Automatic segmentation of digitized data for reverse engineering applications

Reverse engineering is the process of developing a Computer Aided Design (CAD)model and a manufacturing database for an existing part. This process is used in CAD modeling of part prototypes, in designing molds, and in automated inspection of parts with complex surfaces. The work reported in this paper is on the automatic segmentation of 3-Dimensional (3-D) digitized data captured by a laser scanner or a Coordinate Measuring Machine (CMM) for reverse engineering applications. Automatic surface segmentation of digitized data is achieved using a combination of region and edge based approaches. It is assumed that the part surface contains planar as well as curved surfaces that are embedded in a base surface. The part surface should be visible to a single scanning probe (21/2D object). Neural network algorithms are developed for surface segmentation and edge detection. A back propagation network is used to segment part surfaces into surface primitives which are homogenous in their intrinsic differential geometric properties. The method is based on the computation of Gaussian and mean curvatures of the surface. They are obtained by locally approximating the object surface using quadratic polynomials. The Gaussian and mean curvatures are used as input to the neural network which outputs an initial region-based segmentation in the form of a curvature sign map. An edge based segmentation is also performed using the partial derivatives of depth values. Here, the output of the Laplacian operator and the unit surface normal are computed and used as input to a Self-Organized Mapping (SOM) network. This network is used to find the edge points on the digitized data. The combination of the region based and the edge based approaches, segment the data into primitive surface regions. The uniqueness of our approach is in automatic calculation of the threshold level for segmentation, and on the adaptability of the method to various noise levels in the digitized data. The developed algorithms and sample results are described in the paper     

Design and optimization of cantilever based piezoelectric micro power generator for cardiac pacemaker

Piezoelectric micro-power generator (PMPG) converts mechanical vibration energy into electric energy via piezoelectric effects. In cardiac pace makers, the use of PMPG eliminates the need for a traditional lithium iodide battery replacement. In this paper we design and optimize PMPG that is able to harvest the mechanical movement of the heart beat to be converted into usable electrical power in frequency range 1–1.7 Hz. Eight control parameters are selected: which are proof mass material, piezoelectric material, proof mass length, proof mass thickness, piezoelectric layer width, piezoelectric layer thickness, silicon nitride layer width, silicon nitride layer thickness. Orthogonal arrays of Taguchi method for these eight parameters mentioned with three levels and signal-to-noise (S/N) ratio, and ANOVA analysis is studied to determine the optimum design. COMSOL Multiphysics ver. 4.2 is used in 18 different simulations. The maximum output power and highest efficiency designed at 1.2 Hz is equivalent to 72 beat per min. Both Taguchi and ANOVA confirms the same results of determining the parameter of having the most influence on the generated output power at 1.2 Hz in descending order: which are piezoelectric material of PZT-5A, proof mass length of 5 mm, piezoelectric layer thickness of 30 µm, proof mass thickness of 4 mm, piezoelectric layer width of 0.12 mm, silicon nitride layer width of 0.16 mm, silicon nitride layer thickness of 30 µm, and proof mass material of aluminum. Eigen frequency analysis for the first six modes of operation for PMPG frequencies are: 1.2 HZ, 5.4 Hz, 6.9 Hz, 29,7 Hz, 694.8 Hz, 708.3 Hz. The first mode of operation is selected as operation mode and shows that 93 % of PMPG’s total displacement and output power was produced in the range of 1–1.4 Hz, therefore PMPG can work when the heart rate between 60 and 84 bpm. Transient analysis performed at 1.2 Hz reaches the steady state before the first 10 cycles with output power density of 23.13 µW/cm³, which is suitable for powering cardiac pace maker.     

Thermo-mechanical behaviors of the expanded graphite-phase change material matrix used for thermal management of Li-ion battery packs

In this paper, blocks for the thermal management of Li-ion battery are prepared. The blocks are made of paraffin wax, which is used as a phase change material (PCM), and graphite flakes. The process starts by compacting expanded graphite into the desired modular shapes and then impregnating it into molten paraffin wax. The modular pieces were assembled together, followed by finishing operations to achieve a desired packaging geometry.Thermo-mechanical properties of the produced phase change material–expanded graphite (PCM/EG) composites have been studied. The tests include thermal conductivity, tensile compression and bursting test. The results showed that as mass fraction of paraffin wax increases in the composite material, the thermal conductivity, tensile strength, compression strength, and burst strength were improved while tested at low operating temperatures. In contrast, the results showed reverse behaviors when tested at relatively high operating temperature.     

A re-organizing biosurveillance framework based on fog and mobile edge computing

Multimedia Tools and Applications - Biological threats are becoming a serious security issue for many countries across the world. Effective biosurveillance systems can primarily support appropriate...     

