A Review of Ex-situ, In situ and Artificial Intelligence-based Thermographic Measurements in Additively Manufactured Parts

Abstract

Additive manufacturing (AM) encompasses a range of advanced production methods that are increasingly applied across various sectors, particularly where customisation, high-strength materials, or complex parts are required. However, a key challenge remains the need for rapid methods and non-destructive testing (NDT) technologies to ensure part quality, particularly for detecting internal defects. Among these methods, infrared thermography (IRT) is gaining popularity due to its ease of use and low overall system cost (hardware, data acquisition, and processing) when compared to more complex techniques like tomography. AM can greatly benefit from IRT, both ex-situ for quality control and in-situ for process monitoring. This paper reviews the current literature on the application of IRT in the AM field. It examines IRT as a standard method for detecting typical defects in AM parts ex-situ, after the manufacturing process. The effectiveness of IRT techniques is evaluated in terms of their ability to detect defects based on size and depth. The paper also explores the use of IRT for in-situ process monitoring, where thermograms are captured during production and analysed to identify defects early. The advantages and limitations of IRT are discussed and compared with other NDT techniques. Additionally, the use of numerical simulation and artificial intelligence (AI) in enhancing IRT applications is reviewed. The findings highlight that while IRT is a valuable tool for defect characterisation in AM, significant potential remains for developing more advanced and efficient approaches that integrate data from multiple sources.