Over the past several decades, metal Additive Manufacturing (AM) has transitioned from a rapid prototyping method to a viable manufacturing tool. AM technologies can produce parts on-demand, repair damaged components, and provide an increased freedom of design not previously attainable by traditional manufacturing techniques. The increasing maturation of metal AM is attracting high-value industries to directly produce components for use in aerospace, automotive, biomedical, and energy fields. Two leading processes for metal part production are Powder Bed Fusion with laser beam (PBF-LB/M) and Directed Energy Deposition with laser beam (DED-LB/M). Despite the many advances made with these technologies, the highly dynamic nature of the process frequently results in the formation of defects. These technologies are also notoriously difficult to control, and the existing machines do not offer closed loop control. In the present work, the application of various Machine Learning (ML) approaches and in-situ monitoring technologies for the purpose of defect detection are reviewed. The potential of these methods for enabling process control implementation is discussed. We provide a critical review of trends in the usage of data structures and ML algorithms and compare the capabilities of different sensing technologies and their application to monitoring tasks in laser metal AM. The future direction of this field is then discussed, and recommendations for further research are provided. Graphical abstract

Fused filament fabrication (FFF) is the most widespread and versatile material extrusion (MEX) technique. Although powder-based systems have dominated the metal 3D printing landscape in the past, FFF’s popularity for producing metal parts (“metal FFF”) is growing. Metal FFF starts from a polymer–metal composite feedstock and proceeds through three primary stages, namely shaping (i.e., printing), debinding, and sintering. As critically discussed in the present review, the final quality of metal FFF parts is influenced by the characteristics of the composite feedstock, such as the metal loading, polymer backbone, and presence of additives, as well as by the processing conditions. The literature shows that a diverse array of metals, including steel, copper, titanium, aluminium, nickel, and their alloys, can be successfully used in metal FFF. However, the formulation of appropriate polymer binders represents a hurdle to the adoption of new material systems. Meanwhile, intricate geometries are difficult to fabricate due to FFF-related surface roughness and sintering-induced shrinkage. Nonetheless, the comparison of metal FFF with other common metal AM techniques conducted herein suggests that metal FFF represents a convenient option, especially for prototyping and small-scale production. Whilst providing insights into the functioning mechanisms of metal FFF, the present review offers valuable recommendations, facilitating the broader uptake of metal FFF across various industries.

Additive manufacturing (AM, aka 3D printing) is generally acknowledged as a “green” technology. However, its wider uptake in industry largely relies on the development of composite feedstock for imparting superior mechanical properties and bespoke functionality. Composite materials are especially needed in polymer AM, given the otherwise poor performance of most polymer parts in load-bearing applications. As a drawback, the shift from mono-material to composite feedstock may worsen the environmental footprint of polymer AM. This perspective aims to discuss this chasm between the advantage of embedding advanced functionality, and the disadvantage of causing harm to the environment. Fused filament fabrication (FFF, aka fused deposition modelling, FDM) is analysed here as a case study on account of its unparalleled popularity. FFF, which belongs to the material extrusion (MEX) family, is presently the most widespread polymer AM technique for industrial, educational, and recreational applications. On the one hand, the FFF of composite materials has already transitioned “from lab to fab” and finally to community, with far-reaching implications for its sustainability. On the other hand, feedstock materials for FFF are thermoplastic-based, and hence highly amenable to recycling. The literature shows that recycled thermoplastic materials such as poly(lactic acid) (PLA), acrylonitrile-butadiene-styrene (ABS), and polyethylene terephthalate (PET, or its glycol-modified form PETG) can be used for printing by FFF, and FFF printed objects can be recycled when they are at the end of life. Reinforcements/fillers can also be obtained from recycled materials, which may help valorise waste materials and by-products from a wide range of industries (for example, paper, food, furniture) and from agriculture. Increasing attention is being paid to the recovery of carbon fibres (for example, from aviation), and to the reuse of glass fibre-reinforced polymers (for example, from end-of-life wind turbines). Although technical challenges and economical constraints remain, the adoption of recycling strategies appears to be essential for limiting the environmental impact of composite feedstock in FFF by reducing the depletion of natural resources, cutting down the volume of waste materials, and mitigating the dependency on petrochemicals.