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