Abstract
Fused filament fabrication (FFF) is one of the most widely used additive manufacturing (AM) technologies and it has great potential in fabricating prototypes with complex geometry. For high quality manufacturing, monitoring the products in real time is as important as maintaining the FFF machine in the normal state. This paper introduces an approach that is based on the vibration sensors and data-driven methods for in-situ monitoring and diagnosing the FFF process. The least squares support vector machine (LS-SVM) algorithm has been applied for identifying the normal and filament jam states of the FFF machine, besides fault diagnosis in real time. The identification accuracy for the case studies explored here using LS-SVM is greater than 90%. Furthermore, to ensure the product quality during the FFF process, the back-propagation neural network (BPNN) algorithm has been used to monitor and diagnose the quality defects, as well as the warpage and material stack caused by abnormal leakage for the products in-situ. The diagnosis accuracy for the case studies explored here using BPNN is greater than 95%. Results from the experiments show that the proposed approach can accurately recognize the machine failures and quality defects during the FFF process, thus effectively assuring the product quality.
Highlights
The fused filament fabrication (FFF) technique is a generic additive manufacturing (AM)technology [1,2,3,4] for thermoplastic materials and has been increasingly used in various fields like aerospace and healthcare [5,6]
Experimental results during the FFF process have been analyzed
Results and Discussion the failure state and filament jam are diagnosed based on the least squares support vector machine (LS-support vector machine (SVM)) model
Summary
The fused filament fabrication (FFF) technique is a generic additive manufacturing (AM)technology [1,2,3,4] for thermoplastic materials and has been increasingly used in various fields like aerospace and healthcare [5,6]. Part with complex geometry at a relatively low cost. Qualifying these parts is challenging due to the open-loop control of the FFF process. There is an urgent need to develop an advanced monitoring and closed-loop quality control methods for monitoring the FFF process [3,7]. The objectives of in-situ monitoring and diagnosis of the FFF process can be divided into two groups: one for the states of the FFF machine [8,9,10,11,12,13], and the other for the quality of the building parts [14,15,16,17,18,19]
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