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As existing studies show the accuracy of sampling methods depends heavily on the evaluation metric in web accessibility evaluation, the purpose of this paper is to propose a sampling method OPS-WAQM optimized for Web Accessibility Quantitative Metric (WAQM). Furthermore, to support quick accessibility evaluation or real-time website accessibility monitoring, the authors also provide online extension for the sampling method.

In the OPS-WAQM method, the authors propose a minimal sampling error model for WAQM and use a greedy algorithm to approximately solve the optimization problem to determine the sample numbers in different layers. To make OPS-WAQM online, the authors apply the sampling in crawling strategy.

The sampling method OPS-WAQM and its online extension can both achieve good sampling quality by choosing the optimal sample numbers in different layers. Moreover, the online extension can also support quick accessibility evaluation by sampling and evaluating the pages in crawling.

To the best of the authors’ knowledge, the sampling method OPS-WAQM in this paper is the first attempt to optimize for a specific evaluation metric. Meanwhile, the online extension not only greatly reduces the serious I/O issues in existing web accessibility evaluation, but also supports quick web accessibility evaluation by sampling in crawling.

Social media data from financial websites contain information related to enterprise credit risk. Mining valuable new features in social media data helps to improve prediction performance. This paper proposes a credit risk prediction framework that integrates social media information to improve listed enterprise credit risk prediction in the supply chain.

The prediction framework includes four stages. First, social media information is obtained through web crawler technology. Second, text sentiment in social media information is mined through natural language processing. Third, text sentiment features are constructed. Finally, the new features are integrated with traditional features as input for models for credit risk prediction. This paper takes Chinese pharmaceutical enterprises as an example to test the prediction framework and obtain relevant management enlightenment.

The prediction framework can improve enterprise credit risk prediction performance. The prediction performance of text sentiment features in social media data is better than that of most traditional features. The time-weighted text sentiment feature has the best prediction performance in mining social media information.

The prediction framework is helpful for the credit decision-making of credit departments and the policy regulation of regulatory departments and is conducive to the sustainable development of enterprises.

The prediction framework can effectively mine social media information and obtain an excellent prediction effect of listed enterprise credit risk in the supply chain.

In this study, an indoor sensor information fusion positioning system of the quadrotor unmanned aerial vehicle (UAV) was investigated to solve the problem of unstable indoor flight positioning.

The presented system was built on Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU) and LiDAR-Lite devices. Based on this, one can obtain the aircraft's current attitude and the position vector relative to the target and control the attitudes and positions of the UAV to reach the specified target positions. While building a UAV positioning model relative to the target for indoor positioning scenarios under limited Global Navigation Satellite Systems (GNSS), the system detects the environment through the NVIDIA Jetson TX2 (Transmit Data) peripheral sensor, obtains the current attitude and the position vector of the UAV, packs the data in the format and delivers it to the flight controller. Then the flight controller controls the UAV by calculating the posture to reach the specified target position.

The authors used two systems in the experiment. The first is the proposed UAV, and the other is the Vicon system, our reference system for comparison purposes. Vicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.

Vicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.

The potential implications of the three-dimensional printing (3DP) technology are growing enormously in the various health-care sectors, including surgical planning, manufacturing of patient-specific implants and developing anatomical models. Although a wide range of thermoplastic polymers are available as 3DP feedstock, yet obtaining biocompatible and structurally integrated biomedical devices is still challenging owing to various technical issues.

Polyether ether ketone (PEEK) is an organic and biocompatible compound material that is recently being used to fabricate complex design geometries and patient-specific implants through 3DP. However, the thermal and rheological features of PEEK make it difficult to process through the 3DP technologies, for instance, fused filament fabrication. The present review paper presents a state-of-the-art literature review of the 3DP of PEEK for potential biomedical applications. In particular, a special emphasis has been given on the existing technical hurdles and possible technological and processing solutions for improving the printability of PEEK.

