In this study, we present the energy absorption capabilities achieved through the application of hybrid lattice structures, emphasizing their potential across various industrial sectors. Utilizing Ti-6Al-4V and powder bed fusion (PBF) techniques, we fabricated distinct octet truss, diamond, and diagonal lattice structures, tailoring each to specific densities such as 10, 30, and 50%. Furthermore, through the innovative layering of diverse lattice types, we introduced hybrid lattice structures that effectively overcome the inherent energy absorption limitations of single-lattice structures. As a result, we conducted a comprehensive comparison between single-lattice structures and hybrid lattice structures of equal density, unequivocally showcasing the latter's superior energy absorption performance in terms of compression. The single-lattice structure, OT, showed an energy absorption of 42.6 J/m3, while the reinforced hybrid lattice structure, OT-DM, represented an energy absorption of 77.8 J/m3. These findings demonstrate the significant potential of hybrid lattice structures, particularly in energy-intensive domains such as shock absorption structures. By adeptly integrating various lattice architectures and leveraging their collective energy dissipation properties, hybrid lattice structures offer a promising avenue for addressing energy absorption challenges across diverse industrial applications.
Fused filament fabrication (FFF) is increasingly adopted for direct manufacturing of end use parts in an aviation industry. However, the application of FFF technique is still restricted to manufacturing low criticality lightly loaded parts, due to poor mechanical performance. To alleviate the mechanical performance issue, thermal annealing process is frequently utilized. However, problems such as distortion issues and the need for jigs and fixtures limit the effectiveness of the thermal annealing process, especially for low volume complex FFF parts. In this research, a novel low temperature thermal annealing is proposed to address the limitations in conventional annealing. A modified orthogonal array design is applied to investigate the performance of ULTEM™ 9085 FFF coupons. Further, the coupons are annealed with specialized support structures, which are co-printed with the coupons during the manufacturing process. Once the annealing process is completed, multiscale characterizations are performed to identify the mechanical properties of the specimens. Geometrical measurement of post annealed specimens indicates an expansion in the layering direction, which indicates relief of thermal stresses. Moreover, annealed coupons show an improvement in tensile strength and reduction in strain concentration. Mesostructure and fracture surface analysis indicate an increase in ductility and enhanced coalescence. This research shows that the proposed annealing methodology can be applied to enhance the mechanical performance of FFF parts without significant distortion.
Recently, machine learning has gained considerable attention in noncontact direct ink writing because of its novel process modeling and optimization techniques. Unlike conventional fabrication approaches, noncontact direct ink writing is an emerging 3D printing technology for directly fabricating low-cost and customized device applications. Despite possessing many advantages, the achieved electrical performance of produced microelectronics is still limited by the printing quality of the noncontact ink writing process. Therefore, there has been increasing interest in the machine learning for process optimization in the noncontact direct ink writing. Compared with traditional approaches, despite machine learning-based strategies having great potential for efficient process optimization, they are still limited to optimize a specific aspect of the printing process in the noncontact direct ink writing. Therefore, a systematic process optimization approach that integrates the advantages of state-of-the-art machine learning techniques is in demand to fully optimize the overall printing quality. In this paper, we systematically discuss the printing principles, key influencing factors, and main limitations of the noncontact direct ink writing technologies based on inkjet printing (IJP) and aerosol jet printing (AJP). The requirements for process optimization of the noncontact direct ink writing are classified into four main aspects. Then, traditional methods and the state-of-the-art machine learning-based strategies adopted in IJP and AJP for process optimization are reviewed and compared with pros and cons. Finally, to further develop a systematic machine learning approach for the process optimization, we highlight the major limitations, challenges, and future directions of the current machine learning applications.
