Compared with CAD/CAM, fused deposition modeling (FDM) 3D printing technology is simple and safe to operate and has a low cost and high material utilization rate; thus, it is widely used. The present investigation aimed to evaluate the mechanical properties and fit of polyetheretherketone (PEEK) removable partial dentures (RPDs) constructed by FDM. We analyzed mechanical properties of PEEK samples prepared by FDM, milling, or injection molding. RPDs were designed and finite element analysis models was constructed to evaluate maximum stress and strain in the RPDs, cortical bone and mucosa. Geomagic Qualify software was used to analyze gaps between the model and the tissue surface of the framework. The results showed that the compressive strength of the 3D-printed PRDs was greater than that of the injection-molded samples. Finite element analysis demonstrated that the maximum stress on the PRDs was less than the yield strength of the material. Overall, the mechanical properties and fit of the PEEK RPD fabricated by FDM technology essentially fulfilled clinical requirements.
A viscoelastic sandwich structure is widely used in mechanical equipment, but therein viscoelastic layers inevitably suffer from aging which changes the dynamic characteristics of the structure and influences the whole performance of the equipment. Hence, accurate and automatic aging state recognition of the viscoelastic sandwich structure is very significant to monitor structural health state and guarantee equipment operating reliably. To fulfill this task, by analyzing the sensor-based vibration response signals, a novel aging state recognition approach of the viscoelastic sandwich structure based on permutation entropy of dual-tree complex wavelet packet transform and generalized Chebyshev support vector machine is proposed in this article. To extract effective aging feature information, the measured nonlinear and non-stationary vibration response signals are processed by dual-tree complex wavelet packet transform, and multiple permutation entropy features are extracted from the frequency-band signals to reflect structural aging states. For accurate and automatic aging state classification, generalized Chebyshev kernel is introduced, and multi-class generalized Chebyshev support vector machine is developed to classify structural aging states. In order to demonstrate the effectiveness of the proposed method, a typical viscoelastic sandwich structure is designed and fabricated, and various structural aging states are created through the hot oxygen–accelerated aging of viscoelastic layers. The testing results show that the proposed method can recognize the different structural aging states accurately and automatically. In addition, the superiority of dual-tree complex wavelet packet transform in processing vibration response signals and the performance of generalized Chebyshev support vector machine in classifying structural aging states are respectively validated by comparing with the commonly used methods.
materials, bio-medicals, mechanics, and chemistry. Recently, the most commercialized artificial bone, titanium alloy based artificial bone, has been widely used in spine surgery, dental surgery, and thoracic surgery. [2][3][4] However, artificial bone with better biocompatibility, mechanical strength, and superior radiography properties is still desired. [5] Polyether-ether-ketone (PEEK) based artificial bone attracted tremendous interests due to its similar elastic modulus compared to that of cortical bone, [6][7][8] compatibility to radioluscent and magnetic resonance imaging [5,9] and chemical stability. [9][10][11] Therefore, PEEK has been extensively investigated and successfully used in spinal fusion, trauma, neurosurgical and craniomaxillofacial procedures, dental operation, joint replacements, and anterior cruciate ligaments repair. [12,13] Combining with 3D printing (3DP) technology, skeletal and individual PEEK implants were made to repair the chest wall defect in our previous studies. [5,[14][15][16] Up to now, more than 100 3DP PEEK implants have been used in our hospital. Unfortunately, some side effects of PEEK implants have been gradually realized in several years after clinical practices. The most happened cases are unsatisfactory cellular response and poor integration among the implants and Poly-ether-ether-ketone (PEEK) implants with good mechanical properties and chemical inertia, meet the urgent needs of bone substitute. However, its inert interface leads to poor soft tissue integration, which prolongs healing time of surgical incision with many complications. Herein, (3-aminopropyl) triethoxysilane is connected to 3D printed (3DP) PEEK interface by chemical modification. The homogeneous amino groups on amidogen interface enhance PEEK's hydrophilicity and proteinophilia significantly. Fibroblasts cultured on the amidogen PEEK interface show much stronger potential of cell adhesion and migration. Furthermore, soft tissue ingrowth into 3DP PEEK scaffold occurs more and faster in the amidogen interface in vivo. The observation of the microstructure shows tighter implant-tissue bonding interfaces on the amidogen PEEK. To mimic real surgery, 3DP PEEK implants of the same proportions in clinical practice are used to reconstruct the chest wall defects of rabbits. A significant reduction in healing time and incision complications are observed in the amidogen PEEK groups. In addition, 19 related proteins are found in the fibroblasts cultured on the amidogen PEEK interface, which can be used to trace the biological mechanisms. In all, the facile amidogen bio-activation method can significantly boost the soft tissue integration on 3DP PEEK interface with less surgical complications.
