In this review paper, the definition of the tissue engineering (TE) was comprehensively explored towards scaffold fabrication techniques and applications. Scaffold properties and features in TE, biological aspects, scaffold material composition, scaffold structural requirements, and old and current manufacturing technologies were reported and discussed. In almost all the reviewed reports, the TE definition denotes renewal, development, and repairs of damaged tissues caused by various factors such as disease, injury, or congenital disabilities. TE is multidisciplinary that combines biology, biochemistry, clinical medicine, and materials science whose application in cellular systems such as organ transplantation serves as a delivery vehicle for cells and drug. According to the previous literature and this review, the scaffold fabrication techniques can be classified into two main categories: conventional and modern techniques. These TE fabrication techniques are applied in the scaffold building which later on are used in tissue and organ structure. The benefits and drawbacks of each of the fabrication techniques have been described in conjunction with current areas of research devoted to deal with some of the challenges. To figure out, the highlighted aspects aimed to define the advancements and challenges that should be addressed in the scaffold design for tissue engineering. Additionally, this study provides an excellent review of original numerical approaches focused on mechanical characteristics that can be helpful in the scaffold design assessment in the analysis of scaffold parameters in tissue engineering.

A strategy for the preparation of bioactive poly-ether-ether-ketone/hydroxyapatite (PEEK/HA) composites was proposed in this study with the aim of controlling the biological and mechanical properties of different parts of the composites. The strategy integrated solvent-based extrusion freeforming 3D printing technology in order to print high-resolution HA scaffolds and compression molding processes for the production of bioactive PEEK/HA composites. To this end, an optimized model, established using response surface methodology, was employed to optimize the extrusion process parameters on the basis of accurate characterization of the extrusion pressure, and the effects of the filament/pore sizes on the PEEK infiltration depth into the HA scaffold were investigated. The results of scanning electron microscopy and computed tomography analyses revealed that the PEEK/HA composites exhibited a uniform microstructure and a good interface between the HA filaments and the PEEK matrix following the optimization of the process parameters. The HA scaffolds were fully infiltrated by PEEK in both vertical and lateral directions with an infiltration depth of 3 mm while maintaining the HA network structure and uniformity. The biological and mechanical performance test results validated that the PEEK/HA composites possessed excellent biocompatibility as well as yields and compressive strengths within the range of human cortical bone suitable for load-bearing applications.

This paper presents the influence of an inorganic Al₂O₃ layer over MoS₂ particles on the tribological performance of electroless Ni–P–MoS₂/Al₂O₃ composite coatings fabricated without using surfactants. The Al₂O₃-coated MoS₂ particles were prepared by a heterogeneous nucleation process. The dry sliding tests of the composite coatings were tested against a WC ball. SEM was used to observe the surface morphology of particles, composite coatings, and worn surfaces. The results indicate that the coverage of an Al₂O₃ coating on MoS₂ particles significantly affects the surface morphology, frictional coefficient and wear loss of the composite coatings. The incorporation of Al₂O₃-coated MoS₂ particles with lower coverage (up to 7% of Al₂O₃) could obtain compact surface structure of composite coatings, which contribute to reduced wear loss. However, higher coverage would lead to loose surface structure of the composite coatings, and thus increase their wear loss.

The traditional machine tool design method with metal materials makes large-scale moving structures very heavy, which seriously impacts dynamic performance and results in significant energy consumption. Using sandwich structures of composite materials to replace metal materials is an important strategy for lightweight large-scale moving structures. However, this kind of substitution is generally believed to be difficult because foam-filled sandwich structures usually show nonlinear characteristics and must balance the moving mass, material costs, and structural stiffness. In the present study, we proposed a design optimization approach for a large-scale moving framework in a large 5-axis machining center (L5AMC) considering large dimensions in the x, y, and z work space and high machining speed with the aim of minimizing the displacements of the milling head. An improved approach, named the 3-step design optimization, was executed to obtain the optimum framework structures to solve the contradiction between the moving mass, material costs, and structural stiffness. This approach was based on multi-objective optimization and finite element analysis. The structural stiffness of the framework after optimization increased by 89% compared with before optimization although the mass increased by 6% and the material costs increased by 9%. A finite element simulation under four given operational loads showed that the displacements of the milling head were all less than the design requirement of 0.25 mm. The results indicated that the proposed 3-step design optimization approach for the optimal design of a large-scale moving framework was feasible and successful. A 40 m × 6 m × 4 m L5AMC prototype was manufactured, and the actual verification results indicated that the large-scale moving framework fully met the design requirements of the L5AMC and reduced energy consumption.

The present study proposes a back propagation artificial neural network (BPANN) to provide improved precision for predicting the material removal rate (MRR) in ultrasonic machining. The BPANN benefits from the advantage of artificial neural networks (ANNs) in dealing with complex input-output relationships without explicit mathematical functions. In our previous study, a conventional linear regression model and improved nonlinear regression model were established for modelling the MRR in ultrasonic machining to reflect the influence of machining parameters on process response. In the present work, we quantitatively compare the prediction precision obtained by the previously proposed regression models and the presently proposed BPANN model. The results of detailed analyses indicate that the BPANN model provided the highest prediction precision of the three models considered. The present work makes a positive contribution to expanding the applications of ANNs and can be considered as a guide for modelling complex problems of general machining.