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The resection margin (RM) status and microscopic vascular invasion (MVI) are known prognostic factors for intrahepatic cholangiocarcinoma (ICC). An enhanced understanding of their impact on long-term prognosis is required to improve oncological outcomes.A total of 711 consecutive patients who underwent curative liver resection for hepatitis B virus-related ICC were retrospectively analyzed. The different impact of the RM status (narrow,1 cm, or wide, ≥1 cm) and MVI (positive, +, or negative, -) on overall survival (OS) and recurrence-free survival (RFS) were analyzed.The 1-, 3-, and 5-year OS rates were 67.6%, 42.5%, and 33.2% in wide RMMVI (-), 58.0%, 36.1%, and 26.5% in narrow RMMVI (-), 51.0%, 27.0%, and 24.3% in wide RMMVI (+), and 39.0%, 20.4% and 14.3% in narrow RMMVI (+) (Combined analysis of RM and MVI can better stratify the risks of postoperative death and recurrence in patients with HBV-related ICC, which may help subsequent adjuvant therapy and closer follow-up.

Bioprinting three-dimensional (3D) tissue equivalents have progressed tremendously over the last decade. 3D bioprinting is currently being employed to develop larger and more physiologic tissues, and it is of particular interest to generate vasculature in biofabricated tissues to aid better perfusion and transport of nutrition. Having an advantage over manual culture systems by bringing together biological scaffold materials and cells in precise 3D spatial orientation, bioprinting could assist in placing endothelial cells in specific spatial locations within a 3D matrix to promote vessel formation at these predefined areas. Hence, in the present study, we investigated the use of bioprinting to generate tissue-level capillary-like networks in biofabricated tissue constructs. First, we developed a bioink using collagen type-1 supplemented with xanthan gum (XG) as a thickening agent. Using a commercial extrusion-based multi-head bioprinter and collagen-XG bioink, the component cells were spatially assembled, wherein the endothelial cells were bioprinted in a lattice pattern and sandwiched between bioprinted fibroblasts layers. 3D bioprinted constructs thus generated were stable, and maintained structural shape and form. Post-print culture of the bioprinted tissues resulted in endothelial sprouting and formation of interconnected capillary-like networks within the lattice pattern and between the fibroblast layers. Bioprinter-assisted spatial placement of endothelial cells resulted in fabrication of patterned prevascularized constructs that enable potential regenerative applications in the future.

Modern manufacturing enterprises are consuming a considerable amount of energy every year. Improving energy efficiency will not only benefit the enterprises economically, but also help the world to overcome various problems such as energy crisis and air pollution. To achieve this, an accurate energy consumption model is essential. The main objective of this paper is to develop an improved cutting power-based energy consumption model for general end milling process. The proposed model consists of an idle part due to auxiliary components and spindle rotation, and an additional part due to cutting workpiece materials. The first part is modelled as a function of spindle rotation speed, and the other part is considered proportional to the cutting power. Experiments under various milling conditions have demonstrated the effectiveness and efficacy of the proposed model. Comparative studies show that the proposed model is more accurate than other models. Although calibrated from slotting experiments when cutting aluminium alloy, the proposed model is applicable for general milling process. Partial-immersion milling experiments show that the prediction error of the proposed model is as low as 1.74%. When workpiece material changes to titanium alloy, its performance remains decent, with low prediction error of 2.81%. This reveals its capability to provide reliable estimation for different workpiece materials. As such, it could help avoid tedious model calibration, thus saving time, material, and energy. Finally, the energy efficiency of general end milling process is investigated through numerical experiments with the proposed model. By revealing the relationship between energy consumption and various cutting parameters, the proposed model could serve as an excellent platform towards energy-efficient manufacturing/cleaner production.

Additively manufactured lattice structures enable the realisation of light-weight, multi-functional, structures. For example, lattices can be used for high stiffness and buckling resistance in sandwich structures or as support material for additive manufacturing. Topology optimisation and additive manufacturing are two technologies that allow the design, optimisation and manufacture of complex lattice designs. In this work, a new lattice optimisation methodology is presented that tailors the size, shape and orientation of individual lattice trusses in three-dimensional space by using principal strain fields obtained from topology optimisation. This new method of generating functionally graded lattices is shown both numerically and experimentally to be capable of generating lattice structures with greatly improved stiffness and strength when compared to lattice structures with a uniform lattice infill. Upper and lower relative density thresholds and minimum truss member sizes are included in the optimisation workflow to ensure that the optimised lattice designs are compatible with additive manufacturing process constraints. The functional grading method is also shown to be capable of generating conformal lattice structures in three dimensions, even for complex loading conditions and arbitrary volume boundaries.

3D printing and particularly fused deposition modelling (FDM) is widely used for prototyping and fabricating low-cost customised parts. However, present fused deposition modelling 3D printers have limited nozzle condition monitoring techniques to minimize nozzle clogging errors. Nozzle clogging is one of the significant process errors in fused deposition modelling 3D printers, and it affects the quality of prototyped parts in terms of mechanical properties and geometrical accuracy. This paper proposes a dynamic model for current-based nozzle condition monitoring in fused deposition modelling, which is briefly described as follows. First, all the process forces in filament extrusion of the fused deposition modelling were identified and derived theoretically, and theoretical equations of the feed rolling forces and flow-through-nozzle forces were derived. In addition, the effect of the nozzle clogging on the current of extruding motor were identified. Second, based on the proposed dynamic model, current-based nozzle condition monitoring method was proposed. Next, sets of experiments on FDM machine using polylactic acid (PLA) material were carried out to verify the proposed theoretical model, and the results were analysed and evaluated. Findings of the present study indicate that nozzle clogging in FDM 3D printing can be monitored by sensing the current of the filament extruding motor. The proposed model can be used efficiently for monitoring nozzle clogging conditions in fused deposition modelling 3D printers as it is based on the fundamental process modelling.

