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The degree of cure and temperature are consistent variables used in models to describe the state of material behaviour development for a thermoset during cure. Therefore, the validity of a cure kinetics model is an underlying concern when combining several material models to describe a part forming process, as is the case for process modeling. The goals of this work are to identify sources of uncertainty in the decision-making process from cure measurement by differential scanning calorimeter (DSC) to cure kinetics modeling, and to recommend practices for reducing uncertainty. Variability of cure kinetics model predictions based on DSC measurements are investigated in this work by a study on the carbon-fiber-reinforced-plastic (CFRP) T800H/3900-2, an interlaboratory Round Robin comparison of cure studies on T800H/3900-2, and a literature review of cure models for Hexcel 8552. It is shown that variability between model predictions can be as large as 50% for some process conditions when uncertainty goes unchecked for decisions of instrument quality, material consistency, measurement quality, data reduction and modeling practices. The variability decreases to 10% when all of the above decisions are identical except for the data reduction and modeling practices. In this work, recommendations are offered for the following practices: baseline selection, balancing heats of reaction, comparing data over an extensive temperature range (300 K), choosing appropriate models to describe a wide range of behaviour, testing model reliability, and visualization techniques for cure cycle selection. Specific insight is offered to the data reduction and analysis of thermoplastic-toughened systems which undergo phase separation during cure, as is the case for T800H/3900-2. The evidence of phase separation is a history-dependent Tg-α relationship. In the absence of a concise outline of best practices for cure measurement by DSC and modeling of complex materials, a list of guidelines based on the literature and the studies herein is proposed.

The degree of cure and temperature are consistent variables used in models to describe the state of material behaviour development for a thermoset during cure. Therefore, the validity of a cure kinetics model is an underlying concern when combining several material models to describe a part forming process, as is the case for process modeling. The goals of this work are to identify sources of uncertainty in the decision-making process from cure measurement by differential scanning calorimeter (DSC) to cure kinetics modeling, and to recommend practices for reducing uncertainty. Variability of cure kinetics model predictions based on DSC measurements are investigated in this work by a study on the carbon-fiber-reinforced-plastic (CFRP) T800H/3900-2, an interlaboratory Round Robin comparison of cure studies on T800H/3900-2, and a literature review of cure models for Hexcel 8552. It is shown that variability between model predictions can be as large as 50% for some process conditions when uncertainty goes unchecked for decisions of instrument quality, material consistency, measurement quality, data reduction and modeling practices. The variability decreases to 10% when all of the above decisions are identical except for the data reduction and modeling practices. In this work, recommendations are offered for the following practices: baseline selection, balancing heats of reaction, comparing data over an extensive temperature range (300 K), choosing appropriate models to describe a wide range of behaviour, testing model reliability, and visualization techniques for cure cycle selection. Specific insight is offered to the data reduction and analysis of thermoplastic-toughened systems which undergo phase separation during cure, as is the case for T800H/3900-2. The evidence of phase separation is a history-dependent Tg-α relationship. In the absence of a concise outline of best practices for cure measurement by DSC and modeling of complex materials, a list of guidelines based on the literature and the studies herein is proposed.

The degree of cure and temperature are consistent variables used in models to describe the state of material behaviour development for a thermoset during cure. Therefore, the validity of a cure kinetics model is an underlying concern when combining several material models to describe a part forming process, as is the case for process modeling. The goals of this work are to identify sources of uncertainty in the decision-making process from cure measurement by differential scanning calorimeter (DSC) to cure kinetics modeling, and to recommend practices for reducing uncertainty. Variability of cure kinetics model predictions based on DSC measurements are investigated in this work by a study on the carbon-fiber-reinforced-plastic (CFRP) T800H/3900-2, an interlaboratory Round Robin comparison of cure studies on T800H/3900-2, and a literature review of cure models for Hexcel 8552. It is shown that variability between model predictions can be as large as 50% for some process conditions when uncertainty goes unchecked for decisions of instrument quality, material consistency, measurement quality, data reduction and modeling practices. The variability decreases to 10% when all of the above decisions are identical except for the data reduction and modeling practices. In this work, recommendations are offered for the following practices: baseline selection, balancing heats of reaction, comparing data over an extensive temperature range (300 K), choosing appropriate models to describe a wide range of behaviour, testing model reliability, and visualization techniques for cure cycle selection. Specific insight is offered to the data reduction and analysis of thermoplastic-toughened systems which undergo phase separation during cure, as is the case for T800H/3900-2. The evidence of phase separation is a history-dependent Tg-α relationship. In the absence of a concise outline of best practices for cure measurement by DSC and modeling of complex materials, a list of guidelines based on the literature and the studies herein is proposed. / Applied Science, Faculty of / Materials Engineering, Department of / Graduate

