Controlling drilling temperature and thrust forces play a significant role in reducing tool wear and improving machining efficiency. In this work, drilling experiments are set up to measure flank surface temperature via thermocouple sensor wires passed through the coolant holes of 10mm twist drill and brazed to the drill flank surface. The testing setup is an inverted drilling jig where the workpiece (Aluminum 6061-T6 rod) is chucked into the spindle of a vertical machining center. Thrust forces are co-measured using Kistler type 3-component plate dynamometer attached to the table. A design of experiment (DOE) using JMP-SAS/STAT ® was adopted for selecting combinations of cutting speed and feed values that cover a wide range. Drilling temperature rise and thrust forces are found to correlate with cutting conditions of feed (f), maximum cutting speed (V), and drilling depth (Dp). Nonlinear regression analysis produced correlating equations of flank temperature rise and thrust forces to conditions and follow a mechanistic power law of the form a 1 f a 2 V a 3 Dp a 4 where a 1 , a 2 , a 3 and a 4 are identified via regression fitting.

Composites are the revolutionary materials that were developed for the ease of the technology. Similar to all families of materials, composites are being extensively studied nowadays. One of the composites ‘main studies is the homogenization study to determine composites modulus of elasticity function of multiple variables based on differentiating several inclusions’ geometries, or quantities or orientations. However, homogenization studies require extensive numerical and analytical work. This work uses a statistical optimized tool TSREG (tabu search combined with statistical regression proven to achieve models with the highest R-squared and lowest p-values for each variable in addition to the lowest MAPE (mean absolute percentage error)) to predict a model relating composite modulus of elasticity to inclusions shape (as aspect ratio), volume fraction, and orientation (0-30-60-90°). Experimental data of modulus of elasticity compressed 3D printed ABS plastics cubes of 16 × 16 × 16 mm3 size having one inclusion (as empty spheres or ellipsoids with zero Young’s modulus) were utilized. For the voids, their geometries were varied to cover spherical and elliptical shapes with several aspect ratios (0.2-0.4-0.65-0.75-0.9-1), volume fractions (0-0.1-0.2-0.3-0.35-0.4-0.5-1), and orientations (0-30-60-90°). This model helps researchers to determine the composite modulus of elasticity using one significant and accurate expression without using numerical analysis.

It is widely recognized that bone mineral content is a main contributor to cortical bone stiffness. Previous works by the authors revealed that stiffness of mid-diaphysis cortical bone increases with increasing radial position from interior to exterior regions. In this work, we correlate this radial cortical stiffness to the chemical composition of several bone rings cut from 2-year old bovine cow femur (collected fresh from butcher). This mineralization is quantified using energy-dispersive X-ray (EDX) spectroscopy. On each bone ring, five regions are assigned along a 4 mm radial line covering the entire cortical wall thickness. Locations along the radial distance are assigned to acquire the chemical analysis spectrum. Calcium (Ca) and Phosphorus (P) elements chemical elements are traced/detected. Measured mineralization results are expressed as per weight percent concentration (wt %). These elemental results for Calcium (Ca) and Phosphorus (P) are correlated to radial position and stiffness values using statistical analysis (SPSS ® ). Calcium (Ca) and Phosphorus (P) elements were positively correlated with stiffness values and radius whilst Ca/P ratio was almost constant with the radius. Findings suggest that with increasing radius, Ca (wt%) and P (wt %) showed a fairly increasing trend that correlates to increasing stiffness values proving that increased bone mineralization would contribute to cortical bone stiffness.

A non-linear regression model using SAS/STAT (JMP ® software; Proc regression module) is developed for estimating the elastic stiffness of finite composite domains considering the combined effects of volume fractions, shapes, orientations, inclusion locations, and number of multiple inclusions. These estimates are compared to numerical solutions that utilized another developed homogenization methodology by the authors (dubbed the generalized stiffness formulation, GSF) to numerically determine the elastic stiffness tensor of a composite domain having multiple inclusions with various combinations of geometric attributes. For each inclusion, these considered variables represent the inclusions’ combined attributes of volume fraction, aspect ratio, orientation, number of inclusions, and their locations. The GSF methodology’s solutions were compared against literature-reported solutions of simple cases according to such well-known techniques as Mori-Tanaka and generalized self-consistent type methods. In these test cases, the effect of only one variable was considered at a time: volume fraction, aspect ratio, or orientation (omitting the number and locations of inclusions). For experimental corroboration of the numerical solutions, testing (uniaxial compression) was performed on test cases of 3D printed test cubes. The regression equation returns estimates of the composite’s ratio of normalized longitudinal modulus (E11) to that of the matrix modulus (Em) or E11/Em when considering any combination of all of the aforementioned inclusions’ variables. All parameters were statistically analyzed with the parameters retained are only those deemed statistically significant (p-values less than 0.05). Values returned by the regression stiffness formulation solutions were compared against values returned by the GSF formulation numerical and against the experimentally found stiffness values. Results show good agreement between the regression model estimates as compared with both numerical and experimental results.

