No abstract

Additive manufacturing (AM) has brought about a revolution in the way we can manufacture complex products with customized features. AM has paved its way in the application areas ranging from aerospace, automotive, consumer to biomedical. AM of composites has attracted special attention due to its promise in improving, modifying, and diversifying the properties of generic materials through introducing reinforcements. This review provides a detailed landscape of fiber‐reinforced composites processed via AM techniques. Different AM processes, various material formulations, and strengths and drawbacks of AM methods are discussed. Emphasis is paid to AM techniques focusing on continuous fibers, as they hold the promise of becoming the next‐generation composite fabrication methodology. The article also tries to identify the potential of AM technology for fiber‐reinforced composites and delves into challenges facing the area.

Additive manufacturing (AM) or 3D printing is growing rapidly in the manufacturing industry and has gained a lot of attention from various fields owing to its ability to fabricate parts with complex features. The reliability of the 3D printed parts has been the focus of the researchers to realize AM as an endpart production tool. Machine Learning (ML) has been applied in various aspects of AM to improve the whole design and manufacturing workflow especially in the era of industrial revolution 4.0. In the review article, various types of ML techniques are first introduced. It is then followed by the discussion on their use in various aspects of AM such as design for 3D printing, material tuning, process optimization, in-situ monitoring, cloud service, and cybersecurity. Potential applications in the fields of biomedical, tissue engineering and building & constructions will be highlighted. The challenges faced by ML in AM such as computational cost, standards for qualification, and data acquisition techniques will also be discussed. In the authors' perspective, in-situ monitoring of AM processes will significantly benefit from the object detection ability of ML. As a large data set is crucial for ML, data sharing of AM would enable faster adoption of ML in AM. Standards for the shared data are needed to facilitate easy sharing of data. The use of ML in AM will become more mature and widely adopted as better data acquisition techniques and more powerful computer chips for ML are developed.

This article provides a database of the mechanical properties of additively manufactured polymeric materials fabricated using material extrusion (e.g., fused filament fabrication (FFF)).Mechanical properties available in the literatures are consolidated in table form for different polymeric materials for FFF. Mechanical properties such as tensile, compressive, flexural, fatigue and creep properties are discussed in detail. The effects of printing parameters such as raster angle, infill, and specimen orientation on properties are also provided, together with a discussion of the possible causes (e.g., texture, microstructure changes, and defects) of anisotropy in properties. In addition to that, research gaps are identified which warrant further investigation.

No abstract