The reviewed literature highlighted that there exist numerous scientific and technical means which can be adopted for improving the quality features of the 3D-printed PEEK-based biomedical structures. The discussed technological innovations will help the 3DP system to enhance the layer adhesion strength, structural stability, as well as enable the printing of high-performance thermoplastics.

The content of the present manuscript will motivate young scholars and senior scientists to work in exploring high-performance thermoplastics for 3DP applications.

This paper aims to provide an overview of the recent findings of the mechanical properties of parts manufactured by fused deposition modeling (FDM). FDM has become a widely used technique for the manufacturing of thermoplastic parts. The mechanical performance of these parts under service conditions is difficult to predict due to the large number of process parameters involved. The review summarizes the current knowledge about the process-property relationships for FDM-based three-dimensional printing.

The review first discusses the effect of material selection, including pure thermoplastics and polymer-matrix composites. Second, process parameters such as nozzle temperature, raster orientation and infill ratio are discussed. Mechanisms that these parameters affect the specimen morphology are explained, and the effect of each parameter on the strength of printed parts are systematically presented.

Mechanical properties of FDM-produced parts strongly depend on process parameters and are usually lower than injection-molded counterparts. There is a need to understand the effect of each parameter and any synergistic effects involved better.

Through the optimization of process parameters, FDM has the potential to produce parts with strength values matching those produced by conventional methods. Further work in the field will make the FDM process more suitable for the manufacturing of load-bearing components.

This paper presents a critical assessment of the current knowledge about the mechanical properties of FDM-produced parts and suggests future research directions.

Fused filament fabrication (FFF) is a type of additive manufacturing (AM) based on materials extrusion. It is the most widely practiced AM route, especially used for polymer-based rapid prototyping and customized product fabrication in relation to aerospace, automotive, architecture, consumer goods and medical applications. During FFF, part quality (surface finish, dimensional accuracy and static mechanical strength) is greatly influenced by several process parameters. The paper aims to study FFF parametric influence on aforesaid part quality aspects. In addition, dynamic analysis of the FFF part is carried out.

Interpretive structural modelling is attempted to articulate interrelationships that exist amongst FFF parameters. Next, a few specimens are fabricated using acrylonitrile butadiene styrene plastic at varied build orientation and build style. Effects of build orientation and build style on part’s ultimate tensile strength, flexure strength along with width build time are studied. Prototype beams (of different thickness) are fabricated by varying build style. Instrumental impact hammer Modal analysis is performed on the cantilever beams (cantilever support) to obtain the natural frequencies (first mode). Parametric influence on natural frequencies is also studied.

Static mechanical properties (tensile and flexure strength) are greatly influenced by build style and build orientation. Natural frequency (NF) of prototype beams is highly influenced by the build style and beam thickness.

FFF built parts when subjected to application, may have to face a variety of external dynamic loads. If frequency of induced vibration (due to external force) matches with NF of the component part, resonance is incurred. To avoid occurrence of resonance, operational frequency (frequency of externally applied forces) must be lower/ higher than the NF. Because NF depends on mass and stiffness, and boundary conditions, FFF parts produced through varying build style may definitely correspond to varied NF. This aspect is explained in this work.

The fused filament fabrication (FFF) process is an additive manufacturing technique used in engineering design. The mechanical properties of parts manufactured by FFF are influenced by the printing parameters. The mechanical properties of rigid thermoplastics for FFF are well defined, while thermoplastic elastomers (TPE) are uncommonly investigated. The purpose of this paper is to investigate the influence of extruder temperature, bed temperature and printing speed on the mechanical properties of a thermoplastic elastomer.

Regression models predicting mechanical properties as a function of extruder temperature, bed temperature and printing speed were developed. Tensile specimens were tested according to ASTM D638. A 3×3 full factorial analysis, consisting of 81 experiments and 27 printing conditions was performed, and models were developed in Minitab. Tensile tests verifying the models were conducted at two selected printing conditions to assess predictive capability.