Cutting pressure models have long been utilized for calculating machining force components both tangential and normal. This work lists literature-reported closed form analytical models for torque and thrust of material cutting pressures. Typically, these models are equations formulated in terms of process parameters including mainly cutting speed (V, m/min), feed (f, mm/rev), and tool rake angle (α, degrees). This work aims to benefit from the variation in rake angle and cutting speeds encountered along the lip cutting edge of a standards twist drill to generate normal and tangential cutting pressure empirical models for aluminum 6061-T6 material based on a limited number of drilling feed rate and spindle speed combinations. To isolate the 10mm twist drill bit cutting edge forces, drilling experiments were conducted utilizing 2.5mm pre-cored workpieces. Cutting lip edge toque and thrust data were collected from conducted series of drilling experiments with total number of 10 varying combinations of feed rate and spindle speeds. Values of cutting pressure in terms of feed, speed, and rake angle parameters were identified using a custom-developed MATLAB ® code. Minimized is the difference between the cutting pressure-based closed form torque and thrust models versus the experimentally collected cutting lip torque and thrust measurements. Based on this MATLAB ® code, cutting feed, speed and rake angle parameters found around −0.2, −0.1 and 1.5 respectively based on a convergence criteria of 10 −10 and maximum iteration number of 1000. These optimized cutting pressure parameters were found to predict well the collected experimental forces and were found to be in line with parameter values as reported in the literature.

As alternative to complex modeling, simple empirical equations that provide estimates of the chisel edge’s torque and thrust cutting force components are a useful tool for estimating chisel cutting forces. This work aim to develop a model capable of estimating torque and thrust forces generated by only the drill’s chisel edge as function of machining feed (f), cutting velocity (V), and chisel edge’s width (w). The chisel edge’s generated machining forces are quantified and statistically verified utilizing JMP-SAS/STAT ® design of experiment (DOE) software. An experimental setup involving 50 drilling tests was conducted with drilling feed, spindle speed and chisel web dimensions varied to enhance the statistical significance of the collected data. Experimental torque and thrust data were collected using a 4-channel rotary dynamometer (Kistler model 9123) at a 200 Hz sampling rate. Torque and thrust values encountered at the chisel edge were isolated from those of the full drill. Due to its wide industrial use, the workpiece material investigated is Aluminum 6061-T6. In-line with literature, reported values of experimentally collected data showed that chisel edge’s forces increased with drilling feed and drill bit web dimension while decreased with increasing speed (perhaps due to thermal softening). JMP-SAS/STAT ® module, nonlinear fitting with convergence criteria of 10 −15 and 70 iterations was utilized The developed model equations allow the estimation of the forces encountered at the chisel edge of a classical twist drill while cutting aluminum alloy 6061-T6 material.

This work reports on an empirical study to account for the effects of the drilling parameters of cutting speed: V, m/s (max at the outer periphery), tool feed (f, mm/rev)), and drill diameter (D, mm) on the generated thrust (T, N) and torque (M, N.m). A total of 50 drilling tests were conducted on a vertical machining center based on design of experiment (DOE) test matrix using JMP-SAS/STAT ® software. The test matrix employed 2 different aluminum alloys: one wrought (6061-T6) and another cast (A356-T6) with both tempered to the T6 condition. Two classical chisel drills of different diameters (10mm and 12.7mm) were used. Drilling parameter variables employed varied from very mild to very aggressive combinations including 5 values of spindle speeds (796, 1592, 3183, 6366, and 9868 rpm) and 5 values of drilling feeds (0.04, 0.08, 0.16, 0.32, 0.64 mm/rev). Drilling forces and torques were recorded using a 4-component dynamometer (Kistler model 9123) at sampling rate of 200 Hz. At full drill engagement, cutting torque and thrust status were identified for each drilling test case. By fitting to the measured drilling forces, power law equations with parametric variables D, f, and V are developed. Initial values of the model’s power coefficients were initially identified using Matlab ® using a least square optimization function with target to minimize the difference between the predicting model and the experimental data. These values were fed as initially-guessed values to the nonlinear modeling Gauss method in JMP-SAS/STAT ® with 50 observations for each set of experiments. For both torque and thrust and for both aluminum alloys employed, the model’s coefficient values were identified setting the convergence criteria to 10 −15 with total of 60 iterations. For both thrust and torque, it was found that the power coefficients for feed and cutting speed are statistically significant (better than p-values < 0.05) with values of about 0.7 and −0.1, respectively.

