# Optimization of Carbon Fiber Reinforced Plastic Curing Parameters for Aerospace Application
## 1 Introduction
Similarly, the optimization of surface finish, void formation, void distribution, tensile strength, flexural strength, stiffness, and mass has also been investigated in literature [23; 32]. DOE is a systematic data collection and analysis tool that is used to determine the effects of curing parameters on the properties. Determination of optimum CFRP properties requires DOE in the curing parameters space followed by experimentation. An efficient DOE helps to minimize the number of experiments and saves time and resources. This was achieved by exploiting available information about the curing process (Section 2) which was then used in selecting the levels of curing parameters. This helped to choose the starting point in the likely vicinity of the optimum property, which made the optimization efficient, requiring a lower number of experiments. In contrast, other advanced optimization techniques such as artificial neural networking (ANN) cannot be used for optimization with a smaller number of experiments. The results predicted by ANN do not always describe the most effective improvement. For example, the main effects of a process parameter cannot be described as statistically significant with clarity. However, these limitations of ANNcan be overcome if used with DOE and response surface methodology (RSM) as a hybrid model [34]. DOE provides a high level of control over the variables, allowing researchers to utilize as many variations as they want without destroying the validity of the research design, leading to excellent results. Some of the standard design of experiments (DoE) techniques, i.e., full factorial [24], fractional factorial, central composite [35], and Taguchi design [20], were used for experimentation in various optimization schemes. Generally, it is a common practice to develop intricate mathematical objective functions between curing parameters and properties for optimization using numerical methods [23; 32; 36]. In the case of multiple properties optimization, the mathematical modeling of the objective function becomes even more complex. This requires the use of a heuristic algorithm to find out the optimum parameter at the cost of accuracy and precision [22; 36]. However, most of the referred studies are generic and not targeted at a specific engineering application. For aerospace engineering applications such as the skin of an aircraft (aimed in this work), mechanical properties such as tensile strength and impact toughness have generally conflicting objective functions. Hence, engineering decisions about trade-off amongst various available options becomes a necessity. In this work, an aerospace-specific multiple properties optimization scheme for a CFRP with a minimal number of experiments is presented. A full factorial 2\({}^{3}\) DoE was used for the design and execution of experiments. A reference CC and seven other CCs were designed using existing knowledge of composite properties. The mechanical tests for tensile strength, impact toughness, and hardness were conducted. ANOVA was used to identify significant curing parameters and interactive terms, with a 95% confidence level [37; 38]. Regression equations and three-dimensional surface plots were then developed between significant curing parameters and properties of CFRP. The concept of composite desirability function was used to trade-off between conflicting properties of tensile strength and impact toughness [39; 40; 41; 42]. With the existing knowledge of some of the composite properties, the approach presented in this paper can be used to quickly reach optimization point without expanding large experimental resources.