Parameter Optimization of Robotize Line Scan Thermography for CFRP Composite Inspection

• Published in: Journal of nondestructive evaluation Vol. 37; no. 1; pp. 1 - 9
• Main Authors: Khodayar, Fariba, Lopez, Fernando, Ibarra-Castanedo, Clemente, Maldague, Xavier
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2018; Springer Nature B.V
• Subjects: Aerial thermography; Aerospace industry; Aircraft components; Algorithms; Carbon fiber reinforced plastics; Characterization and Evaluation of Materials; Classical Mechanics; Composite materials; Computer simulation; Control; Defects; Destructive testing; Dynamical Systems; Engineering; Fiber reinforced polymers; Infrared cameras; Inspection; Mathematical models; Mechanical properties; Nondestructive testing; Optimization; Porous materials; Robot arms; Scanning; Signal processing; Signal to noise ratio; Solid Mechanics; Three dimensional models; Vibration; Line scan thermography; Optimization; Finite element; Signal to noise
• ISSN: 0195-9298; 1573-4862
• DOI: 10.1007/s10921-017-0459-8

Demands for Composite materials is increasing more and more because of their specific mechanical properties, especially in aerospace industry. Due to the porous structure of composite materials, there is the negligible probability of breaking up and defects in the internal structure. Detection of deep defects is a challenging subject in the field of Non-destructive testing. Due to the large size of composite components in the aerospace industry, line scanning thermography (LST) coupled with a robot arm is used to inspect large composite materials. In this paper, an innovative optimization procedure has been employed using analytical model, 3-D simulation using COMSOL Multiphysics, experimental setup and signal processing algorithms. The goal is to maximize the detection depth and signal to noise value as the criteria to evaluate the inspection quality and performance. the proposed algorithm starts searching to find the optimization variables of robotized LST such as scanning speed, source power and distance considering all technical and mechanical constraints. The optimal values are dependent on the material structure, thermal specifications of the composite, defect shape and infrared camera resolution. Using the proposed optimization algorithm, the detection depth was increased to 3.5 mm in the carbon fiber reinforced polymer and the signal to noise ratio was enhanced to 95%.