Each mechanical property was significantly affected by at least two of the investigated FFF parameters, where printing speed and extruder temperature terms influenced all mechanical properties (p < 0.05). Notably, tensile modulus could be increased by 21%, from 200 to 244 MPa. Verification prints exhibited properties within 10% of the predictions. Not all properties could be maximized together, emphasizing the importance of understanding FFF parameter effects on mechanical properties when making design decisions.

This work developed a model to assess FFF parameter influence on mechanical properties of a previously unstudied thermoplastic elastomer and made property predictions within 10% accuracy.

Polyetheretherketone (PEEK) is used to manufacture biomedical implants because it has a high strength-to-weight ratio and high strength and is biocompatible. However, the use of fused deposition modeling to print a PEEK results in low strength and crystallinity. This study aims to use the Taguchi method to optimize the printing factors to obtain the highest tensile strength of the printed PEEK object. The annealing effect on printed PEEK object and crystallinity are also investigated.

This study determines the printing factors including the printing speed, layer thickness, printing temperature and extrusion width. Taguchi experimental design with a L9 orthogonal array is used to print the tensile specimen and carried out the tensile test to compare the tensile strength and porosity. Analysis of variance (ANOVA) is used to determine the experimental error and to determine the optimization printing parameters to obtain the highest tensile strength. A multivariate linear regression analysis is used to obtain the linear regression equation for predicting the theoretical tensile strength. An X-ray analysis is achieved to evaluate the crystalline of printed object. The effect of annealing is investigated to improve the tensile strength of printed part. An intervertebral lumber device is printed to demonstrate the feasibility of the obtained optimization parameters for practical application.

Taguchi experiment designs nine sets of parameters to print the PEEK tensile specimen. The experimental results and the ANOVA present that the order in which the factors affect the tensile strength for printed PEEK parts is the layer thickness, the extrusion width, the printing speed and the printing temperature. The optimized printing parameters are a printing speed of 5 mm/s, a layer thickness of 0.1 mm, a printing temperature of 395 °C and an extrusion strand width of 0.44 mm. The average tensile strength of printed specimen with the optimized printing parameters is 91.48 MPa, which is slightly less than the theoretical predicted value of 94.34 MPa. After annealing, the tensile strength increases to 98.85 MPa, which is comparable to that for molded PEEK and the porosity decreases to 0.3 from 3.9%. X-ray diffraction results show that all printed and annealed specimens have a high degree of crystallinity. The printed intervertebral lumber device has ultimate compressive load of 13.42 kN.

The optimized printing parameters is suitable for low-price fused deposition modeling machine because it does not involve a table at high temperature and can print the PEEK object with high tensile strength and good crystalline. Annealing parameters offer a good solution for tensile strength improvement.

Material extrusion (MEX) is a class of additive manufacturing (AM) process based on MEX principle. In the viewpoint of Industry 4.0 and sustainable manufacturing, AM technologies are gaining importance than conventional manufacturing route (subtractive manufacturing). Because of the ease of use and lesser operation skills, MEX had wide popularity in industry for product and prototype development. This study aims to analyze energy consumption of MEX-based AM process and its influencing factors.

A group of factors were identified pertaining to MEX-based AM process. In this viewpoint, this study presents the configuration of a structural model using interpretive structural modeling (ISM) to depict dominant factors in MEX-based AM process. A total of 18 influencing factors are identified and ranked using ISM methodology for MEX process. The Impact Matrix Cross-reference Multiplication Applied to a Classification analysis was done to categorize influencing factors into four groups for MEX-based AM process.

The derivation of structural model would enable AM practitioners to systematically analyze the factors and to derive key factors which enable comprehensive energy modeling and energy assessment studies. Also, it facilitates the development of energy efficient AM system.

The development of structural model for analysis of factors influencing energy consumption of MEX-based AM is the original contribution of the authors.