Curing autoclave

S.2.2.2. Composite adherends. Composite adherends are bonded in both the cured and uncured states. Wherever possible the adhesive and all adherends are cured simultaneously to avoid the added cost of additional autoclave cure cycles. In many cases this is not practical due to part size and complexity. Cured parts can be bonded to uncured parts, which is known as cobonding, and fully cured parts can be bonded together, which is known as secondary bonding. Adhesives for composites are formulated to be compatible with matrix resins in either cured or uncured states. 

First part qualification. First part qualification is a process performed the first time a new bonded assembly is manufactured or the first time a new tool is used to manufacture a bonded assembly. First part qualification provides assurance that all of the aspects that control bond assembly quality, such as the design dimensions, detail part manufacturing techniques, tool dimensions, layup procedures and autoclave cure cycle parameters are correct and will produce a bond assembly that meets the engineering requirements. 

Figures 4.10 and 4.11, which can be compared to the autoclave cure in Figure 4.6, show two important applications of FDEMS sensing as applied to processing of an epoxy system. Figure 4.10 shows the effect on processing properties as monitored by the value of coz"((d) for...

Figure 4.15 The viscosity at each sensor position of a 192-ply graphite-epoxy composite during an FDEMS sensor-controlled autoclave cure...

Ciriscioli, P., Springer G. Smart Autoclave Cure Technomic (1990) Lancaster, Penn. 

Evolving from efforts [22] to use the best features of trial-and-error, process model, expert system, and expert model approaches, QPA [23-25] combines KBES traits with online dielectric, pressure, and temperature data to implement autoclave curing control. QPA combines extensive sensor data with KBES rules to determine control actions. These rules determine curing progress based upon process feedback, and implement control action. QPA adjusts production parameters on-line as such—within the limits of its heuristics—QPA can accommodate batch-to-batch prepreg variations. 

Because it considers no analytical model, QPA does not make explicit use of heat transfer dynamics. Nonetheless, QPA does reduce the autoclave curing cycle durations in several experimental autoclave curing runs. 

The objective of the Springer KBES is twofold To ensure a high-quality part in the shortest autoclave curing cycle duration. This KBES is similar to QPA in that sensor outputs are combined with heuristics not with an analytical curing model. The rules for compaction dictate that dielectrically measured resin viscosity be held Constant during the First temperature hold in the autoclave curing run. The autoclave temperature is made to oscillate about the target hold temperature in an attempt to attain constant viscosity. Full pressure is applied from the cure cycle start. 

Perry and Lee [28,29] offer an enhancement of QPA, based upon use of dual heat flux sensors and additional thermocouples in autoclave curing. This enhancement entails determining heat transfer properties during the cure, then using these properties in conjunction with PID regulatory control of autoclave temperature. Using the additional sensors, Perry and Lee employ an on-line Damkohler number in lieu of the second time-derivative of temperature to avoid exothermic thermal runaway within the prepreg stack thermoset resin. The Damkohler number is defined as ... 

For purposes of this chapter, model-predictive control can be discussed in the context of continuous and batch process applications. The former constitutes traditional applications the latter describes processes such as autoclave curing. We will discuss the former first, to put the latter into context. 

Batch processes, such as autoclave curing, are inherently nonlinear and dynamic. For on-line quality control, the model must predict the outcome of the batch (i.e., product quality) in terms of the input and processing variables. The variables associated with the process are ... 

Intermediate secondary measurements, y Dependent variables whose values are recorded by sensors used in the process. These variables are indirect indicators of final product quality as such, they should be useful in predicting autoclave curing outcomes. 

For use in a strategy such as SHMPC, a model must also be relatively small because it must provide real-time output in an on-line application. In a batch process, such as autoclave curing, model output must be available before the process moves into its next phase (i.e., before the next measurement is recorded). This requirement for real-time models in an on-line application limits the types of models that can serve in SHMPC. 

When available, fundamental process models are preferred. For many complex processes such as composite manufacturing in general and autoclave curing in particular, however, these models are often not available. This lack of availability is due to an inadequate understanding of the complex events that take place during the process. A fundamental process model is occasionally available, but it is still unsuitable for on-line model predictive control application due to the extensive computing time required to solve the model s equations. This lack of... 

For continuous process systems, empirical models are used most often for control system development and implementation. Model predictive control strategies often make use of linear input-output models, developed through empirical identification steps conducted on the actual plant. Linear input-output models are obtained from a fit to input-output data from this plant. For batch processes such as autoclave curing, however, the time-dependent nature of these processes—and the extreme state variations that occur during them—prevent use of these models. Hence, one must use a nonlinear process model, obtained through a nonlinear regression technique for fitting data from many batch runs. 

Ciriscioli, P.R., Springer, G.S. Smart Autoclave Cure of Composites (1990) Technomic Publishing Co. Inc., Lancaster, Penn... 

Part heat-up rate during autoclave processing can dramatically influence final part quality. At least three variables can affect the autoclave heat-up rate for composite parts (1) tool material and design, (2) the actual placement of the tool within the autoclave, and (3) the autoclave cure cycle used. Recommendations for the design of an individual tool are fairly obvious and well understood in industry (e.g., thin tools heat faster than thick tools materials with a high thermal conductivity heat faster than those with lower thermal conductivity and tools with well-designed gas flow paths heat-up faster than those with restricted flow paths [e.g., tools... 

Dave, R., Kardos, J., Dudukovic, M. Process Modeling of Thermosetting Matrix Composites A Guide For Autoclave Cure Cycle Selection, American Society for Composites, First Technical Conference, Dayton, OH, Oct. 1986... 

Abrams, F., et al. Qualitative Process Automation for Autoclave Curing of Composites, AFWAL-TR-87-4083, Interim Report for the Period 15 October-15 May 1987... 

The chapter is divided into a section on development of process cycles or plans and a section on in-process control. The tools to be discussed include design of experiments, expert systems, models, neural networks, and a variety of combinations of these techniques. The processes to be discussed include injection molding, resin transfer molding, autoclave curing, and prepreg manufacturing. The relative cost and difficulty of developing tools for these applications will be discussed where data is available. 

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