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Internal defects commonly occur during the 3D printing process of Polylactic Acid (PLA), and significant challenges remain in detecting and extracting these defects, as well as understanding the relationship between defects and material fatigue life. This research proposes the Chroma-YOLO Enhanced Integrated Framework, an improved YOLOv11n-based model that integrates HSV defect extraction module and a random forest prediction model. Comprehensive ablation experiments demonstrate that the Chroma-YOLO model achieves significant improvements of 6.9% and 7.3% for mAP50 and mAP50-95 metrics, respectively, compared to the baseline YOLOv11n model, confirming substantial enhancements in feature extraction capability and target localization accuracy. Furthermore, this framework establishes a comprehensive model from defect detection to fatigue life prediction by combining the HSV color space-based defect detection technique with the random forest machine learning algorithm. The random forest-based predictive model achieves a remarkable accuracy of 96.25% and 99.09%for the test and validation set, respectively, for fatigue life prediction of 3D-printed PLA, which shows significant improvement compared to the conventional prediction methodologies.

Percutaneous coronary intervention (PCI) with stent implantation is one of the most effective treatments for cardiovascular diseases (CVDs). However, there are still many complications after stent implantation. As a medical device with a complex structure and small size, the manufacture and post-processing technology greatly impact the mechanical and medical performances of stents. In this paper, the development history, material, manufacturing method, and post-processing technology of vascular stents are introduced. In particular, this paper focuses on the existing manufacturing technology and post-processing technology of vascular stents and the impact of these technologies on stent performance is described and discussed. Moreover, the future development of vascular stent manufacturing technology will be prospected and proposed.

For data-driven intelligent manufacturing, many important in-process parameters should be estimated simultaneously to control the machining precision of the parts. However, as two of the most important in-process parameters, there is a lack of multi-task learning (MTL) model for simultaneous estimation of surface roughness and tool wear. To address the problem, a new MTL model with shared layers and two task-specific layers was proposed. A novel parallel-stacked auto-encoder (PSAE) network based on stacked denoising auto-encoder (SDAE) and stacked contractive auto-encoder (SCAE) was designed as the shared layers to learn deep features from cutting force signals. To enhance the performance of the MTL model, the scaled exponential linear unit (SELU) was introduced as the activation function of SDAE. Moreover, a dynamic weight averaging (DWA) strategy was implemented to dynamically adjust the learning rate of different tasks. Then, the time-domain features were extracted from raw cutting signals and low-frequency reconstructed wavelet packet coefficients. Frequency-domain features were extracted from the power spectrum obtained by the Fourier transform. After that, all features were combined as the input vectors of the proposed MTL model. Finally, surface roughness and tool wear were simultaneously predicted by the trained MTL model. To verify the superiority and effectiveness of the proposed MTL model, nickel-based superalloy Haynes 230 was machined under different cutting parameter combinations and tool wear levels. Some other intelligent algorithms were also implemented to predict surface roughness and tool wear. The results showed that compared with the support vector regression (SVR), kernel extreme learning machine (KELM), MTL with SDAE (MTL_SDAE), MTL with SCAE (MTL_SCAE), and single-task learning with PSAE (STL_PSAE), the estimation accuracy of surface roughness was improved by 30.82%, 16.67%, 14.06%, 26.17%, and 16.67%, respectively. Meanwhile, the prediction accuracy of tool wear was improved by 46.74%, 39.57%, 41.51%, 38.68%, and 39.57%, respectively. For practical engineering application, the dimensional deviation and surface quality of the machined parts can be controlled through the established MTL model.

