Machine vision assisted micro-filament detection for real-time monitoring of electrohydrodynamic inkjet printing

3D Bioprinting in Microgravity: Opportunities, Challenges, and Possible Applications in Space

3D bioprinting has developed tremendously in the last couple of years and enables the fabrication of simple, as well as complex, tissue models. The international space agencies have recognized the unique opportunities of these technologies for manufacturing cell and tissue models for basic research in space, in particular for investigating the effects of microgravity and cosmic radiation on different types of human tissues. In addition, bioprinting is capable of producing clinically applicable tissue grafts, and its implementation in space therefore can support the autonomous medical treatment options for astronauts in future long term and far-distant space missions. The article discusses opportunities but also challenges of operating different types of bioprinters under space conditions, mainly in microgravity. While some process steps, most of which involving the handling of liquids, are challenging under microgravity, this environment can help overcome problems such as cell sedimentation in low viscous bioinks. Hopefully, this publication will motivate more researchers to engage in the topic, with publicly available bioprinting opportunities becoming available at the International Space Station (ISS) in the imminent future.

Robust and High-Performance Machine Vision System for Automatic Quality Inspection in Assembly Processes

This paper addresses the problem of automatic quality inspection in assembly processes by discussing the design of a computer vision system realized by means of a heterogeneous multiprocessor system-on-chip. Such an approach was applied to a real catalytic converter assembly process, to detect planar, translational, and rotational shifts of the flanges welded on the central body. The manufacturing line imposed tight time and room constraints. The image processing method and the features extraction algorithm, based on a specific geometrical model, are described and validated. The algorithm was developed to be highly modular, thus suitable to be implemented by adopting a hardware–software co-design strategy. The most timing consuming computational steps were identified and then implemented by dedicated hardware accelerators. The entire system was implemented on a Xilinx Zynq heterogeneous system-on-chip by using a hardware–software (HW–SW) co-design approach. The system is able to detect planar and rotational shifts of welded flanges, with respect to the ideal positions, with a maximum error lower than one millimeter and one sexagesimal degree, respectively. Remarkably, the proposed HW–SW approach achieves a 23× speed-up compared to the pure software solution running on the Zynq embedded processing system. Therefore, it allows an in-line automatic quality inspection to be performed without affecting the production time of the existing manufacturing process.

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.

Instrument for fine control of drop-on-demand electrohydrodynamic jet printing by current measurement

In this work, an instrument of drop-on-demand electrohydrodynamic jet (DoD E-Jet) printing device equipped with a current measurement and control system was designed and developed for finely controlling the printing process. The relationships between the current and printing parameters of voltage, frequency, and flow rate were deeply investigated, and the examination data and conclusion were obtained under the condition of the needle size remaining unchanged. Especially, the equation relationship between the flow rate and current was established, which can be used for the modification of the DoD E-Jet printing process. The map describing the stable printing range, droplet size, and current was also recognized, which can help us to select parameters for stable printing. Based on the current measurement and control system and the established relationship, the optimized current and printing parameters were chosen to print uniform graphene microstructures. This instrument provides an effective method for monitoring, adjusting, and controlling the DoD E-Jet printing process and further improving the quality of the printed structures for micro/nanoelectromechanical system (M/NEMS) devices.