A methodology to determine the thermal diffusivity of additively manufactured jet engine blades based on laser-induced temperature field. (15th November 2022)

• Record Type: Journal Article
• Authors: Adamczyk, Wojciech; Pawlak, Sebastian; Durejko, Tomasz; Łazińska, Magdalena; Białecki, Ryszard; Orlande, Helcio R.B.; Widuch, Agata; Polański, Marek
• Is Part Of: Measurement. Volume 203(2022)
• Publication Date: 2022-11-15
• Subjects: Jet engine blade -- Laser engineered net shaping (LENS®) -- Thermal diffusivity -- Laser spot thermography -- Machine learning -- Predictive model; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111955
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: This paper presents a new methodology for the thermal diffusivity determination of additively manufactured jet engine blades by using active infrared thermography. The technique is combined with a dedicated procedure based on deep machine learning models. The investigated samples were fabricated from different metal alloy powders by using the Laser Engineered Net Shaping (LENS®) technique. The experimental data for model development were in the form of laser shot-induced temperature fields, obtained from an original, in-house developed test apparatus. The advantage of the proposed methodology is that it can be used for the non-destructive measurements of the additively manufactured jet engine blades, which distinguishes the presented approach from the traditional laser-flash technique or other well-established methods. The obtained experimental values of the thermal diffusivity are in good agreement with data measured using the ASTM standard test method. The presented non-destructive technique has significant implementation/commercialization potential when applied to quality control or diagnostic procedures of 3D-printed parts. Graphical abstract: Highlights: NDT technique for the thermal diffusivity assessment of the 3D-printed engine blades. Experimental data obtained for different metal alloys. High accuracy of the developed predictive model. Significant implementation/commercialization potential of proposed methodology.