Bioresorbable polymeric stents have attracted great interest for coronary artery disease because they can provide mechanical support first and then disappear within a desired time period. The conventional manufacturing process is laser cutting, and generally they are fabricated from tubular prototypes produced by injection molding or melt extrusion. The aim of this study is to fabricate and characterize a novel bioresorbable polymeric stent for treatment of coronary artery disease. Polycaprolactone (PCL) is investigated as suitable material for biomedical stents. A rotary 3D printing method is developed to fabricate the polymeric stents. Surface modification of polymeric stent is performed by immobilization of 2-N, 6-O-sulfated chitosan (26SCS). Physical and chemical characterization results showed that the surface microstructure of 3D-pinted PCL stents can be influenced by 26SCS modification, but no significant difference was observed for their mechanical behavior. Biocompatibility assessment results indicated that PCL and S-PCL stents possess good compatibility with blood and cells, and 26SCS modification can enhance cell proliferation. These results suggest that 3D printed PCL stent can be a potential candidate for coronary artery disease by modification of sulfated chitosan (CS).

Coronary artery disease (CAD) is the leading killer of humans worldwide. Bioresorbable polymeric stents have attracted a great deal of interest because they can treat CAD without producing long-term complications. Bioresorbable polymeric stents (BMSs) have undergone a sustainable revolution in terms of material processing, mechanical performance, biodegradability and manufacture techniques. Biodegradable polymers and copolymers have been widely studied as potential material candidates for bioresorbable stents. It is a great challenge to find a reasonable balance between the mechanical properties and degradation behavior of bioresorbable polymeric stents. Surface modification and drug-coating methods are generally used to improve biocompatibility and drug loading performance, which are decisive factors for the safety and efficacy of bioresorbable stents. Traditional stent manufacture techniques include etching, micro-electro discharge machining, electroforming, die-casting and laser cutting. The rapid development of 3D printing has brought continuous innovation and the wide application of biodegradable materials, which provides a novel technique for the additive manufacture of bioresorbable stents. This review aims to describe the problems regarding and the achievements of biodegradable stents from their birth to the present and discuss potential difficulties and challenges in the future.

Robotic milling offers significant advantages for machining large aerospace components due to its low cost and high flexibility. However, compared to computerized numerical control (CNC) machine tools, robot systems exhibit lower stiffness, leading to force-induced deformation during milling process that significantly compromises path accuracy. This study proposed a dynamic robot posture programming method to enhance the stiffness for aluminum alloy milling task. Firstly, a milling force prediction model is established and validated under multiple postures and various milling parameters, confirming its stability and reliability. Secondly, a robot stiffness model is developed by combining system stiffness and milling forces within the milling coordinate system to formulate an optimization index representing stiffness performance in the actual load direction. Finally, considering the constraints of joint limit, singular position and joint motion smoothness and so on, the robot posture in the milling trajectory is dynamically programmed, and the joint angle sequence with the optimal average stiffness from any cutter location (CL) point to the end of the trajectory is obtained. Under the assumption that positioning errors were effectively compensated, the experimental results demonstrated that the proposed method can control both axial and radial machining errors within 0.1 mm at discrete points. For the specific milling trajectory, compared to the single-step optimization algorithm starting from the initial optimal posture, the proposed method reduced the axial error by 12.23% and the radial error by 8.61%.

Surface quality has a significant impact on the service life of machine parts. Grinding is often the last process to ensure surface quality and accuracy of material formation. In this study, a high-quality surface was developed by determining the coefficient of friction in grinding a quartz fiber-reinforced silica ceramic composite. By processing the physical signals in the grinding process, a multi-objective function was established by considering grinding parameters, i.e., surface roughness, coefficient of friction, active energy consumption, and effective grinding time. The weight vector coefficients of the sub-objective functions were optimized through a multi-objective evolutionary algorithm based on the decomposition (MOEA/D) algorithm. The genetic algorithm was used to optimize the process parameters of the multi-objective function, and the optimal range for the coefficient of friction was determined to be 0.197~0.216. The experimental results indicated that when the coefficient of friction tends to 0.197, the distribution distance of the microscopic data points on the surface profile is small and the distribution uniformity is good. When the coefficient of friction tends to 0.216, the surface profile shows a good periodic characteristic. The quality of a grinding surface depends on the uniformity and periodicity of the surface’s topography. The coefficient of friction explained the typical physical characteristics of high-quality grinding surfaces. The multi-objective optimization function was even more important for the subsequent high-quality machining of mechanical parts to provide guidance and reference significance.

