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

AI-driven 3D bioprinting for regenerative medicine: From bench to bedside

In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting.

Intelligent Optimization Design Framework for Alternating Current Pulse Modulation Electrohydrodynamic Printing Process Parameters

To achieve efficient size tuning of printed microstructures on insulating substrates, an integrated process parameter intelligent optimization design framework for alternating current pulse modulation electrohydrodynamic (AC-EHD) printing is proposed for the first time. The framework is comprised of two stages: the construction of a prediction model and the acquisition of process parameters. The first stage employs the elk herd optimizer(EHO)-artificial neural network(ANN) to establish a mapping relationship between printing process parameters and the size of deposited droplets. The analysis of the prediction performance of the EHO-ANN model across various datasets reveals that the model exhibits commendable accuracy and robustness in predicting printed droplet size. In the second stage, the process parameters of AC-EHD printing are intelligently determined by utilizing the error between the model output and the desired droplet size as the fitness value for EHO. By comparing three sets of experimental cases with varying droplet sizes, it is observed that the actual printed droplet sizes closely align with the desired values, thus validating the effectiveness of this framework. The framework proposed in this paper mitigates the time and material wastage caused by adjusting AC-EHD printing process parameters on insulating substrates, thereby significantly enhancing the usability of the technology.

AI for biofabrication

Biofabrication is an advanced technology that holds great promise for constructing highly biomimeticin vitrothree-dimensional human organs. Such technology would help address the issues of immune rejection and organ donor shortage in organ transplantation, aiding doctors in formulating personalized treatments for clinical patients and replacing animal experiments. Biofabrication typically involves the interdisciplinary application of biology, materials science, mechanical engineering, and medicine to generate large amounts of data and correlations that require processing and analysis. Artificial intelligence (AI), with its excellent capabilities in big data processing and analysis, can play a crucial role in handling and processing interdisciplinary data and relationships and in better integrating and applying them in biofabrication. In recent years, the development of the semiconductor and integrated circuit industries has propelled the rapid advancement of computer processing power. An AI program can learn and iterate multiple times within a short period, thereby gaining strong automation capabilities for a specific research content or issue. To date, numerous AI programs have been applied to various processes around biofabrication, such as extracting biological information, designing and optimizing structures, intelligent cell sorting, optimizing biomaterials and processes, real-time monitoring and evaluation of models, accelerating the transformation and development of these technologies, and even changing traditional research patterns. This article reviews and summarizes the significant changes and advancements brought about by AI in biofabrication, and discusses its future application value and direction.

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.