Abstract Additive manufacturing (AM) is a revolutionary technology that fabricates parts layerwise and provides many advantages. This article discusses polymer AM processes such as material extrusion, vat photopolymerization (VPP), powder-bed fusion (PBF), binder jetting (BJ), material jetting (MJ), and sheet lamination (SL). It presents the benefits of online monitoring and process control for polymer AM. It also introduces the respective monitoring devices used, including the models and algorithms designed for polymer AM online monitoring and control. 

Online Monitoring and Control of Polymer Additive Manufacturing Processes

The quality of the filling algorithm in additive manufacturing directly affects the strength of the part. The commonly used 3D printing filling algorithm at this stage is the classic filling algorithm. The density of each part in the filling region is the same, and there is a cavity structure in the transverse direction, which makes the strength of the part in the transverse direction lower. Therefore, this paper proposed a new filling algorithm—variable density filling algorithm based on Delaunay triangulation. First, we performed concave-polygon-convex decomposition on the filling area to form printing sub-regions; then, the filling density value was set according to the required intensity of each region, and we used the Poisson disk sampling algorithm to generate the filling point set. Finally, Delaunay triangulation was performed on the generated point set to generate filled traces. The comparison with the two commonly used classical filling algorithms proves that the algorithm can improve the strength of the part to a certain extent, and the printing time and the consumption of consumables will not increase significantly.

/pdf/variable-density-filling-algorithm-based-on-delaunay-7fe4rxgm.pdf

Variable Density Filling Algorithm Based on Delaunay Triangulation

Qualitative and quantitative characterization of powder bed quality in laser powder-bed fusion additive manufacturing by multi-task learning

Three-dimensional printing, often known as additive manufacturing (AM), is a groundbreaking technique that enables rapid prototyping. Monitoring AM delivers benefits, as monitoring print quality can prevent waste and excess material costs. Machine learning is often applied to automating fault detection processes, especially in AM. This paper explores recent research on machine learning-based mechanical fault monitoring systems in fused deposition modeling (FDM). Specifically, various machine learning-based algorithms are applied to measurements extracted from different parts of a 3D printer to diagnose and identify faults. The studies often use mechanical-based fault analysis from data gathered from sensors that measure attitude, acoustic emission, acceleration, and vibration signals. This survey examines what has been achieved and opens up new opportunities for further research in underexplored areas such as SLM-based mechanical fault monitoring.

/pdf/exploring-machine-learning-based-fault-monitoring-for-1p37sf5g.pdf

Exploring Machine Learning-Based Fault Monitoring for Polymer-Based Additive Manufacturing: Challenges and Opportunities

With the development of 3D printing technology, people put forward higher requirements for molding quality and efficiency. In the whole printing system, the design of scanning path directly affects the geometric accuracy, strength and molding efficiency of products. Simple parallel path and contour path can no longer meet the needs of manufacturing industry. In this paper, a new filling algorithm is proposed. On the basis of continuous filling without pause, the connection of sub-regions is realized by using Fermat spiral. The new algorithm integrates convex decomposition, PCA, variable spacing filling and other operations to further improve the printing efficiency. Finally, the superiority of the new algorithm is verified by comparing with the common algorithm.

An Optimal Filling Path Planning Based on Principal Component Analysis

With the increasing application of additive manufacturing in various fields, it is particularly important to monitor the manufacturing process, product quality, and printer status aiming at obtaining high-quality manufactured products. This paper introduces a kind of additive manufacturing process monitoring based on the vibration data of the fused deposition modeling(FDM). The random forest model has been applied to identify the normal state and filament jam state of the printer, which could achieve an accuracy rate of 94 %. The experimental results demonstrate that the proposed method can accurately identify the mechanical faults in the process of additive manufacturing, and effectively monitor the machine health and ensure the fabricated parts quality.

In-Situ Monitoring of Additive Manufacturing Process Based on Vibration Data

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An Optimal Filling Path Planning Based on Principal Component Analysis

In-Situ Monitoring of Additive Manufacturing Process Based on Vibration Data