Drop-on-demand (DOD) bioprinting has been widely used in tissue engineering due to its high-throughput efficiency and cost effectiveness. However, this type of bioprinting involves challenges such as satellite generation, too-large droplet generation, and too-low droplet speed. These challenges reduce the stability and precision of DOD printing, disorder cell arrays, and hence generate further structural errors. In this paper, a multi-objective optimization (MOO) design method for DOD printing parameters through fully connected neural networks (FCNNs) is proposed in order to solve these challenges. The MOO problem comprises two objective functions: to develop the satellite formation model with FCNNs; and to decrease droplet diameter and increase droplet speed. A hybrid multi-subgradient descent bundle method with an adaptive learning rate algorithm (HMSGDBA), which combines the multi-subgradient descent bundle (MSGDB) method with Adam algorithm, is introduced in order to search for the Pareto-optimal set for the MOO problem. The superiority of HMSGDBA is demonstrated through comparative studies with the MSGDB method. The experimental results show that a single droplet can be printed stably and the droplet speed can be increased from 0.88 to 2.08 m·s−1 after optimization with the proposed method. The proposed method can improve both printing precision and stability, and is useful in realizing precise cell arrays and complex biological functions. Furthermore, it can be used to obtain guidelines for the setup of cell-printing experimental platforms. Keywords: Drop-on-demand printing, Inkjet printing, Gradient descent multi-objective optimization, Fully connected neural networks

Background Mesenchymal stem cells (MSCs) can migrate to tissue injury sites where they can induce multipotential differentiation and anti-inflammation effects to treat tissue injury. When traditional therapeutic methods do not work, MSCs are considered to be one of the best candidates for cell therapy. MSCs have been used for treating several injury- and inflammation-associated diseases, including liver cirrhosis. However, the therapeutic effect of MSCs is limited. In some cases, the anti-inflammatory function of naïve MSCs is not enough to rescue tissue injury. Methods Carbon tetrachloride (CCl4) was used to establish a mouse liver cirrhosis model. Enhanced green fluorescence protein (EGFP) and hepatocyte nuclear factor-4α (HNF-4α) overexpression adenoviruses were used to modify MSCs. Three weeks after liver injury induction, mice were injected with bone marrow MSCs via their tail vein. The mice were then sacrificed 3 weeks after MSC injection. Liver injury was evaluated by measuring glutamic-pyruvic transaminase (ALT) and glutamic oxalacetic transaminase (AST) levels. Histological and molecular evaluations were performed to study the mechanisms. Results We found that HNF-4α-overexpressing MSCs had a better treatment effect than unmodified MSCs on liver cirrhosis. In the CCl4-induced mouse liver injury model, we found that HNF-4α-MSCs reduced inflammation in the liver and alleviated liver injury. In addition, we found that HNF-4α promoted the anti-inflammatory effect of MSCs by enhancing nitric oxide synthase (iNOS) expression, which was dependent on the nuclear factor kappa B (NF-κB) signalling pathway. Conclusions MSCs overexpressing HNF-4α exerted good therapeutic effects against mouse liver cirrhosis due to an enhanced anti-inflammatory effect. Gene modification is likely a promising method for improving the effects of cell therapy. Electronic supplementary material The online version of this article (10.1186/s13287-019-1260-7) contains supplementary material, which is available to authorized users.

Specific structures of esophagus play an important role in specific functions. However, current esophageal tissue engineering scaffolds replicate them poorly. To address this issue, the objective of this study is to fabricate a hierarchical PCL/F127 scaffold inspired by the natural esophageal structure. The hierarchical scaffold consists of aligned fibers in micro-size and thin film with nano-sized pores through combining E-jetting and E-spraying. The aligned fibers of scaffold guide the orientation and induce the elongation of fibroblasts, mimicking the uniformly oriented esophageal muscles. Furthermore, the film functioned as a protective barrier to replicate the esophageal mucosa. Meanwhile, the film also increases cell adhesion area, hence, improving cell proliferation. These two features of the fabricated hierarchical scaffold mimic the structure of natural esophagus. Moreover, fabricated hierarchical scaffold possesses comparable mechanical properties to natural esophagus. These results prove the potential of fabricated scaffold for esophagus tissue repair.

Improving energy efficiency has been one of main objectives in modern manufacturing enterprises. Various approaches aiming at efficient energy management have been proposed/developed, among which minimizing energy consumption by energy-sensible production scheduling techniques has emerged as a promising one. However, reported workshop models are quite simple and unrealistic. This paper studies a more realistic workshop model called ultra-flexible job shop (uFJS). In an uFJS, the sequence among operations for a job can be changed within certain constraints. To formulate this energy-efficient scheduling problem, a mixed-integer linear programming model was developed. To deal with large-sized problems, a specially designed genetic algorithm (GA) was subsequently proposed and implemented. Numerical results showed the proposed GA worked with decent effectiveness and efficiency. Finally, several comparative studies are carried out to further demonstrate its efficacy in terms of energy efficiency improvement. The advantage of the uFJS as compared to other relative simple workshop models is also shown. By considering the flexibility in operation sequencing in each job, the uFJS effectively integrates process planning and scheduling in discrete parts manufacturing system, thus providing a much larger solution space for more energy-efficient solutions. It therefore provides an excellent platform for decision-makers when developing energy-efficient techniques and strategies

