Quality Control of Metal Additive Manufacturing

Abstract

Metal Additive Manufacturing (AM) is an emerging technology for rapid prototype manufacturing, and the structural integrity of printed structures is extremely important and should meet the specifications and high standards of the above industries. In several metal AM techniques, residual stresses and micro-cracks that occur during the manufacturing procedure can result in irreversible damage and structural failure of the object after its manufacturing. Thus effective quality control of AM is highly required. Most Non-Destructive Testing (NDT) techniques (X-Ray, Computed Tomography, Thermography) are ineffective in detecting residual stresses. Bulk, cost, and resolution are limitations of such technologies. These methods are time consuming both for data acquisition and data analysis and have not yet been successfully integrated into AM technology. However two sets of NDT techniques: Electromagnetic Acoustic Transducers (EMAT) and Eddy Current (EC) Testing, can be applied for residual stress detection for AM techniques. Therefore a crucial and novel extension system incorporation of big data collection from sensors of the both techniques and analysis through machine learning (ML) can estimate the likelihood of the AM techniques to introduce anomalies into the printed structures, which can be used as an on-line monitoring and detection system to control the quality of AM.