A. Chukwujekwu Okafor

An approach for development of damage-free drilling of carbon fiber reinforced thermosets

A new comprehensive approach to select cutting parameters for damage-free drilling in carbon fiber reinforced epoxy composite material is presented. The approach is based on a combination of Taguchis experimental analysis technique and a multi-objective optimization criterion. The optimization objective includes the contributing effects of the drilling performance measures: delamination, damage width, surface roughness, and drilling thrust force. A hybrid process model based on a database of experimental results together with numerical methods for data interpolation are used to relate drilling parameters to the drilling performance measures. Case studies are presented to demonstrate the application of this method in the determination of optimum drilling conditions for damage-free drilling in BMS 8-256 composite laminate. A process map based on the results is presented as a tool for drilling process design and optimization for the investigated tool/material combination.

Structural damage detection in beams by wavelet transforms

The use of a laser-based optical system and wavelet transforms is explored for the detection of changes in the properties of cantilevered aluminum beams as a result of damage. The beams were modeled using the ANSYS 5.3 finite-element method and the first six mode shapes for the damaged and the undamaged cases obtained. Damage was simulated by a reduction in the stiffness of one element. Gaussian white noise was added externally to simulate field conditions. The results show that a spatially-localized abnormality in the mode shape could be represented uniquely by a small set of wavelet coefficients while the white noise was uniformly spread throughout the wavelet space. It was observed that the damage clearly manifested in the sixth-order detail of certain modes only. A different finite-element model was used as a test beam to validate the proposed method. An actual aluminum beam, fabricated with dimensions similar to the test beam, was excited and the mode shapes recorded with the scanning laser vibrometer. Damage was created by machining a notch in the beam of the same dimensions as the finite-element test beam. An image of the damage location was obtained from the continuous wavelet transform coefficients. The magnitude of the wavelet coefficients at the damage location showed a close correlation to the severity of damage. It was observed to increase with increasing damage. The finite-element test beam results showed a close correlation to the corresponding experimental beam results. The method benefits from the fact that the undamaged mode shapes were not used to evaluate the condition of the beam, which in most field conditions is not feasible.

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

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 backpropagation 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 non-linear strain-displacement relations. The non-linear 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 on-line health-monitoring system.

Predicting quality characteristics of end-milled parts based on multi-sensor integration using neural networks: individual effects of learning parameters and rules

Artificial neural networks have been shown to have a lot of potential as a means of integrating multi-sensor signals for real-time monitoring of machining processes. However, many questions still remain to be answered on how to optimize the training parameters during the training phase to optimize their subsequent performance, especially in view of the fact that the few published articles have made conflicting recommendations. This paper presents a systematic evaluation of the individual effects of training parameters — learning rate, momentum rate, number of hidden layer nodes, transfer function and learning rule-on the performance of back-propagation networks used for predicting quality characteristics of end-milled parts. Multi-sensor signatures (acoustic emission, spindle vibration, cutting force components and machining time) acquired during circular end-milling of 4140 steel and the corresponding measured quality characteristics (surface roughness and bore tolerance) were used to train the networks. The network is part of a proposed intelligent machining monitoring and diagnostic system for quality assurance of machined parts. The network performances were evaluated using four different criteria: maximum error, rms error, mean error and number of training cycles. One of the results obtained shows that the hyperbolic tangent transfer function gives a better performance than the sigmoid and sine functions respectively. Optimum combinations of training parameters have been observed. The effects of various combinations of training parameters are presented.

Development of Web-Based Virtual CNC Milling Machine Tool With Mechanistic Cutting Force Models for Education and Learning

This paper presents the results of the development of Virtual Computer Numerical Control Milling Machine Tool (VCNC-MMT) with cutting force models for web-based education and learning. This research is divided into five parts: 1) Virtual modeling of the machine parts, work-piece, cutting tools, and fixtures. 2) Assembly of the virtual components and assignment of the kinematics to the VCNC-MMT. 3) Development of virtual controller and offline simulation of machining process. 4) Implementation of the VCNC-MMT and simulation on the internet using X3D modeling language for long distance education, learning and training. 5) Development of the mechanistic cutting force models and incorporation with the VCNC-MMT. Mechanistic cutting force models for helical end mills with corner radius has been completed and simulated using MATLAB.© 2010 ASME

Acoustic Emission Detection and Prediction of Fatigue Crack Propagation in Composite Patch Repairs Using Neural Networks

An aircraft is subjected to severe structural and aerodynamic loads during its service life. These loads can cause damage or weakening of the structure especially for aging military and civilian aircraft, thereby affecting its load carrying capabilities. Hence composite patch repairs are increasingly used to repair damaged aircraft metallic structures to restore its structural efficiency. This paper presents the results of Acoustic Emission (AE) monitoring of crack propagation in 2024‐T3 Clad aluminum panels repaired with adhesively bonded octagonal, single sided boron/epoxy composite patch under tension‐tension fatigue loading. Crack propagation gages were used to monitor crack initiation. The identified AE sensor features were used to train neural networks for predicting crack length. The results show that AE events are correlated with crack propagation. AE system was able to detect crack propagation even at high noise condition of 10 Hz loading; that crack propagation signals can be differentiated from...