Determination and optimization for the analysis of factors affecting paint thickness in the process of painting aircraft detail parts with horizontal and fixed-axis robots

The weights of the materials used during the production of aircraft have critical importance in terms of the operational costs of the aircraft. Fuel expenses increase depending on the weight of the aircraft. In this context, solutions that minimize weight are sought to be produced during the production and design phase of the product. One of the factors contributing to weight in aircraft is the surface coatings of structural materials. Among these coatings, paint contributes the most to weight. Paints serve critical functional purposes for aircraft, such as protecting materials from corrosion, meeting customers' cosmetic expectations, and providing other functional features (such as signaling, etc.). While the applied paint is expected to fulfill these functional requirements for the material, efforts are also made to minimize paint consumption. In the process of paint application, numerous factors influence paint thickness. Due to the difficulty and complexity of controlling these factors, designers provide applicator companies with specified thickness values within a certain range (e.g., between 15-25 microns). Two main problems can arise in production areas according to the multitude of factors affecting paint thickness and the variability of these factors. The first problem is the issue of thick/thin paint obtaining a paint thickness after application that is below the specified lower limit or above the upper limit defined by the customer/designer. Secondly, even if the paint thickness falls within the range specified by the customer, it may not be close to the lower limit of the range; consequently, this leads to an increase in both paint consumption and operational costs of the aircraft due to the weight imposed by the paint. In the scope of the study, initially, factors influencing paint thickness in the process of painting detail parts with horizontal and fixed-axis robots were identified, along with the lower and upper limits associated with these factors. Subsequently, a factorial experimental design was employed to create an experimental plan, and experiments were conducted accordingly. Furthermore, a normal distribution analysis was performed for all variables, and the relationships between independent variables and both each other and dependent variables were examined. After the analysis, factorial experimental analysis was conducted, ANOVA and regression models were scrutinized, and the optimum levels of the factors were determined. Verification experiments were then conducted based on the identified optimum levels. As a result of all these processes, it was observed that by optimizing only the parameters in the horizontal and fixed-axis robotic painting process, both paint consumption could be reduced, and quality defects resulting from paint thickness could be eliminated, leading to financial gains.