Ni-based superalloy GH4169 is widely demanded in the aerospace industry because of its excellent properties. However, the cutting of GH4169 at normal temperature has many challenges, such as tool wear, machining accuracy, and production efficiency. Cryogenic cutting has been an advanced method in assisting material removal machining. This paper focused on the cryogenic cutting performance of GH4169 at different initial temperatures, namely, 20 °C, −30 °C, −80 °C, and –130 °C. Firstly, the cryogenic mechanical properties of GH4169 were obtained by the Hopkinson pressure bar test at speed of 12 m/s and 18 m/s. The obtained data was used to analyze the cryogenic cutting performance of GH4169 at evaluated temperatures. The single factor milling experiments of GH4169 were carried out at room temperature and evaluated cryogenic levels, and the cutting performance in terms of cutting chips, cutting forces, and tool wear was investigated. The results showed that cryogenic cooling at −130 °C could increase the shear yield strength of the GH4169 by around 19.80% and the length of the cutting chip decreased monotonically by 53.45% compared with the length at room temperature. However, the cutting forces were not monotonically decreased. The cutting forces increased with the decrease of temperature when the initial temperature varied from 20 to −80 °C. However, when the initial temperature further dropped to –130 °C, the cutting forces were reduced by 30.60% for Fx , 24.02% for Fy , and 16.15% for Fz , respectively. Similarly, tool wear at the rake face and flank face is the most severe at –80 °C and the least at –130 °C. The average wear bandwidth at room temperature is 92.06 μm and decreases to 83.358 μm at –130 °C, which is reduced by 9.45%.

In order to promote the development and application of environment-friendly plastics, a glass fiber-modified starch/polylactic acid composite was prepared by the melt extrusion method. The influence of glass fiber content on the mechanical and thermal properties of the composite was studied, and multi-objective optimization of the injection molding process was carried out with consideration of the influence of material properties. The results show that with the increase of glass fiber content from 0 to 5%, the tensile strength of the composite decreases first and then increases to an average of 60Mpa, which is about 30% higher than that of pure polylactic acid. The elastic modulus and thermal stability increase, while the elongation at break and flow rate decrease with the increase of glass fiber content. In addition, the optimized injection parameters are obtained, which can effectively reduce the warping deformation, volume shrinkage, and residual stress of injection parts. Glass fiber modified starch/polylactic acid composites show great potential in engineering application, and may provide a reference for the development and application of high-performance and green degradable materials.

The machining process of aluminum alloy usually produces built-up edge and tool sticking problems due to their low hardness and large plastic deformation, which may further affect the machined surface quality and tool life. This paper aims to investigate the influence of different cutting fluids on the machined surface quality and tool life during the milling process of 7050 aluminum alloy. A novel cutting fluid (QC-2803) was considered in the study, which is synthesized by addition of alkyl alcohol amide and chlorinated polyolefin, and the traditional cutting fluid (CCF-10) was used as the control group. The physical and chemical properties of two cutting fluids were characterized. The milling process of 7050 aluminum alloy was carried out under two different cutting fluid conditions. The machined surface morphology, cutting force and tool wear morphology were observed during the process. Results show that the surface tension of the novel cutting fluid is significantly lower than that of the traditional cutting fluid, which makes it easier to produce a lubricating film between the aluminum alloy and tool, and further benefits the machined surface quality and tool life. As a result, the surface roughness and cutting force are reduced by ~20.0% and ~42.9%, respectively, and the tool life is increased by 25.6% in the case of the novel cutting fluid (QC-2803). The results in this paper revealed the important laws of cutting fluid with metal surface quality, cutting performance and tool wear, which helps to control the machined surface quality and tool life by the selection of cutting fluid during metal milling.