3D printing and particularly fused filament fabrication is widely used for prototyping and fabricating low-cost customized parts. However, current fused filament fabrication 3D printers have limited nozzle condition monitoring techniques to minimize nozzle clogging errors. Nozzle clogging is one of the most significant process errors in current fused filament fabrication 3D printers, and it affects the quality of the prototyped parts in terms of geometry tolerance, surface roughness, and mechanical properties. This paper proposes a nozzle condition monitoring technique in fused filament fabrication 3D printing using a vibration sensor, which is briefly described as follows. First, a bar mount that supports the liquefier in fused filament fabrication extruder was modeled as a beam excited by a system of process forces. The boundary conditions were identified, and the applied forces were analyzed for Direct and Bowden types of fused filament fabrication extruders. Second, a new 3D printer with a fixed extruder and a moving platform was designed and built for conducting nozzle condition monitoring experiments. Third, nozzle clogging was simulated by reducing the nozzle extrusion temperature, which caused partial solidification of the filament around inner walls of the nozzle. Fourth, sets of experiments were performed by measuring the vibrations of a bar mount during extrusion of polylactic acid, acrylonitrile butadiene styrene, and SemiFlex filaments via Direct and Bowden types of fused filament fabrication extruders. Findings of the current study show that nozzle clogging in fused filament fabrication 3D printers can be monitored using an accelerometer sensor by measuring extruder’s bar mount vibrations. The proposed technique can be used efficiently for monitoring nozzle clogging in fused filament fabrication 3D printers as it is based on the fundamental process modeling.

The incidence of peripheral nerve injuries is on the rise and the current gold standard for treatment of such injuries is nerve autografting. Given the severe limitations of nerve autografts which include donor site morbidity and limited supply, neural guide conduits (NGCs) are considered as an effective alternative treatment. Conductivity is a desired property of an ideal NGC. Reduced graphene oxide (rGO) possesses several advantages in addition to its conductive nature such as high surface area to volume ratio due to its nanostructure and has been explored for its use in tissue engineering. However, most of the works reported are on traditional 2D culture with a layer of rGO coating, while the native tissue microenvironment is three-dimensional. In this study, PCL/rGO scaffolds are fabricated using electrohydrodynamic jet (EHD-jet) 3D printing method as a proof of concept study. Mechanical and material characterization of the printed PCL/rGO scaffolds and PCL scaffolds was done. The addition of rGO results in softer scaffolds which is favorable for neural differentiation. In vitro neural differentiation studies using PC12 cells were also performed. Cell proliferation was higher in the PCL/rGO scaffolds than the PCL scaffolds. Reverse transcription polymerase chain reaction and immunocytochemistry results reveal that PCL/rGO scaffolds support neural differentiation of PC12 cells.

The rapid development of additive manufacturing technology provides a more flexible and efficient manufacturing solution for the high value-added parts with a complex topology. However, the poor surface finish poses a major obstacle for the wide application of the additively manufactured components when their functionality and tolerance are benchmarked with those produced by the conventional process. In selective laser melting, visible pattern and texture of the printed layers and unmolten particles cannot be avoided although research works have been conducted to improve the surface finish by optimising the selective laser melting strategy and process parameters. Therefore, a dedicated post-processing technique is generally required to improve the surface finish of the additively manufactured products in terms of various levels of geometric complexity. In this study, a vibration-assisted conformal polishing tool is developed to finish a representative v-groove structure fabricated by selective laser melting. Experiments are conducted to investigate the effects of abrasive size and polishing time on the improvement in surface roughness. The developed technique in this paper can be applied to finish the additively manufactured internal structures such as honeycomb structure and irregular holes that have linear projection along a single axis.

The design and fabrication of tissue engineering scaffolds is a highly complex process. In order to provide a proper architecture for cells to grow, proliferate, and differentiate to form tissues, scaffolds have to be made with suitable properties. However, the limited structural designs and conventional fabrication techniques severely cripple the improvement of scaffold properties. To overcome these limitations, many researchers have recently adopted computational methods combined with 3D printing techniques as a new approach for scaffold design and fabrication. This approach allows scaffolds to be designed and fabricated with highly complex microstructures and good control and accuracy. Previous works have also shown this approach to be a very useful tool to predict the scaffold properties and to optimize the scaffold designs with a great reduction of experimental iterations. As this approach combining computational methods and 3D printing techniques for scaffold design and fabrication has many advantages over the conventional trial-and-error based approach, it is imperative to provide a state-of-the-art review on the topic. To this end, this article reviews the various applications of computational methods in scaffold design and simulation; it also briefly reviews the application of 3D printing techniques to fabricate the computationally designed scaffolds. Finally, the limitations and future trends of this approach are discussed. Overall, this review will enable readers to understand the benefits of using computational methods coupled with 3D printing to design and fabricate scaffolds, and thus help researchers to improve and optimize the scaffold properties for future tissue engineering research. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 107B: 1329-1351, 2019.

Designing metallic cellular structures with triply periodic minimal surface (TPMS) sheet cores is a novel approach for lightweight and multi-functional structural applications. Different from current honeycombs and lattices, TPMS sheet structures are composed of continuous and smooth shells, allowing for large surface areas and continuous internal channels. In this paper, we investigate the mechanical properties and energy absorption abilities of three types of TPMS sheet structures (Primitive, Diamond, and Gyroid) fabricated by selective laser melting (SLM) with 316 L stainless steel under compression loading and classify their failure mechanisms and printing accuracy with the help of numerical analysis. Experimental results reveal the superior stiffness, plateau stress and energy absorption ability of TPMS sheet structures compared to body-centred cubic lattices, with Diamond-type sheet structures performing best. Nonlinear finite element simulation results also show that Diamond and Gyroid sheet structures display relatively uniform stress distributions across all lattice cells under compression, leading to stable collapse mechanisms and desired energy absorption performance. In contrast, Primitive-type structures display rapid diagonal shear band development followed by localized wall buckling. Lastly, an energy absorption diagram is developed to facilitate a systematic way to select optimal densities of TPMS structures for energy absorbing applications.

