Shape control is a critical task in the composite fuselage assembly process due to the dimensional variabilities of incoming fuselages. To realize fuselage shape adjustment, actuators are used to pull or push several points on a fuselage. Given a fixed number of actuators, the locations of actuators on a fuselage will impact on the effectiveness of shape control. Thus, it is important to determine the optimal placement of actuators in the fuselage shape control problem. In current practice, the actuators are placed with equal distance along the edge of a fuselage without considering its incoming dimensional shape. Such practice has two limitations: (1) it is non-optimal and (2) larger actuator forces may be applied for some locations than needed. This paper proposes an optimal actuator placement methodology for efficient composite fuselage shape control by developing a sparse learning model and corresponding parameter estimation algorithm. The case study shows that our proposed method achieves the optimal actuator placement for shape adjustments of the composite fuselage.

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Shape control of composite parts is vital for large-scale production and integration of composite materials in the aerospace industry. The current industry practice of shape control uses passive manual metrology. This has three major limitations: (i) low efficiency: it requires multiple trials and a longer time to achieve the desired shape during the assembly process; (ii) nonoptimal: it is challenging to reach optimal deviation reduction; and (iii) experience-dependent: highly skilled engineers are required during the assembly process. This paper describes an automated shape control system that can adjust composite parts to an optimal configuration in a manner that is highly effective and efficient. The objective is accomplished by (i) building a finite element analysis (FEA) platform, validated by experimental data; (ii) developing a surrogate model with consideration of actuator uncertainty, part uncertainty, modeling uncertainty, and unquantified uncertainty to achieve predictive performance and embedding the model into a feed-forward control algorithm; and (iii) conducting multivariable optimization to determine the optimal actions of actuators. We show that the surrogate model considering uncertainties (SMU) achieves satisfactory prediction performance and that the automated optimal shape control system can significantly reduce the assembly time with improved dimensional quality.

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