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 end-part 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 industry 4.0. In this 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 biomedical, tissue engineering and building and construction 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.

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A review on machine learning in 3D printing: applications, potential, and challenges

https://repository.tudelft.nl/islandora/object/uuid%3A4afb3b29-9395-4fcd-bdc4-49d668c0b350/datastream/OBJ/download

An approach to develop printable strain hardening cementitious composites

https://par.nsf.gov/servlets/purl/10166329

3-D printing of concrete: Beyond horizons

Three-dimensional (3D) printing with foam concrete, which is known for its distinct physical and mechanical properties, has not yet been purposefully investigated. The article at hand presents a methodological approach for the mixture design of 3D-printable foam concretes and a systematic investigation of the potential application of this type of material in digital construction. Three different foam concrete compositions with water-to-binder ratios between 0.33-0.36 and densities of 1100 to 1580 kg/m³ in the fresh state were produced with a prefoaming technique using a protein-based foaming agent. Based on the fresh-state tests, including 3D printing as such, an optimum composition was identified, and its compressive and flexural strengths were characterized. The printable foam concrete showed low thermal conductivity and relatively high compressive strengths of above 10 MPa; therefore, it fulfilled the requirements for building materials used for load-bearing wall elements in multi-story houses. Thus, it is suitable for 3D-printing applications, while fulfilling both load-carrying and insulating functions.

https://www.mdpi.com/1996-1944/12/15/2433/pdf

Material Design and Performance Evaluation of Foam Concrete for Digital Fabrication

A systematic review and analysis of the viability of 3D-printed construction in remote environments

Research on small-scale 3D printing has rapidly evolved, where numerous industrial products have been tested and successfully applied. Nonetheless, research on large-scale 3D printing, directed to large-scale applications such as construction and automotive manufacturing, yet demands a great a great deal of efforts. Large-scale 3D printing is considered an interdisciplinary topic and requires establishing a blended knowledge base from numerous research fields including structural engineering, materials science, mechatronics, software engineering, artificial intelligence and architectural engineering. This review article summarizes key topics of relevance to new research trends on large-scale 3D printing, particularly pertaining (1) technological solutions of additive construction (i.e. the 3D printers themselves), (2) materials science challenges, and (3) new design opportunities.

https://iopscience.iop.org/article/10.1088/1757-899X/324/1/012088/pdf

Large-Scale 3D Printing: The Way Forward

The robotic gripping assist (“RGA”) provides a user with additional grip strength by supporting and forcefully pushing a user's fingers and hand to a gripping position. Motors are supported on a user's forearm and act as a source for the forced movement. A flexible member is worn on the back of a user's hand. The motors individually draw in or let out wires that cause the flexible member to move from a gripping to non-gripping position, and may pivot the flexible member laterally to provide for ulnar and radial wrist flexion. By bending the flexible member downward, through reeling in wires below the member, the attached fingers of the user are forced into a gripping position. Lateral movement is provided by reeling in the wires on the side of the intended bending direction.

Fady Al Najjar

Papers

Large-Scale 3D Printing: The Way Forward

Robotic gripping assist