3D Printing and Performance Study of Porous Artificial Bone Based on HA-ZrO2-PVA Composites.

An ideal artificial bone implant should have similar mechanical properties and biocompatibility to natural bone, as well as an internal structure that facilitates stomatal penetration. In this work, 3D printing was used to fabricate and investigate artificial bone composites based on HA-ZrO2-PVA. The composites were proportionally configured using zirconia (ZrO2), hydroxyapatite (HA) and polyvinyl alcohol (PVA), where the ZrO2 played a toughening role and PVA solution served as a binder. In order to obtain the optimal 3D printing process parameters for the composites, a theoretical model of the extrusion process of the composites was first established, followed by the optimization of various parameters including the spray head internal diameter, extrusion pressure, extrusion speed, and extrusion line width. The results showed that, at the optimum parameters of a spray head diameter of 0.2 mm, extrusion pressure values ranging from 1-3 bar, a line spacing of 0.8-1.5 mm, and a spray head displacement range of 8-10 mm/s, a better structure of biological bone scaffolds could be obtained. The mechanical tests performed on the scaffolds showed that the elastic modulus of the artificial bone scaffolds reached about 174 MPa, which fulfilled the biomechanical requirements of human bone. According to scanning electron microscope observation of the scaffold sample, the porosity of the scaffold sample was close to 65%, which can well promote the growth of chondrocytes and angiogenesis. In addition, c5.18 chondrocytes were used to verify the biocompatibility of the composite materials, and the cell proliferation was increased by 100% when compared with that of the control group. The results showed that the composite has good biocompatibility.

Effect of Drilling Parameters on Machining Performance in Drilling Polytetrafluoroethylene.

Polytetrafluoroethylene (PTFE) plays an important role in semiconductor manufacturing. It is an important processing material for the key sealing components in the field of immersion lithography. The lack of research related to the mechanical processing of PTFE leads to many challenges in producing complex parts. This paper conducted a drilling experiment on PTFE. The effect of cutting parameters on the drilling performance was investigated. Thrust, torque, surface roughness, and drilling temperature were used to evaluate the influence of cutting parameters on drilling performance. In addition, the empirical mathematical models of thrust and torque were developed using analysis of variance (ANOVA). The results indicated that the spindle speed had the most important effect on the thrust and the feed rate had the most significant effect on the torque. The lowest values of thrust and torque were, respectively, 22.64 N and 0.12 Nm, achieved in the case of spindle speed of 5000 rev/min, and feed rate of 50 mm/min. The surface quality is also best at this cutting parameter. Studies have shown that higher spindle speeds with lower feed rates are ideal parameters for improving the drilling performance and machining quality of PTFE. In addition, it was found that the temperature differences due to different drilling depths were related to chip accumulation. Surface roughness inconsistencies at various locations in the inner wall of the hole were influenced by chip adhesion during machining. This paper provides a suggestion for optimizing cutting parameters and hole quality.

Dynamic information of the time-dependent tobullian biomolecular structure using a high-accuracy size-dependent theory.

As the most rigid cytoskeletal filaments, tubulin-labeled microtubules bear compressive forces in living cells, balancing the tensile forces within the cytoskeleton to maintain the cell shape. The current structure is often under several environmental conditions as well as various dynamic or static loads that can decrease the stability of the viscoelastic tubulin-labeled microtubules. For this issue, the dynamic stability analysis of size-dependent viscoelastic tubulin-labeled microtubules using modified strain gradient theory by considering the exact three-length scale parameter. Viscoelastic properties are modeled using Kelvin-Voight model to study the time-dependent tubulin-labeled microtubules structure. By applying energy methods (known as Hamilton's principle), the motion equations of the tubulin-labeled microtubules are developed. The dynamic equations are based on first-order shear deformation theory (FSDT), and generalized differential quadrature and fourth-order Runge-Kutta methods are employed to find the model for the natural frequencies. The novelty of the current study is to consider the effects of viscoelastic properties, and exact values of size-dependent parameters on dynamic behaviors of the tubulin-labeled microtubules. Considering three-length scale parameters (l0 = h, l1 = h, l2 = h) in this size-dependent theory leads to a better agreement with molecular dynamic (MD) simulation in comparison with other theories. The results show that when the rigidity of the edges is improved by changing the simply supported to clamped supported boundary conditions, the maximum deflection and stability of the living part would be damped much more quickly.Communicated by Ramaswamy H. Sarma.

