Tissue engineering has played an important role in the field of regenerative medicine and biomedical engineering for many years. Through the targeted interaction of cells, suitable carrier materials, and growth stimuli, it is possible to produce a variety of tissue substitutes (Nerem and Sambanis, 2007) as well as in vitro models for preclinical studies (Godbey and Atala, 2002; Hirt et al., 2014). Anthony Atala, for example, succeeded in producing a human bladder and implanting it in a patient as early as 1999 (Atala et al., 2006). However, traditional tissue engineering methods often depend on the use of molds and cores and are thus restricted in their geometric freedom. Moreover, these methods are labor intensive and difficult to reproduce, which is not practical in the context of an economically scalable production, neither it is regarding standardization in terms of governmental regulation guidelines.

A promising technology to enable reproducibility and scalability while maintaining high quality and standardization is 3D-Bioprinting. In conventional additive manufacturing processes, 3D-Printing is used for rapid prototyping on the one hand and to move from mass production to mass customization on the other (Bak, 2003; Berman, 2012). In contrast, the use of 3D-Bioprinting in combination with further automation steps is intended to enable scalable and standardized production of printed tissue or microfluidic models to replace labor intensive handcraft. It also provides the capability to use digital models that can be easily adapted to individual needs for functional human tissue substitutes or replicas such as liver, skin or bones. 3D-Bioprinting covers a wide range of different processes and technologies that differ fundamentally in terms of the method of transferring bioink from the cartridge to a substrate or a previously printed layer. Besides procedural differences 3D-Bioprinting methods offer some common significant advantages. They allow the use of sensor-based control and regulation of the printing process and thus enable online quality monitoring, which offers great added value in terms of achieving reproducible printing results with a high shape-fidelity, printing resolution and cell viability. Sensor integration and online quality assessment (QA) has the potential to pave the way for standardized manufacturing platforms. In conjunction with robotic process automation, this could enable the industrially scalable production of clean meat or Organs-on-Chips (OoCs) through 3D-Bioprinting. In addition, real-time recording of sensor data and corresponding online quality control can provide a foundation for the certification of Advanced Therapy Medicinal Products (ATMPs), which is critical for the approval of clinical applications.

Bioprinting technology can be looked at from two different perspectives, a rather technical one or an economical one. The former is subject of current research and technical advantages are widely observed. The economic consideration, however, is often disregarded. Therefore, online QA and further automation, suitability for scalable production of printed tissue and standardization are often ignored. This paper places current automation solutions in the context of scalable biofabrication, considers transferable processes from other industries, and provides a perspective on the fully automated use of 3D-Bioprinting processes to move from handcraft to standardized production.

Increased automation of bioprinting processes, for example, enables online quality control and live adjustments, as well as scalability of the printing processes, leading to an improvement of the current state from both an economic and medical perspective. Thus, the following chapter presents an overview of the most common hardware components regarding already established bioprinting automation, future automation potentials and procedural interfaces for further automation as well as transferrable technologies from other industries.

To identify automation potentials and find currently applied automation solutions, hardware components and process steps need to be observed and analyzed. Due to the wide range of different systems and printing methods those differ. Yet there are fundamental similarities that can be schematically illustrated cumulatively (Figure 1).

In general, the 3D-Bioprinting process chain can be distinguished in print preparation, the printing procedure, and post-printing tissue maturation (Figure 1). Print preparation covers the entire path from 3D design, to the generation of a data set for path calculation of the subsequent print path, to material synthesis and cell cultivation as well as the final bioink formulation. In the post-printing phase, the printed 3D structure is typically cultivated in an incubator, conditioned and is then available for further use, e.g., for OoCs, implantation or further examination of the tissue for research issues. Within the printing process which comprises fluid handling, robotics and control engineering to transfer bioink from the cartridge to a substrate or previously printed layer, a preparatory phase, the actual printing phase and the post-processing of the printed object can be subdivided in terms of automation. All these three phases offer automation potentials, and due to smooth transitions between the phases, not all components can necessarily be assigned to one phase. After hardware initialization and typically a system-depend cleaning protocol, the preparatory phase starts. There, the previously designed 3D data and the bioink are loaded into the printing system and the print cartridge, respectively. One or more bioinks are then deposited layer by layer according to the predefined print path. The way in which the material is applied and the shaping of the 3D structure are highly dependent on the specified bioprinting method and strategy (Duarte Campos and Blaeser, 2021). The most frequently used technologies and strategies are either dependent on traditional deposition techniques (extrusion- or inkjet-based) or optics-/light-based (stereolithography or laser-assisted) (Choudhury et al., 2018). Independent of the system, online process monitoring and control as well as QA-methods are taking on an increasingly important role during the printing process. The use of Artificial Intelligence (AI) for camera-based detection and evaluation of anomalies (Jin et al., 2021), bio-chemical quality control (Tröndle et al., 2019) and a monitoring system with direct print path feedback and correction (Armstrong et al., 2019) are just a few examples. AI could help to combine and accel those technologies even further. The post-processing phase includes all steps that take place after printing and before cultivation in the incubator, e.g. robotic transportation of the printed object and intermediate storage in a magazine for OoC-applications. Further examples of the current state and future potentials regarding the print-process automation are presented and discussed in the following chapters.

In addition to the general view and the former illustration (Figure 1), it is useful to get an overview of the different printing methods to connect them with hardware, sensors and control components. Table 1 shows in a structured way which process steps, components as well as sensors and actuators are applicable for which 3D-bioprinting method with regard to process automation.

The assignment shown here is not complete due to clarity reasons, but reflects the most important elements and a large part of the sensors and actuators for 3D-Bioprinting. Components that appear repeatedly in different print elements are listed only once, including their associated sensors and actuators. This provides a reasonable reference in a clear framework to the topic of automation and automation potentials. In addition to the above, from a technological cell biological point of view, further consideration of biosensors would be interesting, for example, in order to be able to draw conclusions about cell expression and contraction as well as proliferation and metabolic activities. Equally, from a manufacturing perspective, control and regulation, further sensors, e.g. for recording micro-vibrations, or safety-relevant elements such as end stops can be considered.

3D-Bioprinting, whether in the field of new regenerative medical products as biomimetic and bionic cell-loaded implants, the development of clean meat or for drug discovery via the use of OoCs, has emerged as a novel innovative technology with great potential for the future. This is demonstrated both by the recent strong increase of interest from scientific institutions via rising publication numbers as well as exponential increasing patent registrations and industrial interest. Further research in the field of 3D-Bioprinting process automation, especially in the area of live quality monitoring, is essential to overcome technological and regulatory challenges. Progress from this and further developments in AI will be supported in the future by the smart combination and use of already known sensor and control technologies that can be burrowed from other sectors (e.g. food industry, conventional printing and additive manufacturing industry) to fully exploit the great potential of 3D-Bioprinting. Thus, the production of new regenerative medicinal products, clean meat and OoCs for animal-free drug development can be further advanced to make commercial biofabrication more realistic from an economic and scalability perspective.