The aim of this study is to quantitatively discern the compressive stiffness along the thickness of bovine cortical bone from the inner surface toward the bone’s periosteum (outer) surface. Ring-shaped bone slices from randomly selected 3 cow bovine bones were cut from mid diaphysis femur. Micro hardness measurements (ASTM E384-11e1 Standard- test method for Vickers hardness of materials with load of 500gf and dwelling time of 10s) were conducted along radial lines emanating from the geometric center of the bone transverse cross section. Values of compressive stiffness (longitudinal young’s modulus, E 11 ) using the slope of the unloading curve of micro hardness tests at maximum load were extracted per the method advanced by Oliver and Pharr. Fitting these data points in a linear fit resulted in statistically significant and simple linear equations for compressive modulus as function of radial distance. For the anatomical anterior and posterior regions, compressive modulus values were found to range from 18 to 23.4 GPa (average of 21.2 GPa) and 23 to 26.0 GPa (average of 23.8 GPa), respectively. This is about 12% increase in average stiffness values of outer lamella near the periosteum as compared with inner regions.

Pores (namely lacunae, clusters of canaliculi, Haversian canals, and resorption cavities) are present throughout cortical bone. This paper characterizes the area fraction (AF, %)) of each type of these pores as function of distance from the bone’s geometric center while noting the region in which such pores are located: midcortical or periosteal. Optical slides (at 20X) are taken from 2 cortical bone biopsies named bone 1 and bone 2 and cut at mid-diaphysis femur from 2 different (about 2 year-old) bovine cows. The slides are collected from posterior (pericortical) and anterior (intracortical) locations. The area of each of these biopsies is about 2.5mm × 3mm located near the outer cortex of the bone. In polar coordinates from the bone’s center, the areas cover radial distance of about 3.3 mm (of radius, R) and encompass an arc of 10°. Automated segmentation is used to locate and identify all pores in the optical slides the shapes of which are best fitted into ellipses. Values of area fraction, AF (%) of said fitted ellipses are then automatically calculated in secondary osteons for both regions. Variations in values of area fraction AF (%) are related to actual areas of pores (based on their defining equations). Observations suggest that area fractions (%) of all pores (but to lesser degree for Haversian canals), to significantly decrease linearly and in a steep fashion with R (statistically significant, p < 0.01) in the anterior region where osteonal growth is expected to have continued to develop. However, in the posterior region where osteonal growth appears to have matured, area fraction (%) values seem to have reached a steady state resulting in fairly flat behavior versus R. All observations are equally applicable for biopsies collected from bone 1 and bone 2.

The lamellar or Haversian system is comprised mainly of fundamental units “osteons”. Haversian canals run through the center of the osteons where one or more blood vessels are located. The bone matrix is comprised of concentric lamellae surrounding Haversian canals. Those lamellae are punctuated by holes called lacunae, which are connected to each other through the canaliculi supplying nutrients. Haversian canals, lacunae and canaliculi of the Haversian system constitute the main porosities in cortical bone, thus it is advantageous to segregate those systems in segmented images that will help medical image analysis in accounting for porosities. To the authors’ best knowledge, no work has been published on segregating Haversian systems with its 3 predominant components (Haversian canals, lacunae, and canaliculi) via automated image segmentation of optical microscope images. This paper aims to detect individual osteonal Haversian system via optical microscope image segmentation. Automation is assured via artificial intelligence; specifically neural networks are used to procure an automated image segmentation methodology. Biopsies are taken from cortical bone cut at mid-diaphysis femur from bovine cows (which age is about 2 year-old). Specimens followed a pathological procedure (fixation, decalcification, and staining using H&E staining treatment) in order to get slides ready for optical imaging. Optical images at 20X magnification are captured using SC30 digital microscope camera of BX-41M LED optical Olympus microscope. In order to get the targeted segmented images, utilized was an image segmentation methodology developed previously by the authors. This methodology named “PCNN-PSO-AT” combines pulse coupled neural networks to particle swarm optimization and adaptive thresholding, yielding segmented images quality. Segmentation is occurred based on a geometrical attribute namely orientation used as the fitness function for the PSO. The fitness function is built in such way to maximize the identified number of features (which are the 3 components of the osteonal system) having same orientation. The segmentation methodology is applied on several test images. Results were compared to manually segmented images using suitable quality metrics widely used for image segmentation evaluation namely precision rate, sensitivity, specificity, accuracy and dice. The main goal of segmentation algorithms is to capture as accurate as possible structures of interest, herein Haversian (osteonal) system. High quality segmented images obtained as well as high values of quality metrics (approaching unity) prove the robustness of the segmentation methodology in reaching high fidelity segments of the Haversian system.