Experimental Investigation and Optimal Prediction of Maximum Forming Angle and Surface Roughness of an Al/SUS Bimetal Sheet in an Incremental Forming Process Using Machine Learning.

Bimetal sheets have superior properties as they combine different materials with different characteristics. Producing bimetal parts using a single-point incremental forming process (SPIF) has increased recently with the development of industrial requirements. Such types of sheets have multiple functions that are not applicable in the case of monolithic sheets. In this study, the correlation between the operating variables, the maximum forming angle, and the surface roughness is established based on the ensemble learning using gradient boosting regression tree (GBRT). In order to obtain the dataset for the machine learning, a series of experiments with continuous variable angle pyramid shape were carried out based on D-Optimal design. This design is created based on numerical variables (i.e., tool diameter, step size, and feed rate) and categorical variable (i.e., layer arrangement). The grid search cross-validation (CV) method was used to determine the optimum GBRT parameters prior to model training. After the parameter tuning and model selection, the model with a better generalization performance is obtained. The reliability of the predictive models is confirmed by the testing samples. Furthermore, the microstructure of the aluminum/stainless steel (Al/SUS) bimetal sheet is analyzed under different levels of operating parameters and layer arrangements. The microstructure results reveal that severe cracks are attained in the case of a small tool diameter while a clear refinement is observed when a high tool diameter value with small step down is used for both Al and SUS layers.

In Situ Controlled Surface Microstructure of 3D Printed Ti Alloy to Promote Its Osteointegration.

It is well known that three-dimensional (3D) printing is an emerging technology used to produce customized implants and surface characteristics of implants, strongly deciding their osseointegration ability. In this study, Ti alloy microspheres were printed under selected rational printing parameters in order to tailor the surface micro-characteristics of the printed implants during additive manufacturing by an in situ, controlled way. The laser path and hatching space were responsible for the appearance of the stripy structure (S), while the bulbous structure (B) and bulbous⁻stripy composite surface (BS) were determined by contour scanning. A nano-sized structure could be superposed by hydrothermal treatment. The cytocompatibility was evaluated by culturing Mouse calvaria-derived preosteoblastic cells (MC3T3-E1). The results showed that three typical microstructured surfaces, S, B, and BS, could be achieved by varying the 3D printing parameters. Moreover, the osteogenic differentiation potential of the S, B, and BS surfaces could be significantly enhanced, and the addition of nano-sized structures could be further improved. The BS surface with nano-sized structure demonstrated the optimum osteogenic differentiation potential. The present research demonstrated an in situ, controlled way to tailor and optimize the surface structures in micro-size during the 3D printing process for an implant with higher osseointegration ability.

Establishment and Verification of the Cutting Grinding Force Model for the Disc Wheel Based on Piezoelectric Sensors.

In this paper, a new model of cutting grinding force for disc wheels is presented. Initially, it was proposed that the grinding cutting force was formed by the grinding force and cutting force in combination. Considering the single-grit morphology, the single-grit average grinding depth, the effective number of grits, and the contact arc length between the grit and the workpiece comprehensively, the grinding force model and the cutting force model were established, respectively. Then, a universal grinding cutting force model was optimized by introducing the effective grit coefficient model, dependent on the probability statistical method and the grit height coefficient model with Rayleigh's distribution theory. Finally, according to the different proportions of the grinding force and cutting force, the grinding cutting force model, with multi-particles, was established. Simulation and experimental results based on piezoelectric sensors showed that the proposed model could predict the intermittent grinding cutting force well. Moreover, the inclusion of the grit height coefficient and the effective grits number coefficient improved the modeling accuracy. The error between the simulation and experimental findings in grinding cutting force was reduced to 7.8% in comparison with the traditional model. In addition, the grinding cutting force can be divided into three segments; increasing, steadiness, and decreasing, respectively found through modeling.