Purpose In the past decade, three-dimensional (3D) printing has gained attention in areas such as medicine, engineering, manufacturing art and most recently in education. In biomedical, the development of a wide range of biomaterials has catalysed the considerable role of 3D printing (3DP), where it functions as synthetic frameworks in the form of scaffolds, constructs or matrices. The purpose of this paper is to present the state-of-the-art literature coverage of 3DP applications in tissue engineering (such as customized scaffoldings and organs, and regenerative medicine). Design/methodology/approach This review focusses on various 3DP techniques and biomaterials for tissue engineering (TE) applications. The literature reviewed in the manuscript has been collected from various journal search engines including Google Scholar, Research Gate, Academia, PubMed, Scopus, EMBASE, Cochrane Library and Web of Science. The keywords that have been selected for the searches were 3 D printing, tissue engineering, scaffoldings, organs, regenerative medicine, biomaterials, standards, applications and future directions. Further, the sub-classifications of the keyword, wherever possible, have been used as sectioned/sub-sectioned in the manuscript. Findings 3DP techniques have many applications in biomedical and TE (B-TE), as covered in the literature. Customized structures for B-TE applications are easy and cost-effective to manufacture through 3DP, whereas on many occasions, conventional technologies generally become incompatible. For this, this new class of manufacturing must be explored to further capabilities for many potential applications. Originality/value This review paper presents a comprehensive study of the various types of 3DP technologies in the light of their possible B-TE application as well as provides a future roadmap.
Control of a bioprocess is a challenging task mainly due to the nonlinearity of the process, the complex nature of microorganisms, and variations in critical parameters such as temperature, pH, and agitator speed. Generally, the optimum values chosen for critical parameters during Escherichia coli (E.coli) K-12fed-batch fermentation are37 ᵒC for temperature, 7 for pH, and 35 % for Dissolved Oxygen (DO). The objective of this research is to enhance biomass concentration while minimizing energy consumption. To achieve this, an Event-Triggered Control (ETC) scheme based on feedback-feed forward control is proposed. The ETC system dynamically adjusts the substrate feed rate in response to variations in critical parameters. We compare the performance of classical Proportional Integral (PI) controllers and advanced Model Predictive Control (MPC) controllers in terms of bioprocess yield. Initially, the data are collected from a laboratory-scaled 3L bioreactor setup under fed-batch operating conditions, and data-driven models are developed using system identification techniques. Then, classical Proportional Integral (PI) and advanced Model Predictive Control (MPC) based feedback controllers are developed for controlling the yield of bioprocess by manipulating substrate flow rate, and their performances are compared. PI and MPC-based Event Triggered Feed Forward Controllers are designed to increase the yield and to suppress the effect of known disturbances due to critical parameters. Whenever there is a variation in the value of a critical parameter, it is considered an event, and ETC initiates a control action by manipulating the substrate feed rate. PI and MPC-based ETC controllers are developed in simulation, and their closed-loop performances are compared. It is observed that the Integral Square Error (ISE) is notably minimized to 4.668 for MPC with disturbance and 4.742 for MPC with Feed Forward Control. Similarly, the Integral Absolute Error (IAE) reduces to 2.453 for MPC with disturbance and 0.8124 for MPC with Feed Forward Control. The simulation results reveal that the MPC-based ETC control scheme enhances the biomass yield by 7%, and this result is verified experimentally. This system dynamically adjusts the substrate feed rate in response to variations in critical parameters, which is a novel approach in the field of bioprocess control. Also, the proposed control schemes help reduce the frequency of communication between controller and actuator, which reduces power consumption. Control of a bioprocess is a challenging task mainly due to the nonlinearity of the process, the complex nature of microorganisms, and variations in critical parameters such as temperature, pH, and agitator speed. Generally, the optimum values chosen for critical parameters during Escherichia coli (E.coli) K-12fed-batch fermentation are37 ᵒC for temperature, 7 for pH, and 35 % for Dissolved Oxygen (DO). The objective of this research is to enhance biomass concentration while minimizing energy consumption. To achieve this, an Event-Triggered Control (ETC) scheme based on feedback-feed forward control is proposed. The ETC system dynamically adjusts the substrate feed rate in response to variations in critical parameters. We compare the performance of classical Proportional Integral (PI) controllers and advanced Model Predictive Control (MPC) controllers in terms of bioprocess yield. Initially, the data are collected from a laboratory-scaled 3L bioreactor setup under fed-batch operating conditions, and data-driven models are developed using system identification techniques. Then, classical Proportional Integral (PI) and advanced Model Predictive Control (MPC) based feedback controllers are developed for controlling the yield of bioprocess by manipulating substrate flow rate, and their performances are compared. PI and MPC-based Event Triggered Feed Forward Controllers are designed to increase the yield and to suppress the effect of known disturbances due to critical parameters. Whenever there is a variation in the value of a critical parameter, it is considered an event, and ETC initiates a control action by manipulating the substrate feed rate. PI and MPC-based ETC controllers are developed in simulation, and their closed-loop performances are compared. It is observed that the Integral Square Error (ISE) is notably minimized to 4.668 for MPC with disturbance and 4.742 for MPC with Feed Forward Control. Similarly, the Integral Absolute Error (IAE) reduces to 2.453 for MPC with disturbance and 0.8124 for MPC with Feed Forward Control. The simulation results reveal that the MPC-based ETC control scheme enhances the biomass yield by 7%, and this result is verified experimentally. This system dynamically adjusts the substrate feed rate in response to variations in critical parameters, which is a novel approach in the field of bioprocess control. Also, the proposed control schemes help reduce the frequency of communication between controller and actuator, which reduces power consumption.
Dry machining of Ti-6Al-4V alloy was investigated using SNMA120408 grade inserts. The material studied is designed for orthopedic applications. The effects of main cutting speed (VC), at constant feed rate (F) and depth of cut (DC) on machining characteristics (Feed force (Ff), radial force (Rf), Tangential Force (Tf)) and surface integrity (i.e., tool-chip contact length, chip segmentation, surface roughness, and tool wear) were examined. Experimental data indicate the cutting speed as the major parameter with direct impact on the machining characteristics. Increasing of the cutting speed promotes higher tangential forces that allow a decrease of the chip contact length; a smaller contact length results in a lower surface roughness and flank wear rate, respectively. To gain further insight from the simulated turning process an advanced Finite Element (FE) model was developed. The numerical model was built on the DEFORM-3D commercial software by incorporating the experimental cutting parameters. The numerical simulations results agree very well with experimental outputs in terms of cutting forces (FCS), tool-chip (T-C) contact length. Therefore, it was possible to estimate with accuracy the effective stress (σE) and the cutting temperature (TC). Further, due to its high robustness, the numerical model developed can be implemented in solving the industrial challenge (i.e., biomedical field) for predicting formations of serrated chip segment, chip thickness, potential types of chips, types of fracture mechanism and tool wear mechanism/rate generated during machining process.
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