Background Patients with lung adenocarcinoma (LUAD) have high mortality rate and poor prognosis. The LUAD cells display increased aerobic glycolysis, which generates energy required for their survival and proliferation. Deregulation of Wnt/β-catenin signaling pathway induces the metabolism switching and oncogenesis in tumor cells. RING finger protein 115 (RNF115) is an E3 ligase for ubiquitin-mediated degradation. Although the oncogenic functions of RNF115 have been revealed in breast tumor cells, the effect of RNF115 on lung cancer is still not clear. Methods RNF115 expression and its correlation with the features of LUAD patients were analyzed by using public database and our own cohort. The functions of RNF115 in proliferation and energy metabolism in LUAD cells were explored by downregulating or upregulating RNF115 expression. Results We demonstrated that RNF115 was overexpressed in LUAD tissues and its expression was positively correlated with the poor overall survival of LUAD patients. Moreover, RNF115 overexpression inhibited LUAD cell apoptosis and promoted cellular proliferation and metabolism in LUAD cells. On the contrary, RNF115 knockdown displayed reverse effects. Furthermore, the underlying mechanism of the biological function of RNF115 in LUAD was through regulating Wnt/β-catenin pathway via ubiquitination of adenomatous polyposis coli (APC). Conclusion The current study reveals a close association between RNF115 expression and prognostic conditions in LUAD patients and the oncogenic roles of RNF115 in LUAD at the first time. These findings may help establish the foundation for the development of therapeutics strategies and clinical management for lung cancer in future.

Background and Aim: The resection margin (RM) status and microscopic vascular invasion (MVI) are known prognostic factors for intrahepatic cholangiocarcinoma (ICC). An enhanced understanding of their impact on long-term prognosis is required to improve oncological outcomes.Methods: We reviewed data on 711 consecutive patients who underwent curative liver resection for hepatitis B virus–related ICC. The different impact of the RM status (narrow, Results: The 1-, 3-, and 5-year OS rates were 67.6%, 42.5% and 33.2% in wide RM & & MVI (-), 58.0%, 36.1% and 26.5% in narrow RM & MVI (-), 51.0%, 27.0% and 24.3% in wide RM & MVI (+), and 39.0%, 20.4% and 14.3% in narrow RM & MVI (+) (p < 0.001).The 1-, 3-, and 5-year RFS rates were 60.0%, 40.2% and 28.7% in wide RM & MVI (-), 45.2%, 34.3% and 24.2% in narrow RM & MVI (-), 40.0%, 18.5% and 12.3% in wide RM & MVI (+), and 28.2%, 11.5% and 9.8% in narrow RM & MVI (+) (p < 0.001). Multivariate analysis showed that RM & MVI were independent risk factors for the OS and RFS. Conclusions: Combined analysis of RM and MVI can better stratify the risks of postoperative death and recurrence in patients with HBV-related ICC, which may help subsequent adjuvant therapy and follow-up.

For complex manufacturing process, the raw features are always in a heterogeneous manner that contains lots of high dimensional process parameters and categorical targets (e.g. product quality "Pass" or "Fail") without numerical measurements. It makes the classification problems quite challenging. To improve the classification accuracy, additional data structure is necessarily considered, e.g. feature interactions. Factorization Machines (FM) is a general classification method which considers the effect of feature interactions towards target. This paper discusses the classification performance of FM methods and its extensions to manufacturing processes and other industry fields. Real-world datasets are employed for the validation of classification performance. Experimental results show that FM-based approaches efficiently improve the classification accuracy about 2.49%-5.94% compared with conventional classification methods.

Earthworm-like soft robots have been widely studied for various applications, such as medical endoscopy and pipeline inspection. Many actuation modes have been chosen to drive the soft robots, including pneumatic actuators, dielectric elastomeric actuators, and shape memory actuators. Pneumatic actuators stand out since the soft robots with pneumatic actuation can produce relatively large forces and displacements with relatively ease of fabrication. Currently, several pneumatic actuators are used to realize elongating movement and anchoring movement of the earthworm for peristaltic motion. More pneumatic actuators not only require more pumps and valves to actuate and control the earthworm, but also lead to less efficient movement control of the earthworm. To address this issue, a new design with integrated single pneumatic actuator and cellular structures is developed to realize elongating movement and anchoring movement of the earthworm-like soft robot in peristaltic motion. With the new design, the simulation model of the new earthworm is developed to simulate both elongating and anchoring movements of the earthworm. A 3D printed prototype of the earthworm-like soft robot is fabricated to validate the proposed design and simulation model. Experimental results show good agreement with the simulation in elongations of peristaltic motion as the differences between the simulated and experimental is 5.8 % in one cycle of the peristaltic motion.