Viscoelastic sandwich structure is playing an important role in mechanical equipment, but therein viscoelastic material inevitably suffers from aging which affects structural service performance and the whole performance of equipment. Therefore, the aging state detection of viscoelastic sandwich structure based on vibration response signal is essential for monitoring the health state of structure and guaranteeing the operation safety of equipment. However, the weakness of structural vibration response variation caused by material aging make this task challenging. In this paper, a novel method based on ensemble local mean decomposition (ELMD) and sensitive IA spectrum entropy is proposed for this task. As an adaptive nonlinear and non-stationary signal processing method, ELMD is introduced to decompose the structural vibration response signal, and a series of instantaneous amplitudes (IAs) are obtained. Then, the spectrum entropies of these IAs are developed to quantitatively assess the aging state of viscoelastic sandwich structure. However, the IA spectrum entropies have different sensitivities to the aging state. Therefore, the most sensitive IA spectrum entropy is selected with a distance evaluation technique to detect the aging state of viscoelastic sandwich structure. In order to demonstrate the effectiveness of the proposed method, the experimental device of a viscoelastic sandwich structure is designed, and different structural aging states are created through the accelerated aging of viscoelastic material. The results show the outstanding performance of the proposed method.
Three-dimensional printing (3DP) technology is suitable for manufacturing personalized orthopedic implants for reconstruction surgery. Compared with traditional titanium, polyether-ether-ketone (PEEK) is the ideal material for 3DP orthopedic implants due to its various advantages, including thermoplasticity, thermal stability, high chemical stability, and radiolucency suitable elastic modulus. However, it is challenging to develop a well-designed method and manufacturing technique to meet the clinical needs because it requires elaborate details and interplays with clinical work. Furthermore, establishing surgical standards for new implants requires many clinical cases and an accumulation of surgical experience. Thus, there are few case reports on using 3DP PEEK implants in clinical practice. Herein, we formed a team with a lot of engineers, scientists, and doctors and conducted a series of studies on the 3DP PEEK implants for chest wall reconstruction. First, the thoracic surgeons sort out the specific types of chest wall defects. Then, the engineers designed the shape of the implant and performed finite element analysis for every implant. To meet the clinical needs and mechanical requirements of implants, we developed a new fused deposition modeling technology to make personalized PEEK implants. Overall, the thoracic surgeons have used 114 personalized 3DP PEEK implants to reconstruct the chest wall defect and further established the surgical standards of the implants as part of the Chinese clinical guidelines. The surface modification technique and composite process are developed to overcome the new clinical problems of implant-related complications after surgery. Finally, the major challenges and possible solutions to translating 3DP PEEK implants into a mature and prevalent clinical product are discussed in the paper.
Viscoelastic sandwich structure plays an important role in mechanical equipment, nevertheless viscoelastic material inevitably suffers from gradual aging. For guaranteeing the operation safety of mechanical equipment, it is urgent to perform the aging state detection of viscoelastic sandwich structure with vibration response signal analysis. However, the structural vibration response signal is non-stationary and its variation caused by the structural aging state change is very puny, and the abnormal state samples is lacking. The vibration-based structural aging state detection has become a challenging task. Therefore, a novel method based on redundant second generation wavelet packet transform (RSGWPT) and fuzzy support vector data description (FSVDD) is proposed for this task. For extracting sensitive aging feature information, RSGWPT is introduced to process the structural vibration response signal, and multiple energy features are extracted from the frequency-band signals to reflect structural aging state change. For accurate and automatic aging state identification, by fusing fuzzy theory, FSVDD only uses the normal state samples for training and can identify the abnormal severity degrees is developed to identify the structural aging states. The proposed method is applied on a viscoelastic sandwich structure to validate its effectiveness, and different structural aging states are created through the accelerated aging of viscoelastic material. The analysis results show the outstanding performance of the proposed method.
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