Material characterisation of additive manufacturing titanium alloy (Titanium 6Al-4V) for quality control and properties evaluations. Issue 4 (2nd October 2022)

• Record Type: Journal Article
• Authors: Hossain, Md Shahjahan; Baniasadi, Mahmoud; Taheri, Hossein
• Is Part Of: Advances in materials and processing technologies. Volume 8:Issue 4(2022)
• Page Start: 4678
• Page End: 4697
• Publication Date: 2022-10-02
• Subjects: Additive manufacturing (AM) -- direct energy deposition (DED) -- nanoindentation -- non-destructive testing (NDT) -- material characterisation; Materials -- Periodicals; Manufacturing processes -- Periodicals; 620.1105
• DOI: 10.1080/2374068X.2022.2079589
• Languages: English
• ISSNs: 2374-068X

Abstract

ABSTRACT: Current additive manufacturing (AM) technologies use various raw materials such as plastics and metals to fabricate parts. Moreover, the increasing use of metallic parts makes AM applicable to commercial use such as aeronautical, and medical field. For this reason, manufacturing process quality evaluation becomes essential to achieve higher quality products. Quality control and material property assessment for AM parts is a major challenge due to the complexity of the materials and geometry of the parts. Non-Destructive Testing (NDT) is being proposed as a cost-effective way to evaluate the quality of the product. For this reason, the nanoindentation, scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS) techniques are being used as useful forms of NDT techniques to assess the quality and material characteristics in metal AM samples. The material properties of additively manufactured Titanium 6Al-4 V alloy are studied to verify the in-situ process monitoring published results at different manufacturing process conditions. The comparison of material properties of three different process conditions made by direct energy deposition (DED) metal additive manufacturing is discussed to understand the variation. The results show that the characteristics of materials are a function of process parameters and have a strong correlation to the expected in-situ monitoring indicators.