Among different treatments of critical-sized bone defects, bone tissue engineering (BTE) is a fast-developing strategy centering around the fabrication of scaffolds that can stimulate tissue regeneration and provide mechanical support at the same time. This area has seen an extensive application of bioceramics, such as calcium phosphate, for their bioactivity and resemblance to the composition of natural bones. Moreover, recent advances in additive manufacturing (AM) have unleashed enormous potential in the fabrication of BTE scaffolds with tailored porous structure as well as desired biological and mechanical properties. Robocasting is an AM technique that has been widely applied to fabricate calcium phosphate scaffolds, but most of these scaffolds do not meet the mechanical requirements for load-bearing BTE scaffolds. In light of this challenge, various approaches have been utilized to mechanically strengthen the scaffolds. In this review, the current state of knowledge and existing research on robocasting of calcium phosphate scaffolds are presented. Applying the Gibson-Ashby model, this review provides a meta-analysis from the published literature of the compressive strength of robocast calcium phosphate scaffolds. Furthermore, this review evaluates different approaches to the mechanical strengthening of robocast calcium phosphate scaffolds. The aim of this review is to provide insightful data and analysis for future research on mechanical strengthening of robocast calcium phosphate scaffolds and ultimately for their clinical applications.

Apparent anisotropies have been found in the microstructure and mechanical properties of selective laser melted (SLM) Ti–6Al–4V alloy, which will significantly influence the comprehensive performance of the SLM parts. In this study, the effect of scanning strategies on material anisotropy, including surface morphology, microstructure, microhardness, quasi-static and dynamic mechanical properties, were comprehensively investigated with 0°, 67.5°, and 90° scanning strategies. The SLM Ti6Al4V alloy prepared by the 0° scanning strategy exhibited the most pronounced anisotropy. The size of the primary columnar crystals on the front surface (158–173 μm) is approximately 1.8–3.2 times larger than that of the top surface (54–88 μm), and the microhardness at the top surface is ∼30% higher than that of the front surface. In addition, the modified Johnson-Cook constitutive model for the SLM Ti6Al4V alloy was developed based on the results of quasi-static compression and dynamic compression with the average absolute relative error controlled within 10%. This provides a reliable solution and an effective material constitutive model for modeling and simulation of the machining SLM parts with higher accuracy and enhanced physical meaning.

Overlapping melting trajectories and partially-melted powders result in poor surface morphology and high surface roughness values (Ra = ~13.34 μm) of selective laser melted (SLMed) Ti6Al4V alloy. Secondary processing of SLMed components is thus an essential finishing operation to produce functional SLMed parts in precision engineering. This paper investigates the effects of laser scanning strategies (0°, 67.5° and 90° laser scanning schemes) and machining surfaces (top and front surfaces) on the machining performance of SLMed Ti6Al4V alloy in milling, compared with that of annealed ASTM B265 Ti6Al4V alloy. High degree of anisotropy of SLMed Ti6Al4V alloy is reflected in cutting force, surface morphology and surface roughness on different machining surfaces. The machining anisotropy is dominated by the anisotropy of microstructure and mechanical properties of SLMed Ti6Al4V alloy, where the anisotropy weakens following the sequence of 0°, 90° and 67.5° SLMed samples. It is verified that high cutting speed can improve machining anisotropy features of SLMed titanium alloy. The chip shape of SLMed Ti6Al4V alloy is a typical conical spiral chip, and the bending degree and length of chips produced by SLMed Ti6Al4V alloy are larger than those produced by annealed Ti6Al4V alloy.

This paper aims to improve the surface quality of 316L stainless steel parts manufactured by selective laser melting (SLM) using dry mechanical-electrochemical polishing (DMECP). DMECP is an advanced surface finishing method combining the advantages of both mechanical and electrochemical polishing techniques in a more environmentally friendly manner. In this paper, the SLM process-related defects causing poor surface quality are analysed first. The material removal mechanism of DMECP is investigated to continuously remove the oxide layers formed during polishing. Surface morphology and roughness evolution under different polishing conditions are characterised. The top surface roughness can be reduced by over 91% from 8.72 μm to 0.75 μm compared to side surface by over 93% from 12.10 to 0.80 μm. The material removal on the top surface is more efficient than that on the side surface under the same polishing condition. The secondary defects formed during polishing can be removed using mechanical polishing mode. The chemical element composition of the polished surface exhibits almost identical content to the initial 316L powders. Compared with the initial dark and rough surfaces, the results validate the capability of DMECP as an effective tool to improve the SLM surface quality and achieve a mirror finish.

Stress shielding is one of the main problems that lead to bone resorption and revision surgery after implantation. Most of the commercially available metallic non-porous bone implants have a much greater stiffness than the native human bones and are prone to cause stress-shielding. With an open cell structure and intricate architecture, hyperbolic minimal surfaces offer several advantages such as less stress concentration, high permeability and high surface area to volume ratio, thus providing an ideal environment for cell adhesion, migration, and proliferation. This paper explores the use of porous bone implant design based on Triply Periodic Minimal Surfaces (TPMS) which is additively manufactured with ceramic material (Alumina) using Lithography-based Ceramics Manufacturing (LCM) technology. A total of 12 different primitive surface structure unit cells with pore size in the range of 500–1000 μm and porosity above 50% were considered. This is one of the earliest studies reporting the 3D printing of TPMS-based structures using ceramic material. Our results suggest that the choice of material and a porous TPMS-based design led to fabrication of structures with a much lesser compressive modulus comparable with the native bone and hence could potentially be adopted for bone implant design to mitigate the stress-shielding effect.