Wear behavior of Al–Cu and Al–Cu/SiC components produced by powder metallurgy

In the present study, the dry sliding wear behavior of some powder metallurgy (P/M) Al–Mg–Cu alloys with different weight percentage of Cu (0, 1, 2, 3, 4, and 5 wt%) and corresponding metal matrix composites reinforced with 5 or 10 vol% silicon carbide particles (SiC) have been carried using pin-on-disk apparatus. The tested specimens were tested against hardened steel disk as a counter face at room conditions (∼20 °C and ∼50% relative humidity). The normal load was 40 N and sliding velocity of counter face disk was 150 rpm (0.393 m/s) and total testing time of 60 min, which corresponds to a distance of 1414 m. Generally, both hardness and wear resistance were enhanced by the addition of Cu and/or SiC to the Al-4 wt% Mg alloy. The formations of mechanically mixed layer (MML) as a result of material transfer from counter face disk to the samples and vice versa were observed in all tested specimens.     

Prediction of density,porosity and hardness in aluminum–copper-based composite materials using artificial neural network

The potential of using feed forward backpropagation neural network in prediction of some physical properties and hardness of aluminium–copper/silicon carbide composites synthesized by compocasting method has been studied in the present work. Two input vectors were used in the construction of proposed network; namely weight percentage of the copper and volume fraction of the reinforced particles. Density, porosity and hardness were the three outputs developed from the proposed network. Effects of addition of copper as alloying element and silicon carbide as reinforcement particles to Al–4 wt.% Mg metal matrix have been investigated by using artificial neural networks. The maximum absolute relative error for predicted values does not exceed 5.99%. Therefore, by using ANN outputs, satisfactory results can be estimated rather than measured and hence reduce testing time and cost.     

Material Selection of an Elastomer Capable of Absorbing Vibrations Actuated by a 4D Movie Theater

The objective was to investigate possible vibration isolator solutions for a 4D entertainment theater. The paper focused on resolving a vibration leakage issue experienced by customers of Company A which manufactures tactile motion actuators for 4D theater entertainment purposes. The investigation started by utilizing Cambridge Engineering Selector software to determine the value of the mechanical loss factor for given materials. Elastomers had the best mechanical loss coefficient, specifically polyurethanes. While considering the specifications provided by Company A, certain parameters such as nominal load withstanding and prices were considered. After investigating the materials thoroughly, Sorbothane showed the best performance along with suitable prices. The vibrational system investigated resulted in a frequency ratio of 6, and a transmissibility of 2.86% at normal conditions, which indicates that the material selected was suitable. Sorbothane material at shore 00 and durometer 50 showed its capability to withstand maximum nominal loading at 635 kg (1400 lbs). This was 57% higher than the targeted loading. The mechanical loss factor was 0.52 at 50-Hz excitation frequency, which was high enough to dissipate excessive vibrations.     

Wear behavior of Al-Mg-Cu-based composites containing SiC particles

The friction and wear behavior of Al-Mg-Cu alloys and Al-Mg-Cu-based composites containing SiC particles were investigated at room conditions at a pressure of 3.18 MPa and a sliding speed of 0.393 m/s using a pin-on-disk wear testing machine. This study is an attempt to investigate the effects of adding copper as alloying element and silicon carbide as reinforcement particles to Al-4 wt% Mg metal matrix. The wear loss of the copper containing alloys was less than that for the copper free alloys. It was observed that the volume losses in wear test of Al-Mg-Cu alloy decrease continuously up to 5%. Also it was found that the silicon carbide particles play a significant role in improving wear resistance of the Al-Mg-Cu alloying system. The formation of mechanically mixed layer (MML) due to the transfer of Fe from counterface disk to the pin was observed in both Al-Mg-Cu alloys and Al-Mg-Cu/SiC composites.     

Power density optimization for MEMS piezoelectric micro power generator below 100 Hz applications

Microsystem Technologies - In piezoelectric based micro-power generator (PMPG), electrical energy is generated from mechanical vibration by gaining on the piezoelectric effects. This study...     

Multi-criteria end milling parameters optimization of AISI D2 steel using genetic algorithm

This paper focuses on using multi-criteria optimization approach in the end milling machining process of AISI D2 steel. It aims to minimize the cost caused by a poor surface roughness and the electrical energy consumption during machining. A multi-objective cost function was derived based on the energy consumption during machining, and the extra machining needed to improve the surface finish. Three machining parameters have been used to derive the cost function: feed, speed, and depth of cut. Regression analysis was used to model the surface roughness and energy consumption, and the cost function was optimized using a genetic algorithm. The optimal solutions for the feed and speed are found and presented in graphs as functions of extra machining and electrical energy cost. Machine operators can use these graphs to run the milling process under optimal conditions. It is found that the optimal values of the feed and speed decrease as the cost of extra machining increases and the optimal machining condition is achieved at a low value of depth of cut. The multi-criteria optimization approach can be applied to investigate the optimal machining parameters of conventional manufacturing processes such as turning, drilling, grinding, and advanced manufacturing processes such as electrical discharge machining.