Anthony Chukwujekwu Okafor

Derivation of machine tool error models and error compensation procedure for three axes vertical machining center using rigid body kinematics

Abstract Volumetric positional accuracy constitutes a large portion of the total machine tool error during machining. In order to improve machine tool accuracy cost-effectively, machine tool geometric errors as well as thermally induced errors have to be characterized and predicted for error compensation. This paper presents the development of kinematic error models accounting for geometric and thermal errors in the Vertical Machining Center (VMC). The machine tool investigated is a Cincinnati Milacron Sabre 750 3 axes CNC Vertical Machining Center with open architecture controller. Using Rigid Body Kinematics and small angle approximation of the errors, each slide of the three axes vertical machining center is modeled using homogeneous coordinate transformation. By synthesizing the machines parametric errors such as linear positioning errors, roll, pitch and yaw etc., an expression for the volumetric errors in the multi-axis machine tool is developed. The developed mathematical model is used to calculate and predict the resultant error vector at the tool–workpiece interface for error compensation.

Vertical machining center accuracy characterization using laser interferometer: Part 1. Linear positional errors

Abstract This paper presents the results of accuracy characterization of a Vertical Machining Center (VMC) in the form of linear errors and temperature variation using a low powered He–Ne laser (Renishaw ® ) calibration system along with environmental controller unit. The machine investigated is Cincinnati Milacron Sabre 750 three axes CNC VMC with Acramatic 2100 CNC open architecture controller. Temperature distribution of the machine was measured using three temperature sensors strategically attached to predetermined locations on each axis guides. The accuracy of the VMC is characterized in the form of geometric and thermal errors as a function of machine tool nominal axis position, temperature distribution and environmental effect (air temperature, air pressure and relative humidity). Results show that axis drive motors are the major heat sources. Linear positional accuracy is best when the machine is in cold condition and deteriorates with increasing machine operation time for all three axes. X -axis had worst linear displacement accuracy and maximum reversal errors among the three axes being tested.

Detection and characterization of high-velocity impact damage in advanced composite plates using multi-sensing techniques

Abstract Advanced composites are increasingly used in aerospace, naval, and automotive vehicles due to their high specific strength and stiffness. However, the mechanical properties of composite materials may degrade severely in the presence of damage. Damage due to impact in composite plates is often difficult to detect using any single technique. In this paper, the use of multiple sensing techniques to characterize high-velocity impact damage in advanced composites is reported. Broadband wave-based acoustic emission (AE) sensors are used to capture wave signals due to impact while shearography and ultrasonic (UT) immersion techniques are used to assess location and extent of damage after the impact. Five 48-ply [0/+45/90/−45]6s laminated AS4/PEEK composite plates were used as test specimens. Shearography images of all five test specimens were taken before impact testing to detect any pre-existing internal damage from fabrication. Three broadband AE sensors were mounted on the surface of the composite plates to capture the AE signals due to impact. A 3/8-inch diameter stainless steel ball fired from a gas gun facility was used as a projectile to inflict damage to the composite plates. The AE signals were instantaneously acquired during the impact tests and stored in a computer. The AE signals show existence of both the extensional and flexural modes, with extensional modes typically showing first. AE energy also increases to a threshold as the kinetic energy of impact increases. Shearography fringe patterns show existence of damage and this is confirmed and quantified with C-scan images from the UT immersion test. There is good correlation between AE parameters such as AE energy, AE amplitude, and AE count with impact energy and with damage on the composite plates. Due to the low contrast of the shearograms, UT C-scans are used to show extent of damage. This research demonstrates how multiple sensing techniques can be used to characterize high-velocity impact damage in advanced composites.

Optimal ultrasonic pulse repetition rate for damage detection in plates using neural networks

An experimental study for the determination of the optimal pulse repetition rate frequency (PRF) for damage detection in aluminum and composites is presented in this paper. A method for predicting the damage size and depth from C-Scan results using neural networks is also presented. Two graphite fiber IM7/F5250-4 (Bismaleimid) composite plates and four aluminum plates were used for the study. Damage was fabricated by drilling holes of varying depth and diameter on the test specimens. Ultrasonic transmission tests were carried out on a DIGITALWAVE immersion type C-Scan system. PRF values from 100 to 5000 Hz were investigated for the scan. The defect locations were clearly observed as peaks in the C-Scan mesh. The equivalent hole diameter, depth and the location of the holes with respect to a predetermined edge were calculated from the C-Scan plots and correlated with the actual values to determine the optimal PRF values. A close correlation was found between the calculated diameter obtained from the C-Scan results and the actual hole diameter. Low PRF values (100 Hz) were found best for scanning of aluminum and intermediate values (500 Hz) were best for scanning of composites. Prediction of the actual damage size from the C-Scan calculated damage size was successfully accomplished with radial basis function neural network.

Damage detection in composite laminates with built-in piezoelectric devices using modal analysis and neural network

The effect of prescribed delamination on natural frequencies of laminated composite beam specimens is examined both experimentally and theoretically. Delamination in composite laminate is of particular interest because they can cause catastrophic failure of the composite structure. One consequence of delamination in a composite structure is a change in its stiffness. This change in stiffness will degrade the modal frequencies of the composite structure. Modal testing of perfect beam and beams with different size of delamination is conducted using PVDF sensors and piezoceramic patch with sine sweep actuation. Model testing of beams is also conducted using PVDF sensors and instrumented hammer excitation. The results of instrumented hammer excitation and piezoceramic patch excitation are discussed. The experimental modal frequencies are compared with the results obtained using a simplified beam theory. Also, backpropagation neural network models are developed using the results from the simplified beam theory and used to predict delamination size. The effect of learning rate and momentum rate on neural network performance are discussed. Modal frequencies can be easily and accurately obtained with piezoceramic patch excitation and PVDF sensing. There is good agreement between modal frequencies from modal testing and those from the simplified beam theory. The developed neural network models successfully predict delamination size. Prediction errors varied from 0.25% to 19%.