Biomimetic scaffold design is gaining attention in the field of tissue engineering lately. Recently, triply periodic minimal surfaces (TPMSs) have attracted the attention of tissue engineering scientists for fabrication of biomimetic porous scaffolds. TPMS scaffolds offer several advantages, which include a high surface area to volume ratio, less stress concentration, and increased permeability compared to the traditional lattice structures, thereby aiding in better cell adhesion, migration, and proliferation. In literature, several design methods for TPMS scaffolds have been developed, which considered some of the important tissue-specific requirements, such as porosity, Young's modulus, and pore size. However, only one of the requirements of a tissue engineering scaffold was investigated in these studies, and not all of the requirements were satisfied simultaneously. In this work, we develop a design method for TPMS sheet scaffolds, which is able to satisfy multiple requirements including the porosity, Young's modulus, and pore size, based on a parametric optimization approach. Three TPMSs, namely, the primitive (P), gyroid (G), and diamond (D) surfaces, with cubic symmetry are considered. The versatility of the proposed design method is demonstrated by three different applications, namely, tissue-specific scaffolds, scaffolds for stem cell differentiation, and functionally graded scaffolds with biomimetic functional gradients.

Biomimicry is a desirable quality of tissue engineering scaffolds. While most of the scaffolds reported in the literature contain a single pore size or porosity, the native biological tissues such as cartilage and skin have a layered architecture with zone-specific pore size and mechanical properties. Thus, there is a need for functionally graded scaffolds (FGS). EHD-jet 3D printing is a high-resolution process and a variety of polymer solutions can be processed into 3D porous scaffolds at ease, overcoming the limitations of other 3D printing methods (SLS, stereolithography, and FDM) in terms of resolution and limited material choice. In this paper, a novel proof of concept study on fabrication of porous polycaprolactone-based FGS by using EHD-jet 3D printing technology is presented. Organomorphic scaffolds, multiculture systems, interfacial tissue engineering, and in vitro cancer metastasis models are some of the futuristic applications of these polymeric FGS.

Lightweight lattice structures are an important class of cellular structures with high potentials for multi-functional applications. Considering load-bearing requirements, truss buckling is one of the main failure mechanisms for low density and slender lattice structures. Critical buckling loads can be increased by modifying the profile of a truss. In this paper, we present a shape design method to optimize the critical buckling loads for lattice cores with free-form trusses. The free-form truss is represented by Fourier series and implicit surfaces, having smooth truss diameter variations and truss joints. The optimized truss profile is obtained by solving a parametric shape optimization problem with Fourier series coefficients as design variables. The method is used for designing optimized 1D columns and 3D Kagome lattice cores for sandwich panels. The numerical results predict 26.8% and 20.4% improvements of the critical buckling loads for 1D columns and 3D Kagome lattice cores compared to their uniform counterparts of the same mass, respectively. The optimized structures include complex smooth and curved geometries that are well suited for additive manufacturing because of the greater design freedom. Finally, the initial and optimized lattice cores are additively manufactured and tested. The experimental results validate the effectiveness of the proposed method. Keywords: Additive manufacturing, Shape design, Buckling failure, Kagome lattice core, Free-form truss

For negative Poisson's ratio (NPR) cellular structures, most previous research focus on the design of unit cells, and then repeat the unit cell to construct uniform cellular structures. However, there is a disadvantage that these structures do not have much design freedom to achieve high-level functions, such as performing a desired deformation. As a solution, an evolutionary design method is proposed to develop nonuniform cellular structures. To conduct this method, the design domain is divided into finite unit cells with tunable Poisson's ratio (PR). With a given objective deformation, the value of each unit cell's PR is optimized using evolutionary algorithm (EA). In order to reduce the computational cost of the algorithm, discrete cosine transform (DCT) is applied to encode the structure for evolving. Considering the geometrical complexity of the optimized nonuniform cellular structures, additive manufacturing (AM) is chosen to build them physically. Both two-dimensional (2D) and three-dimensional (3D) design cases were developed and analyzed to validate the proposed method. The computational and experimental results showed good conformation with each other. Most importantly, this novel design method brings huge potential to NPR cellular structures with high-level functions and much wider applications. Keywords: Nonuniform cellular structure, Poisson's ratio, Evolutionary algorithm, Re-entrant structure, Additive manufacturing

Shape density-based approaches have been extensively studied for 3D model matching due to its simplicity and explicit geometry descriptions. While most studies are concerned with either the matching capabilities or the theoretical aspects of the algorithms, this study experimentally investigates the descriptors’ affine transformation invariance and noise robustness, as well as bin size effect. Six 3D density-based shape descriptors are derived and implemented. It has been found that the shape density descriptors depend on the bin size, all the descriptors are invariant to affine transformations, and all except Convex Hull are robust to noise.

Object proposal generation, as a preprocessing technique, has been widely used in current object detection pipelines to guide the search of objects and avoid exhaustive sliding window search across images. Current object proposals are mostly based on low-level image cues, such as edges and saliency. However, objectness is possibly a high-level semantic concept showing whether one region contains objects. This paper presents a framework utilizing fully convolutional networks (FCNs) to produce object proposal positions and bounding box location refinement with Support Vector Machine (SVM) to further improve proposal localization. Experiments on the PASCAL VOC 2007 show that using high-level semantic object proposals obtained by FCN, the object recall can be improved. An improvement in detection mean average precision is also seen when using our proposals in the Fast R-convolutional neural network framework. In addition, we also demonstrate that our method shows stronger robustness when introduced to image perturbations, e.g., blurring, JPEG compression, and salt and pepper noise. Finally, the generalization capability of our model (trained on the PASCAL VOC 2007) is evaluated and validated by testing on PASCAL VOC 2012 validation set, ILSVRC 2013 validation set, and MS COCO 2014 validation set.

