Additive manufacturing (AM) creates three-dimensional objects using various approaches, typically layer-by-layer. One emerging method is laser-based powder bed fusion of metals (PBF-LB/M), which uses high-energy lasers to melt metallic powder into shape. AM processes are influenced by many factors, yet there is no standardized framework for quantifying their effects on final products. This guide introduces key principles of experimental design and statistics, outlining a roadmap for conducting rigorous experiments. We review the literature on AM generally and PBF-LB/M specifically to assess how well current practices align with standardized methodologies. In addition, we compare the evolution of experimental techniques in PBF-LB/M to those in a more regulated industry to explore potential cross-pollination. Our analysis reveals that most studies do not adhere to best practices in experimental design and statistical analysis. For example, randomization of run order is rarely mentioned, and statistical model assumptions are often unchecked. Even in tightly regulated fields, experimental designs and statistical methods remain basic and lack sophistication. To improve research quality, we provide recommendations for establishing standardized experimental and reporting practices in AM.

As the field of three-dimensional (3D) bioprinting gains increased momentum, complex 3D bioprinters are being developed to keep up with the needs of biofabrication and tissue engineering. Cartesian-based linear 3D bioprinters have facilitated the fabrication of 3D biological constructs and scaffolds. However, to achieve meaningful advancement in biofabrication, 3D bioprinters need increased freedom of motion, seamless multi-material printing, full automation, and ease of use. In this paper, we propose TwinPrint, a dual-arm robotic 3D bioprinting system, designed to be compatible with soft bioinks to build multi-material constructs, crucial for creating functional tissue. The uniquely integrated robotic 3D bioprinter—comprising an in-house fabricated coaxial nozzle, two 4-axis robotic arms, six microfluidic pumps, and a software interface—work harmoniously as a single unit. We showcase the development of the Python-based software and Graphical User Interface, integrating all components into a single easy-to-use interface. Due to their human-like and instantaneous gelation properties, peptide-based bioinks were used as printing material to test the system. Developed in our laboratory as an alternative to gelatin- and alginate-based bioinks, they avoided chemical and ultraviolet-crosslinking by solidifying instantaneously under physiological conditions. For system performance testing, acellular and cellular constructs were observed for structural fidelity, multi-material layering, printing accuracy, cell viability, and proliferation. Overall, our proposed system showcases an innovative integration of robotics for biofabrication to expedite the printing process and enable multi-task print protocols. By saving time, increasing degrees of freedom, and expanding printing complexity, we believe TwinPrint is a promising next step for biofabrication.

This study aimed to determine the effects of how powder degrades in quality from use in the laser powder bed fusion process and investigate what changes in the powder cause defects in finished parts. It was determined that the reused powder affected the finished part quality, resulting in an increased number of lack-of-fusion pores. This was due to a change in the size distribution of the powder particles, characterized by an increase in larger sizes and a significant decrease in smaller sizes. There was an 11% increase in defective particles over the five prints that went through the sieving process, as well as an increase of ~2% of particles >63 μm, resulting in less powder that could be reused after each print. The results enabled the determination of the life of the powder due to the degradation of the powder from the differing property changes caused by the reuse of the powder.

This study aims to examine the effect of electroshock treatment (EST) on Ti-6Al-4V/Cu-Cr-Zr manufactured by laser melting deposition and explore the microstructure and mechanical properties to investigate the microstructure evolution of copper-based rail composite materials under high-energy-density currents. The results indicated that EST could promote atomic diffusion, enabling rapid preferential growth of TiCu in the metallurgical bonding zone. An increase of the current density promoted the nucleation of the primary Ti2Cu phase induced by the thermal effect of EST, which led to Cu solute enrichment and composition undercooling. Moreover, EST significantly improved nucleation rate and grain boundary migration. The average β grain size of the EST-1 sample increased from 2.83 μm to 3.62 μm, approximately, while the typical basic texture of EST-1 was enhanced. In EST-1, the shear strength of Ti-6Al-4V/Cu-Cr-Zr was 132 MPa, which was 65% higher than that of the original Ti-6Al-4V/Cu-Cr-Zr composite. The improvement in shear strength can be attributed to intergranular nano-precipitation and the improved wettability of Ti-6Al-4V/Cu-Cr-Zr. This work provides valuable insights into the preparation of high-value, high-performance Cu-based composites.

Additive manufacturing (AM) for biomedical metals presents revolutionary opportunities for producing personalized, complex structured biomedical components. However, the high nonlinearity and complexity of the manufacturing process pose significant challenges to the performance consistency of biomedical metals. Traditional trial-and-error approaches and experience-based optimization methods are increasingly inadequate for meeting the demands of high-reliability medical applications. In recent years, machine learning (ML) has emerged as a powerful data-driven tool, deeply integrating into every stage of AM for biomedical metals and providing a driving force for its intelligent transformation and upgrading. This review outlines three key applications of ML in biomedical metal AM: at the property prediction stage, ML enables forward prediction of performance characteristics by establishing precise mapping relationships between process parameters and macrostructure quality, microstructure, and mechanical/functional properties; at the process optimization level, ML-driven inverse optimization algorithms efficiently navigate high-dimensional parameter spaces to achieve both single-objective perfection and multi-objective balancing; at the quality monitoring and control level, ML enables real-time diagnosis of manufacturing defects and even closed-loop adaptive control by integrating multiple in situ sensor data. This review explores how ML can facilitate the biomedical metals during the AM process and outlines its future development toward fully integrated intelligent design and manufacturing processes.