This research program investigates the performance of a steel truss bridge when subjected to both localized web damage and a subsequent post-tensioned strengthening approach. The investigation utilizes a combined approach involving an experimental scale model bridge and a numerical computer model generated using the commercial finite element software RISA 3-D. The numerical model is validated using test data and further extended to parametric studies in order to investigate the theoretical load rating, strain energy, load redistribution, mode shapes and frequency of the bridge for control, damaged and strengthened states. The presence and severity of damage are found to significantly influence the global safety and reliability of the bridge. Also, higher order modes are more susceptible to changes in shape and frequency in the presence of damage. A recovery of truss deflection and a reduction of member forces are achieved by the proposed strengthening method.

A lightweight carbon fiber reinforced polymer (CFRP) c-channel beam was previously designed using analytical theory and finite element analysis and subsequently manufactured through a pultrusion process. Physical testing revealed the prototype did not meet the bending and torsional stiffness of the beam model. An investigation revealed that the manufactured prototype had lower fiber content than designed, compacted geometry, an altered ply layup, missing plies, and ply folds. Incorporating these changes into the beam model significantly improved model-experiment agreement. Using what was learned from the initial prototype, several new beam designs were modeled that compare the cost per weight-savings of different composite materials. The results of these models show that fiberglass is not a viable alternative to CFRP when designing for equivalent stiffness. Standard modulus carbon was shown to have slightly lower cost per-weight savings than intermediate modulus carbon, although intermediate modulus carbon saves more weight overall. Core materials, despite potential weight savings, were ruled out as they do not have the crush resistance to handle the likely clamp loads of any attaching bolts. Despite determining the ideal materials, the manufactured cost per weight-savings of the best CFRP beam design was about double the desired target. / Graduation date: 2013 / Access restricted to the OSU Community at author's request from Oct. 5, 2012 - Oct. 5, 2014

When using Finite Element Analysis (FEA) to model notched composite panels, the values of certain material properties can have a great effect on the outcome of the simulation. Progressive damage modeling is used to model how a composite structure will fail, and how that failure will affect the response of the structure. Many different progressive damage models exist, but the formulation known as Hashin damage is used to model failure in tension and out-of-plane bending in this study. This model has ten different material properties that are used to define the damage response of the material. Each of these material properties must be calculated experimentally in a time consuming and expensive process. A method of determining which properties will have the greatest effect on the model, and therefore, which to spend the most money on accurate tests, is a factorial analysis sensitivity study. Studies of this nature have been used in many different situations regarding material properties testing and optimization. The work presented in this study uses several factorial analysis designs to perform a sensitivity study on the ten Hashin damage parameters in a variety of situations. Five different ply layups are used in modeling specimens that are loaded in tension and out-of-plane bending. The results of this study show that the significant factors depend on the ply layup and loading scenario, but there are generally less than three factors that play a significant role in modeling the failure of the panels. This means that in most cases, rather than spending substantial money on finding ten different material properties, the time and money can be focused on a small subset of the properties, and an accurate model can still be achieved. While the results of the scenarios presented may not apply to all scenarios, the methods presented can be used to perform a similar study in other specific scenarios to find the significant factors for that case. / Graduation date: 2013