Thin-walled tubes have long been used as energy absorbing structures, owing to their high specific energy absorption (SEA) capacity. But, they are associated with substantial high values of peak cr...

In the robotic machining process, the external force due to cutter-workpiece interactions and gravity force due to link weights can cause considerable deviations of the desired trajectory. Besides, gravity compensators can be found in many heavy-duty industrial robots to balance the gravity force. However, the effects of gravity force and balancing force are rarely considered in the deflection analysis. This paper proposes an effective method for the stiffness modeling of heavy-duty industrial robots by taking account of the joint/link compliances, link weights and gravity compensator. Firstly, the static equilibrium equations of each substructure are formulated to derive the expressions of reaction force exerted on each joint. Secondly, a linear map of linear/angular deflections between the joints/links and the end-effector is developed. Finally, the stiffness model of the whole robot is formulated by integrating the joint/link compliances. The robot deflections at several configurations are analyzed, and the deflection distributions throughout the workspace are demonstrated. With this model, it is possible to analytically evaluate the contributions of external force, link weights and gravity compensator to the total deflections. The correctness of the proposed model is also experimentally verified by comparing the calculated and the measured deflections under the same conditions.

Functionally graded lattice core structures show a gradual and localised variation in their mechanical properties with the aim of improving structural performance whilst minimising weight. We present a novel approach to generate optimised functionally graded lattice core structures. Firstly, topology optimisation is performed to return the optimal core density distribution to minimise the structure's compliance subject to a mass constraint. A series of isostatic lines are then constructed with respect to the local principal stresses to generate a lattice structure spatially graded with respect to lattice cell size, aspect ratio and orientation. To validate this novel approach, optimisation is performed on a sandwich core structure subject to three point bending. Experimental tests confirm the greatly improved stiffness and strength properties (101% and 172% respectively) of the core as a result of spatially grading the lattice cells when compared to a benchmark core with uniform cell size of the same density. The new approach also significantly outperforms lattice structures with graded diameters as optimised by state-of-the-art commercial software packages. Non-dimensional core performance indices are formulated to express the relative specific stiffness and strength properties of the core for the three design approaches. Keywords: Topology optimisation, Lattice structures, Finite element analysis, Functionally graded materials, Sandwich structures

Biocompatible tissue engineering (TE) scaffolds were used to closure the leakage area in gastro intestinal (GI) tract as patches; they could induce the regeneration of defected tissue and degraded steadily. However, some disadvantages, such as small and uncontrolled pore size, hinder the application of existed TE scaffolds. In this study, PCL/F127 composite scaffolds were fabricated through electrohydrodynamic jetting (E-jetting) technique. And the E-jetted scaffolds were cultured with esophageal fibroblast cells (the most important cell type in wound healing process) to evaluate their biocompatibility. The results show that E-jetted PCL/F127 scaffold has potential to be an alternative GI tract regeneration tool.

Surface roughness is often considered as one of the most important technical requirements for a machined part in production. Traditional selection of conservative process parameters neither guarantees part surface quality nor attains high metal removal rates. An accurate prediction and effective control of surface roughness is of vital importance for improving machining efficiency and reducing machining cost. In this paper, a novel model is presented for predicting surface roughness when slot milling Al-7075. The model is developed using a hybrid approach combining analytical calculation of specific cutting energy consumption (SCEC) and empirical characterization of the relationship between the surface roughness and SCEC. The proposed model has been validated by experiments under various cutting conditions. Compared to Taguchi methodology for predicting surface roughness, the proposed model is more accurate and reliable. From a novel viewpoint of SCEC, the model may provide valuable information regarding the effects of the cutting parameters on the surface roughness. Besides, the implementation of this model is straightforward, thus providing great potential for surface roughness control in real production.

Conductivity is a desirable property of an ideal nerve guide conduit (NGC) that is being considered for peripheral nerve regeneration. Most of the conductive polymers reported in use for fabrication of tissue engineering scaffolds such as polypyrrole (PPy), polyaniline, polythiophene, and poly(3,4-ethylenedioxythiophene) are non-biodegradable and possess weak mechanical properties to be fabricated into 3D structures. In this study, a biodegradable and conductive block copolymer of PPy and Polycaprolactone (PPy-b-PCL) was used to fabricate 3D porous NGCs using a novel electrohydrodynamic jet 3D printing process which offers superior control over fiber diameter, pore size, porosity, and fiber alignment. PCL/PPy scaffolds with three different concentrations of PPy-b-PCL (0.5, 1, and 2% v/v) were fabricated as a mesh (pore size 125 ± 15 μm) and the effect of incorporation of PPy-b-PCL on mechanical properties, biodegradability, and conductivity of the NGCs were studied. The mechanical properties of the scaffolds decreased with the addition of PPy-b-PCL which aided the ability to fabricate softer scaffolds that are closer to the properties of the native human peripheral nerve. With increasing concentrations of PPy-b-PCL, the scaffolds displayed a marked increase in conductivity (ranging from 0.28 to 1.15 mS/cm depending on concentration of PPy). Human embryonic stem cell-derived neural crest stem cells (hESC-NCSCs) were used to investigate the impact of PPy-b-PCL based conductive scaffolds on the growth and differentiation to peripheral neuronal cells. The hESC-NCSCs were able to attach and differentiate to peripheral neurons on PCL and PCL/PPy scaffolds, in particular the PCL/PPy (1% v/v) scaffolds supported higher growth of neural cells and a stronger maturation of hESC-NCSCs to peripheral neuronal cells. Overall, these results suggest that PPy-based conductive scaffolds have potential clinical value as cell-free or cell-laden NGCs for peripheral neuronal regeneration.

