modeling

The deleterious effect of galvanic activity on the durability of rubber-to-metal adhesive bonds is investigated. The generation of hydroxyl ions at the elastomer/metal bondline due to an imposed voltage (current) is shown to be detrimental for cathodically exposed adhesively bonded structures. Bond weakening was found to be caused by alkali attack on the primer-metal oxide interface and was found to be governed by a diffusion-controlled degradation process. The independent accelerating parameters contributing to weakening were identified as temperature and imposed current density. An Arrhenius relationship was shown to model the effect of temperature quite well. For a given electrolyte, the effect of voltage is accounted for through an exponential relationship that relates the voltages to the corresponding current densities. A model that takes advantage of these relationships was developed and was utilized to fit the experimentally collected weakening data. The model accounts for the delay times as well as weakening rates. This model may be used as a first-order durability predictor for similar adhesively bonded systems upon exposure to cathodic environments.

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.

Segmentation of histology slides of cortical bone using pulse coupled neural networks optimized by particle-swarm optimization

The aim of this study is to automatically discern the micro-features in histology slides of cortical bone using pulse coupled neural networks (PCNN). To the best knowledge of the authors, utilizing PCNN in such an application has not been reported in the literature and, as such, constitutes a novel application. The network parameters are optimized using particle swarm optimization (PSO) where the PSO fitness function was introduced as the entropy and energy of the bone micro-constituents extracted from a training image. Another novel contribution is combining the above with the method of adaptive threshold (T) where the PCNN algorithm is repeated until the best threshold T is found corresponding to the maximum variance between two segmented regions. To illustrate the quality of resulting segmentation according to this methodology, a comparison of the entropy/energy obtained of each pulse is reported. Suitable quality metrics (precision rate, sensitivity, specificity, accuracy, and dice) were used to benchmark the resulting segments against those found by a more traditional method namely K-means. The quality of the segments revealed by this methodology was found to be of much superior quality. Another testament to the quality of this methodology was that the images resulting from testing pulses were found to be of similarly good quality to those of the training images.

A study of the influence of learning style of beginner computer-aided design users on their performance

In this work, we go about answering the question: does the learning style of beginner computer-aided design (CAD) users have any influence on their CAD competence development? We empirically examine a group of 44 novel trainees as they undergo a semester-long formal training program on mechanical 3D software (Pro/Engineer Wildfire). All the while we continuously monitor the trainees performance progress in learning CAD skills. Furthermore, the trainees are classified according to two popular methods: the learning style questionnaire [Honey, P. and Mumford, A., 1992. The manual of learning styles. 3rd ed. Maidenhead: Peter Honey] and the index of learning styles [Felder, R.M. and Soloman, B.A., 2004. Index of Learning Styles (ILS) [online]. Available from: http://www.ncsu.edu/felder-public/ILSpage.html (Accessed 15 February 2006)]. Finally, we report on the statistical correlation between the trainees learning styles and their CAD performance throughout the training and we comment on the various learning styles in relation to their CAD competence building capabilities.

Structural Micro Processing of Haversian Systems of a Cortical Bovine Femur Using Optical Stereomicroscope and MATLAB

