Modeling of EHD inkjet printing performance using soft computing-based approaches

Enhancing quality control in bioprinting through machine learning

Bioprinting technologies have been extensively studied in literature to fabricate three-dimensional constructs for tissue engineering applications. However, very few examples are currently available on clinical trials using bioprinted products, due to a combination of technological challenges (i.e. difficulties in replicating the native tissue complexity, long printing times, limited choice of printable biomaterials) and regulatory barriers (i.e. no clear indication on the product classification in the current regulatory framework). In particular, quality control (QC) solutions are needed at different stages of the bioprinting workflow (including pre-process optimization, in-process monitoring, and post-process assessment) to guarantee a repeatable product which is functional and safe for the patient. In this context, machine learning (ML) algorithms can be envisioned as a promising solution for the automatization of the quality assessment, reducing the inter-batch variability and thus potentially accelerating the product clinical translation and commercialization. In this review, we comprehensively analyse the main solutions that are being developed in the bioprinting literature on QC enabled by ML, evaluating different models from a technical perspective, including the amount and type of data used, the algorithms, and performance measures. Finally, we give a perspective view on current challenges and future research directions on using these technologies to enhance the quality assessment in bioprinting.

Reviews on Machine Learning Approaches for Process Optimization in Noncontact Direct Ink Writing

Recently, machine learning has gained considerable attention in noncontact direct ink writing because of its novel process modeling and optimization techniques. Unlike conventional fabrication approaches, noncontact direct ink writing is an emerging 3D printing technology for directly fabricating low-cost and customized device applications. Despite possessing many advantages, the achieved electrical performance of produced microelectronics is still limited by the printing quality of the noncontact ink writing process. Therefore, there has been increasing interest in the machine learning for process optimization in the noncontact direct ink writing. Compared with traditional approaches, despite machine learning-based strategies having great potential for efficient process optimization, they are still limited to optimize a specific aspect of the printing process in the noncontact direct ink writing. Therefore, a systematic process optimization approach that integrates the advantages of state-of-the-art machine learning techniques is in demand to fully optimize the overall printing quality. In this paper, we systematically discuss the printing principles, key influencing factors, and main limitations of the noncontact direct ink writing technologies based on inkjet printing (IJP) and aerosol jet printing (AJP). The requirements for process optimization of the noncontact direct ink writing are classified into four main aspects. Then, traditional methods and the state-of-the-art machine learning-based strategies adopted in IJP and AJP for process optimization are reviewed and compared with pros and cons. Finally, to further develop a systematic machine learning approach for the process optimization, we highlight the major limitations, challenges, and future directions of the current machine learning applications.

Stereotactic technology for 3D bioprinting: from the perspective of robot mechanism

Three-dimensional (3D) bioprinting has been widely applied in the field of biomedical engineering because of its rapidly individualized fabrication and precisely geometric designability. The emerging demand for bioprinted tissues/organs with bio-inspired anisotropic property is stimulating new bioprinting strategies. Stereotactic bioprinting is regarded as a preferable strategy for this purpose, which can perform bioprinting at the target position from any desired orientation in 3D space. In this work, based on the motion characteristics analysis of the stacked bioprinting technologies, mechanism configurations and path planning methods for robotic stereotactic bioprinting were investigated and a prototype system based on the double parallelogram mechanism was introduced in detail. Moreover, the influence of the time dimension on stereotactic bioprinting was discussed. Finally, technical challenges and future trends of stereotactic bioprinting within the field of biomedical engineering were summarized.

Applications of 3D bioprinting in tissue engineering: advantages, deficiencies, improvements, and future perspectives

Over the past decade, 3D bioprinting technology has progressed tremendously in the field of tissue engineering in its ability to fabricate individualized biological constructs with precise geometric designability, which offers us the capability to bridge the divergence between engineered tissue constructs and natural tissues. In this work, we first review the current widely used 3D bioprinting approaches, cells, and materials. Next, the updated applications of this technique in tissue engineering, including bone tissue, cartilage tissue, vascular grafts, skin, neural tissue, heart tissue, liver tissue and lung tissue, are briefly introduced. Then, the prominent advantages of 3D bioprinting in tissue engineering are summarized in detail: rapidly prototyping the customized structure, delivering cell-laden materials with high precision in space, and engineering with a highly controllable microenvironment. The current technical deficiencies of 3D bioprinted constructs in terms of mechanical properties and cell behaviors are afterward illustrated, as well as corresponding improvements. Finally, we conclude with future perspectives about 3D bioprinting in tissue engineering.