Damage assessment of smart composite plates using piezoceramic and acoustic emission sensors

An experimental study is conducted to develop a technique for detecting and assessing damage in laminated composite plates using piezoceramic (PZT) and acoustic emission (AE) sensors. Test specimens of glass/epoxy and graphite/epoxy composite plates are fabricated using a hot press cure technique. PZT patches and AE sensors are surface mounted on the composite plates to serve as sensors. Low velocity impact tests of plates with all four edges clamped are conducted using a drop weight testing frame with the composites in an undamaged state and again after the composites are damaged. Two test methods are used to assess the damage of the composite plates. The piezoceramic sensor output and the acoustic emission sensor signals are measured during the impact tests. Modal testing is performed to determine the frequencies and dampings of the structures from the frequency response function. The plate is then damaged by a high velocity impact and the results are correlated with the undamaged plate data. It is found that the piezoceramic sensor output is very sensitive to the composite damage and change in modal frequencies and dampings is observed.

Estimation of contact force on composite plates using impact-induced strain and neural network

A method of determining the contact force on laminated composite plates subjected to low velocity impact is developed using the finite element method and a neural network. The back propagation neural network is used to estimate the contact force on the composite plates using the strain signals. The neural network is trained using the contact force and strain histories obtained from finite element simulation results. The finite element model is based on a higher order shear deformation theory and accounts for von-Karman nonlinear strain-displacement relations. The nonlinear time dependent equations are solved using a direct iteration scheme in conjunction with the Newmark time integration scheme. The training process consists of training the network with strain signals at three different locations. The effectiveness of different neural network configurations for estimating contact force is investigated. The neural network approach to the estimation of contact force proved to be a promising alternative to more traditional techniques, particularly for an on-line health monitoring system.

Detection and characterization of damage in composite plates using shearography and wave-based acoustic emission techniques

This paper investigates the use of shearography and waveform- based acoustic emission (AE) techniques to detect and assess damage in composite plates due to high velocity impact. Five 48-ply [0/+45/90/-45]6s laminated AS4/PEEK composite plates donated by Boeing Company in St. Louis were used as test specimens. Shearography images of all five test specimens were taken before impact testing to detect any pre- existing internal damage from fabrication. Three broadband AE sensors were mounted on the surface of the composite plates to measure AE signals due to impact. High velocity impact tests of plates with all four edges clamped were conducted using a gas gun facility. The AE sensor signals were instantaneously acquired during the impact tests and stored in a Pentium computer. The digitized AE signals were processed in time and frequency domains. The raw AE signals were preprocessed to remove reflections from the plate boundaries that distort the wave form and cause errors. The resulting damage due to impact was evaluated using shearography fringe patterns and AE sensor signal features. The results show a correlation of AE parameters such as AE energy, AE amplitude, AE count, and shearography fringe patterns with impact energy and impact damage of the composite plates. The AE signals show the presence of both extensional and flexural wave modes with flexural wave the dominant mode. There is quite a distinctive difference between shearography fringe patterns of undamaged and damaged composite plates.

Monitoring drilling of advanced composites, drill wear, and exit hole delamination using wave-based acoustic emission

An experimental investigation was performed to study drill wear and its effect on acoustic emission and drilled hole exit delamination for both carbide and PCD drills when drilling in advanced composites (AS4/PEEK). Drilling conditions were selected to minimize exit hole delamination for the carbide drill and a series on 80 holes were drilled with both carbide and PCD drills. Acoustic emission signals were acquired and flank wear width were measured after every fifth hole. Carbide drill material was found to wear more than the PCD drill. Exit hole delamination increases with tool wear for both carbide and PCD drills as shown from measurements and SEM micrograph images of drilled holes. The average acoustic emission (AE) energy per event as well as total AE energy were found to decrease with tool wear. The results show that acoustic emission could be used successfully for real-time monitoring and prediction of drill wear and exit hole delamination.

Effects of drilling conditions, drill material, and point angle on acoustic emission and exit hole delamination in drilling advanced fiber-reinforced composites

A 1/3 fractional factorial design of experiment with four factors at three levels was used to investigate drilling of multi-directional AS4/PEEK composites. The design of experiment was conducted to determine the main effects and two factor interactions of drill point angle, cutting speed, feed rate, and drill material on acoustic emission generated in drilling and drilled hole exit delamination. Drill material, feedrate, cutting speed, and drill point angle were found to have statistically significant effects on total and average acoustic emission energy generated which also depends on the location of the acoustic emission spectra (on the workpiece or on the drilling fixture). Drill material was found to have the most significant effect on exit hold delamination, and accounted for over 50% of the variation in the measured exit hole delamination. Carbide drills were found to produce the least amount of exit hold delamination throughout the range of speeds investigated followed by Polycrystalline Diamond drills, while High Speed Steel Cobalt drills were found to produce holes with the greatest amount of exit hold delamination.