Gradual error detection technique for non-destructive assessment of density and tensile strength in fused filament fabrication processes

Abstract

Fused filament fabrication (FFF) is a widely used additive manufacturing process for producing functional components and prototypes. The FFF process involves depositing melted material layer-by-layer to build up 3D physical parts. The quality of the final product depends on several factors, including the component density and tensile strength, which are typically determined through destructive testing methods. X-ray microtomography (XCT) can be used to investigate the pore sizes and distribution. These approaches are time-consuming, costly, and wasteful, making it unsuitable for high-volume manufacturing. In this paper, a new method for non-destructive determination of component density and estimation of the tensile strength in FFF processes is proposed. This method involves the use of gradual error detection by sensors and convolutional neural networks. To validate this approach, a series of experiments has been conducted. Component density and tensile strength of the printed specimens with varying extrusion factor were measured using traditional destructive testing methods and XCT. The cumulative error detection method was used to predict the same properties without destroying the specimens. The predicted values were then compared with the measured values, and it was observed that the method accurately predicted the component density and tensile strength of the tested parts. This approach has several advantages over traditional destructive testing methods. The method is faster, cheaper, and more environmentally friendly since it does not require the destruction of the product. Moreover, it facilitates the testing of each individual part instead of assuming the same properties for components from one series. Additionally, it can provide real-time feedback on the quality of the product during the manufacturing process, allowing for adjustments to be made as needed. The advancement of this approach points toward a future trend in non-destructive testing methodologies, potentially revolutionizing quality assurance processes not only for consumer goods but various industries such as electronics or automotive industry. Moreover, its broader applications extend beyond FFF to encompass other additive manufacturing techniques such as selective laser sintering (SLS), or electron beam melting (EBM). A comparison between the old destructive testing methods and this innovative non-destructive approach underscores the possible fundamental change toward more efficient and sustainable manufacturing practices. This approach has the potential to significantly reduce the time and cost associated with traditional destructive testing methods while ensuring the quality of FFF-manufactured products.