Bioprinting is increasingly being used for fabrication of engineered tissues for regenerative medicine, drug testing, and other biomedical applications. The success of this technology lies with the development of suitable bioinks and hydrogels that are specific to the intended tissue application. For applications such as neural tissue engineering, conductivity plays an important role in determining the neural differentiation and neural tissue regeneration. Although several conductive hydrogels based on metal nanoparticles (NPs) such as gold and silver, carbon-based materials such as graphene and carbon nanotubes and conducting polymers such as polypyrrole (PPy) and polyaniline were used, they possess several disadvantages. The long-term cytotoxicity of metal nanoparticles (NPs) and carbon-based materials restricts their use in regenerative medicine. The conductive polymers, on the other hand, are non-biodegradable and possess weak mechanical properties limiting their printability into three-dimensional constructs. The aim of this study is to develop a biodegradable, conductive, and printable hydrogel based on collagen and a block copolymer of PPy and polycaprolactone (PCL) (PPy-block-poly(caprolactone) [PPy-b-PCL]) for bioprinting of neural tissue constructs. The printability, including the influence of the printing speed and material flow rate on the printed fiber width; rheological properties; and cytotoxicity of these hydrogels were studied. The results prove that the collagen/PPy-b-PCL hydrogels possessed better printability and biocompatibility. Thus, the collagen/PPy-b-PCL hydrogels reported this study has the potential to be used in the bioprinting of neural tissue constructs for the repair of damaged neural tissues and drug testing or precision medicine applications.

Selecting the most relevant feature subset with good predictive performances and computational speed for high-dimensional datasets is usually challenging in manufacturing processes for product quality control. In this paper, an optimal feature subset determination approach for high-dimensional datasets is proposed. The algorithm starts with ranking the importance of individual features using the normalized mutual information concepts. Then, through optimizing a new evaluation metric, an initial relevant feature subset is obtained. Finally, redundant features are eliminated to help obtain the final selection results. The proposed method was successfully used in solving a real-world high-dimensional feature selection problem in the semiconductor industry. Comparisons were made with four other representative feature selection algorithms, including Relief-F, mRMR, FCBF, and IWFS in processing a number of datasets from different applications. It is demonstrated that the proposed method can automatically determine an optimal feature subset to achieve good average predictive accuracy with less computational resources.

Stainless steel 304L (SS304L) has been selected as the material for canisters for spent fuel storage from three nuclear power plants in Taiwan. A crucial issue is extending the spent fuel storage safety standards of the canisters. The anti-saline corrosion abilities of three thermal spray coatings (i.e., Al, ZnAl, and 625 Inconel alloys) on the SS304L were evaluated by immersion in 3.5 wt % aqueous NaCl and with 0.025 g/cm2 NaCl deposition at 80 &deg, C and 80% relative humidity (RH) for 1000 h. The pristine thermal spray coatings were examined using the pull-off adhesion test to understand the adhesion strength, and Vickers hardness was measured for the mechanical properties of the three coatings. Confocal laser scanning microscopy (CLSM) was used to identify the porosities of the coatings. Furthermore, the surfaces of the specimens before and after corrosion were investigated using scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy dispersive spectroscopy (EDS). The composition and distribution of the oxide layers formed on the coating surfaces during corrosion were evaluated. Electrochemical measurement was also performed with the polarization method to quantify the corrosion property of the three thermal spray coatings. The results showed that the corrosion rate of Al coating was lowest from the Tafel analysis after the 1000 h corrosion test in 3.5% aqueous NaCl. In contrast, the corrosion rate of Inconel 625 was lowest after 1000 h of the NaCl deposition corrosion test in a controlled environment. Therefore, the ZnAl thermal spray coating is a potential protection layer, keeping in mind economic considerations, of SS304L for anti-corrosion in saline environments.

Metallic lattice structures are well known for having high specific elastic moduli and strength. However, very little is understood about their resistance to fracture. In this work Ti–6Al–4V lattice structures are additively manufactured by selective laser melting and their fracture toughness characteristics are investigated. Resistance to fracture was determined under Mode-I loading at static rates using an extended compact tension (EC(T)) specimen, modified to contain lattice cells. The lattices consist of octet cells with a 3.5 mm edge length and relative densities ranging from 25% to 56%. Toughness is shown to increase by a power law with relative density and this trend was also obtained with finite element models. A new functional grading optimisation methodology is also presented for increasing fracture toughness. The size optimisation results in a functionally graded lattice whereby lattice truss diameters become the design variables. After size optimisation, initiation fracture toughness increases by up to 37%.

Selective laser melting processes deposit and join metal powders to near net shape in a layer-by-layer manner. The process of melting and re-solidification of several layers of deposited material can result in geometric deviations, and the impact is particularly significant for sub-millimetre structures oriented at a wide range of overhang angles with respect to the building platform. This paper assesses and benchmarks the capabilities of a neural network-based geometric compensation approach for truss lattice structures with circular cross-sections. The neural network method is capable to generate free-form cross-sections with enhanced geometric freedom for compensation compared to more established analytical compensation approaches limited to predefined geometric shapes. For neural network training, lattice dome structures composed of trusses with different overhang angles were designed and printed by selective laser melting and measured via X-ray computed tomography, resulting in point cloud data sets containing more than 20,000 data points for each overhang angle. For experimental validation, neural network-compensated dome structures were benchmarked against dome structures with elliptical parameter compensation. Results show that the neural network compensated lattice trusses achieve higher printing dimensional accuracy compared to the uncompensated structures and those compensated based on elliptical parameter estimates.