Submitted in fulfillment of the requirements of the Degree of M.Tech.: Mechanical Engineering, Durban University of Technology, 2017. / Fatigue crack initiation and propagation behaviour of CFRP have been of great importance because such composites are often used in engineering components that are subjected to continuous cyclic loading. The objective of this thesis work was to investigate the damage characteristics of the fatigue properties of CFRP composites by the modification of the polymer matrix with nanoclay addition. Carbon fibre reinforced epoxy was produced via vacuum assisted resin infusion moulding method (VARIM) with nanoclay concentrations of 0wt%, 1wt%, 3wt% and 5wt%. Tension-tension fatigue tests were conducted at loading levels of 90%, 75% and 60%. The frequency that was used was 3Hz with R value of 0.1. The results showed that at nanoclay percentages of 0wt%, 1wt% and 3wt% there was a consistent trend, where the number of cycles increased in fatigue loading percentages of 90%, 75% and 60%. At 5wt% nanoclay percentage the number of fatigue cycles dropped significantly at the 90% fatigue loading. The brittle nature of the 5wt% laminate became dominate and the sample fractured early at low fatigue cycle numbers. At the 75% fatigue loading, the number of cycles increased and at 60% fatigue loading the 5wt% nanoclay sample exceeded the number of cycles of all the nanoclay percentages by 194%. This was due to the intercalated arrangement of the nanoclays favouring the slow rate of surface temperature increase, during fatigue testing, at low fatigue cycle loading. The Crack Density analysis was performed and showed that at the same time in the fatigue cycle life, the 1wt% had 55 cracks, 3wt% had 52 cracks and the 5wt% had 50 cracks, for the 60% fatigue loading. This proved that it took longer for the cracks to initiate and propagate through the sample as the nanoclay percentage increased. Impact and hardness testing showed that the 5wt% exhibited brittle behaviour, which contributed to the results above. Scanning electron microscopy examination highlighted that the agglomeration of nanoclays delayed the crack initiation and propagation through the specimen and that the extent of fatigue damage decreased as the nanoclay percentage increased. A fatigue failure matrix was developed and showed that delamination, fibre breakage and matrix failure were the predominate causes for the fatigue failure. / M

The demand for materials with high mechanical performances such as Carbon Fiber Reinforced Plastics (CFRP) is increasing. However, there are major challenges in machining CFRP as it involves delamination, fiber pullouts, and extreme cutting tool wear. Analysis of chip formation mechanisms and prediction of associated cutting forces in CFRP machining enables one to address these challenges. This study proposes a mechanistic cutting force model for milling operations of the CFRP workpiece, considering its non-homogeneity and anisotropy, by taking into account variations of fiber cutting angle during machining. A mechanistic model of cutting force constants is obtained from a number of experimentally measured unidirectional CFRP milling forces. The obtained mechanistic force model predictions are verified against experimentally measured milling forces with arbitrary tool path indicating the accuracy of the proposed mechanistic model in predicting cutting forces. The proposed mechanistic cutting force model is capable of being integrated into the manufacturing process to allow optimized machining of quality certified CFRP work-pieces. / Graduate

Corrosion of steel reinforcements is the leading causes of malfunction or even failures of reinforced concrete (RC) structures nationwide and worldwide for many decades. This arises up to substantial economic burden on repairs and rehabilitations to maintain and extend their service life of those RC public projects. The inherent natures of glass fiber-reinforced polymers (GFRP) bars, from their superior corrosion resistance to high strength-to-weight ratio, have promoted their acceptance as a viable alternative for steel reinforcement in civil infrastructures. Comprehensive understanding of the bond between GFRP bars and concrete, in particular under in-service conditions or extremely severe events, enables scientists and engineers to provide their proper design, assessment and long-term predictions, and ultimately to implement them toward the corrosion-free concrete products. This research aims to develop a holistic framework through an experimental, analytical and numerical study to gain deep understanding of the bond mechanism, behavior, and its long-term durability under harsh environments. The bond behavior and failure modes of GFRP bar to concrete are investigated through the accelerated aging tests with various environmental conditions, including alkaline and/or saline solutions, freezing-thawing cycles. The damage evolution of the bond is formulated from Damage Mechanics, while detailed procedures using the Arrhenius law and time shift factor approach are developed to predict the long-term bond degradation over time. Besides, the machine learning techniques of the artificial neural network integrated with the genetic algorithm are used for bond strength prediction and anchorage reliability assessment. Clearly, test data allow further calibration and verification of the analytical models and the finite element simulation. Bond damage evolution using the secant modulus of the bond-slip curves could effectively evaluate the interface degradation against slip and further identify critical factors that affect the bond design and assessment under the limit states. Long-term prediction reveals that the moisture content and elevated temperature could impact the material degradation of GFRP bars, thereby affecting their service life. In addition, the new attempt of the Data-to-Information concept using the machine learning techniques could yield valuable insight into the bond strength prediction and anchorage reliability analysis for their applications in RC structures. / ND NASA EPCoR (FAR0023941) / ND NSF EPSCoR (FAR0022364) / US DOT (FAR0025913)