Proposing an intelligent approach for measuring the thickness of metal sheets independent of alloy type. (July 2019)

• Record Type: Journal Article
• Authors: Nazemi, E.; Aminipour, M.; Olfateh, A.; Golgoun, S.M.; Davarpanah, M.R.
• Is Part Of: Applied radiation and isotopes. Volume 149(2019:Jul.)
• Page Start: 65
• Page End: 74
• Publication Date: 2019-07
• Subjects: Thickness meter -- Gamma ray -- Metal -- Alloy -- ANN -- MCNPX; Radiology -- Periodicals; Radiation -- Industrial applications -- Periodicals; Nuclear chemistry -- Periodicals; Internet resource; Periodical; 660.298
• DOI: 10.1016/j.apradiso.2019.03.023
• Languages: English
• ISSNs: 0969-8043

Abstract

Abstract: Radiation based gauges have been widely utilized as a nondestructive and robust tool for measuring the thickness of metal sheets in industry. The typical radiation thickness meter can just work accurately when the composition of the material is fixed during the measurement process. In conditions that material composition may differ substantially from the nominal composition, such as manufacturing rolled metals factories, the thickness measurements would be along with errors. The purpose of the present research is resolving the problem of measuring the thickness of metal sheets with various alloys. The aluminum is investigated in this work as a case study but the procedure can be applied for other types of metals. As the first step, the performance of various arrangements of two main detection techniques, named dual energy and dual modality, were investigated using MCNPX code to obtain optimum technique and arrangement. The simulation results indicated that a binary combination of 241 Am- 60 Co isotopes as the source and one transmission detector in dual energy technique is the most appropriate choice. After then, an experimental setup based on the obtained optimal technique from simulation investigations was established. The aluminum sheets with 4 alloy types of 1050, 3105, 5052 and 6061 and thicknesses in the range of 0.2–4 cm with a step of 0.2 cm were tested and the obtained data were implemented for testing and training the artificial neural network (ANN). The proposed methodology could predict the thickness of aluminum sheet independent of its alloy type with an error of less than 0.04 cm in experiments. Highlights: Performance of 2 main detection techniques, named dual energy and dual modality, were investigated using MCNPX code. The simulation indicated that a binary combination of 241 Am- 60 Co in dual energy technique is the most appropriate choice. An experimental setup based on the obtained optimal technique from simulations was established. The ANN model could predict the thickness of aluminum sheet independent of its alloy type with an error of less than 0.04 cm.