On Smart Geometric Non-Destructive Evaluation: Inspection Methods, Overview, and Challenges.

Inspection methods, also known as non-destructive evaluation (NDE), is a process for inspecting materials, products, and facilities to identify flaws, imperfections, and malfunctions without destruction or changing the integrity of materials, structures, and mechanisms. However, detecting those defects requires test conducting and results inferring, which is highly demanding in terms of analysis, performance, and time. New technologies are therefore needed to increase the efficiency, probability of detection, and interpretability of NDE methods to establish smart inspection. In this context, Artificial intelligence (AI), as a fundamental component of the Industry 4.0, is a well-suited tool to address downsides associated with the current NDE methods for analysis and interpretation of inspection results, where methods integrating AI into their inspection process become automated and are known as smart inspection methods. This article sheds a light on the conventional methods and the smart techniques used in defects detection. Subsequently, a comparison between the two notions is presented. Furthermore, it investigates opportunities for the integration of non-destructive evaluation (NDE) methods and Industry 4.0 technologies. In addition, the challenges hindering the progress of the domain are mentioned as the potential solutions. To this end, along with Industry 4.0 technologies, a virtual inspection system has been proposed to deploy smart inspection.

Anisotropic Behavior of Al1050 through Accumulative Roll Bonding.

In this study, Al1050 sheets were fabricated in five passes using the accumulative roll bonding (ARB) technique. For a more accurate and complete investigation, different tests were used, including a uniaxial tensile test. The results show that elongation increases about 50% for the annealed sample, which is 2.5 times that of the fifth pass (20%). A five-fold increase can be seen in tensile strength, which was 50 MPa in the annealed sample and reached 250 MPa at the end of the fifth pass. The annealed sample's yield stress was 40 MPa, 4.5 times less than 180 MPa after five passes of ARB. Then, to evaluate sample hardness, the Vickers microhardness test was conducted in the samples' depth direction, which recorded 39 HV for the annealed piece and 68 HV after the last ARB pass. These results show that the hardness increases by 1.8 times after five passes of ARB. In the next step, by conducting fractography tests after the sample fractures during the tensile test, the fracture's mechanism and type were identified and explained. Finally, X-ray diffraction (XRD) was employed to produce pole figures of sample texture, and the anisotropy phenomena of the annealed sample and ARBed samples were wholly examined. In this study, with the help of pole figures, the anisotropic behavior after ARB was investigated and analyzed. In each step of the process, observing the samples' texture states and the anisotropy magnificent was possible. According to the results, normal anisotropy of 0.6 in the annealed sample and 1.8 achieved after the fifth pass of ARB indicates that ARB leads to an increase in anisotropy.