Homogenization theory is utilized to study the effect on the axial stiffness of secondary osteons in cortical bone due to the presence of micro porous features (e.g., lacunae, canaliculi clusters, and Haversian canals). Specifically, 2 geometric characteristics were used to describe these features within the secondary osteons: volume fraction (% porosity) and shape (circular- or elliptical-shaped). Such information was determined for each individual porous feature from an image segmentation methodology developed earlier by Hage and Hamade. For each feature, aspect ratio vectors (or arrays of ratios for each individual porous feature) were used to classify each pore inhomogeneity as cylindrical, elliptical or irregular shape. Two prominent homogenization theories were used: the Mori-Tanaka (MT) and the generalized self-consistent method (GSCM). Using the results of image segmentation, it was possible to calculate the respective Eshelby tensors of each porous feature. To calculate the isotropic stiffness tensors for matrix (C m ) and pores (C p ) the Young’s modulus and Poisson’s ratio for the matrix (E m , ν m ) were assigned as obtained from literature and as those of blood (E p =10MPa, ν p = 0.3), respectively. The effective elastic stiffness tensors (C*) for the secondary osteons were obtained from which axial Young’s modulus was obtained as function of volume fraction (% porosity) of each pore type and their individual shapes. The normalized axial Young’s modulus was found to 1) significantly decrease with increasing volume fraction (%) of porosity and 2) for the same % porosity, to slightly decrease (increase) with increasing ratio of circular-shaped to elliptical-shaped (elliptical-shaped to circular-shaped) porous features. These findings were validated using experimental micro-indentation study performed on secondary osteons.

In this work, we apply an image segmentation technique that uses pulse coupled neural networks to automatically discern the micro-features of cortical bone histology. In order to properly identify them, we exploit the geometric attributes of these micro features namely shape (i.e., circular or elliptical). These micro-constituent attributes are used as targets for the fitness function of the optimization method (particle swarm optimization, PSO) that was combined with PCNN along with an adaptive threshold, (T) that finds the best value for T between two segmented regions. The result is an optimal set of PCNN parameters that was found in this work to yield good-quality segmented pulses of the various micro-features of 2 different cortical bone images.

The finite element method at the micro scale (mFEM) has been gaining in popularity to simulate biomechanical effects. In this paper, a 3D mFEM model is developed to simulate sawing of cortical bone under 2D orthogonal cutting conditions. The aim of the research was to develop a predictive model of the sawing forces and to report them as a function of depth of cut. To obtain the micro geometric input, a heterogeneous anisotropic model was created from several images taken via an optical microscope of the cortex of adult mid-diaphysal bovine femur. In order to identify the various regions representing the micro-architecture of cortical bone, such as osteons, Haversian canals, lamellae and lacunae, MATLAB was utilized for intelligent image processing based on pulsed coupled neural networks. After each micro-phase in the image was assigned the proper mechanical properties, these material-tagged micro-features were imported into the finite element method (FEM) solver. Results from the simulation were correlated to cutting force data that was determined experimentally. Experiments were conducted with individual stainless steel saw blade teeth that were removed from a typical surgical saw blade. The teeth were 0.64 mm thick, with a rake and clearance angle of −10 and 60 degrees, respectively. Representative of clinical conditions for power bone sawing, depths of cut per tooth between 2.5 micrometer and 10 micrometer were investigated. The simulated cutting forces from the mFEM model compared favorably to the experimental data.

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.