Processing of optical images of bone has been a topic of considerable interest in the past and continues to be so. Image processing can be used in medicine in order to improve the image visualization to detect diseases, and to compute properties such as area for abnormal cells. Several studies of bone images have been conducted using several methods including segmentation and image enhancement. The aim of this paper is to generate a standalone automated code for segmenting colored optical microscope images in order to show the microstructure of a cortical bone as a multi-phase (here 4 phases) composite: Lamella (matrix), Haversian canals, osteoblast lamella boundaries (freshly generated lamella lining), and lacunae (containing living cells).For this purpose, we investigate the use of MATLAB, which contains image-processing toolboxes with many analytical capabilities that have been advertised to be useful for many applications including biological systems. In this work, such capabilities are utilized in image processing of the microstructure of bovine cortical bone, which is generally accepted as proxy for human bone. Two specimens of the cortical regions of a bovine femur bones were imaged using Olympus optical microscope. One of the specimens was treated with the Masson’s trichrome staining treatment and the other with the Hematoxylin and Eosin (H&E) treatment. The images from the microscope were captured using a DP12 camera.Furthermore, MATLAB results are contrasted against Stream®, a commercially available software package procured along with the Olympus optical microscope. Via color-coding to facilitate the bone microstructure identification, the image analysis results were compared after computing the areas of each of the 4 constituent microstructural phases. Areas of each phase were calculated and comparisons made between the results obtained from the Stream® software and those obtained from MATLAB. The relative error was found to be quite small (<1%), which proves that MATLAB may be an effective software for medical image processing and may be the tool of choice for standalone applications.Copyright

Geometric-attributes-based segmentation of cortical bone slides using optimized neural networks.

In cortical bone, solid (lamellar and interstitial) matrix occupies space left over by porous microfeatures such as Haversian canals, lacunae, and canaliculi-containing clusters. In this work, pulse-coupled neural networks (PCNN) were used to automatically distinguish the microfeatures present in histology slides of cortical bone. The networks’ parameters were optimized using particle swarm optimization (PSO). When forming the fitness functions for the PSO, we considered the microfeatures’ geometric attributes—namely, their size (based on measures of elliptical perimeter or area), shape (based on measures of compactness or the ratio of minor axis length to major axis length), and a two-way combination of these two geometric attributes. This hybrid PCNN–PSO method was further enhanced for pulse evaluation by combination with yet another method, adaptive threshold (AT), where the PCNN algorithm is repeated until the best threshold is found corresponding to the maximum variance between two segmented regions. Together, this framework of using PCNN–PSO–AT constitutes, we believe, a novel framework in biomedical imaging. Using this framework and extracting microfeatures from only one training image, we successfully extracted microfeatures from other test images. The high fidelity of all resultant segments was established using quantitative metrics such as precision, specificity, and Dice indices.

FEM optimization of process parameters and in-process cooling in the friction stir processing of magnesium alloy AZ31B

Controlling the temperature in friction stir processing (FSP) of Magnesium alloy AZ31b is crucial given its low melting point and surface deformability. A numerical FEM study is presented in this paper where a thermo-mechanical-based model is used for optimizing the process parameters, including active in-process cooling, in FSP. This model is simulated using a solid mechanics FEM solver capable of analyzing the three dimensional flow and of estimating the state variables associated with materials processing. Such processing (input) parameters of the FSP as spindle rotational speed, travel speed, and cooling rate are optimized to minimize the heat affected zone, while maintaining reasonable travel speeds and producing uniformity of the desired grain size distribution of the microstructure in the stirred zone. The simulation results predict that such optimized parameters will result in submicron grain sized structure in the stirred zone and at the corresponding stirred surface. These simulation predictions were verified using published experimental data.Copyright

Developing a Carbon Footprint Calculator for Construction Buildings

Carbon footprint is commonly defined as the total amount of greenhouse gases produced directly or indirectly as a result of an activity. The term carbon footprint has become the standard for measuring the environmental impact of activities in several sectors (e.g., transportation, energy, construction). While there have been several studies documenting calculators that estimate the carbon footprint of individual activities (e.g., driving a car, riding an airplane), the literature describing the process of carbon footprint calculations for construction activities remains limited. The few existing tools that calculate the carbon footprint of construction buildings do not take into account some of the major variables in the design and construction process (e.g., properties of selected materials, location of suppliers). In an effort to improve the accuracy of carbon footprint calculations, this paper presents a tool that estimates the total carbon footprint of construction buildings while taking into consideration project characteristics (e.g., size, location, material choices). The calculator relies on data collected from construction material suppliers and covers the various phases of a construction project. Through a case study, the research team illustrates the use of the tool to identify the activities with high carbon emissions.

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 their designs in the future.