BACKGROUND: Autoimmune hepatitis can cause liver fibrosis, liver cirrhosis, and hepatocellular carcinoma. Its treatment option include the use of steroids and/or immune-suppressive agents such as azathioprine. However, these drugs have some side effects. Thus, close follow-up is needed during treatment. Here, we present an extremely rare case of a patient with an autoimmune hepatitis who died from necrotizing gastritis during immunosuppressive treatment. CASE SUMMARY: A 52-year-old female patient was diagnosed with autoimmune hepatitis. We treated this patient with immunosuppressive agents. High-dose steroid treatment was initially started. Then azathioprine treatment was added while steroid was tapering. Five weeks after the start of treatment, she visited the emergency room due to generalized abdominal pain and vomiting. After computed tomography scan, the patient was diagnosed with necrotizing gastritis and the patient progressed to septic shock. Treatment for sepsis was continued in the intensive care unit. However, the patient died at 6 h after admission to the emergency room. CONCLUSION: In patients with autoimmune infections undergoing immunosuppressant therapy, rare complications such as necrotizing gastritis may occur, thus requiring clinical attention.
In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients with Atrial Fibrillation (AF) have a fivefold increased stroke risk. Early diagnosis, which leads to adequate AF treatment, can decrease the stroke risk by 66% and thereby prevent stroke. The monitoring service is based on Heart Rate (HR) measurements. The resulting signals are communicated and stored with Internet of Things (IoT) technology. A Deep Learning (DL) algorithm automatically estimates the AF probability. Based on this technology, we can offer four distinct services to healthcare providers: (1) universal access to patient data; (2) automated AF detection and alarm; (3) physician support; and (4) feedback channels. These four services create an environment where physicians can work symbiotically with machine algorithms to establish and communicate a high quality AF diagnosis.
Atrial Fibrillation , Stroke , Algorithms , Atrial Fibrillation/diagnosis , Heart Rate , Humans , Monitoring, Physiologic , Stroke/prevention & control
Aerosol jet printing of electronic devices is increasingly attracting interest in recent years. However, low capability and high resistance are still limitations of the printed electronic devices. In this paper, we introduce a novel post-treatment method to achieve a high-performance electric circuit. The electric circuit was printed with aerosol jet printing method on an ULTEM substrate. The ULTEM substrate was fabricated by the Fused Deposition Modelling method. After post-treatment, the electrical resistance of the printed electric circuit was changed from 236 mΩ to 47 mΩ and the electric property was enhanced. It was found that the reduction of electric resistance was caused by surface property changes. Different surface analysis methods including scanning electron microscopy (SEM) and x-ray photoelectron spectroscopy (XPS) were used to understand the effectiveness of the proposed method. The results showed that the microsurface structure remained the same original structure before and after treatment. It was found that the surface carbon concentration was significantly increased after treatment. Detailed analysis showed that the C-C bond increased obviously after treatment. The change of electrical resistance was found to be limited to the material's surface. After polishing, the circuit resistance was changed back to its original value. As the electric circuit is the basic element of electric devices, the proposed method enables the fabrication of high performance devices such as capacitors, strain gauge, and other sensors, which has potential applications in many areas such as industrial, aerospace, and military usage.
Aerosol jet printing (AJP) is a three-dimensional (3D) noncontact and direct printing technology for fabricating customized microelectronic devices on flexible substrates. Despite the capability of fine feature deposition, the complicated relationship between the main process parameters will affect the printing quality significantly in a design space. In this paper, a novel hybrid machine learning method is proposed to determine the optimal operating process window for the AJP process in various design spaces. The proposed method consists of classic machine learning methods, including experimental sampling, data clustering, classification, and knowledge transfer. In the proposed method, a two-dimensional design space is fully explored by a Latin hypercube sampling experimental design at a certain print speed. Then, the influence of the sheath gas flow rate (SHGFR) and the carrier gas flow rate (CGFR) on the printed line quality is analyzed by a K-means clustering approach, and an optimal operating process window is determined by a support vector machine. To efficiently identify more operating process windows at different print speeds, a transfer learning approach is applied to exploit relatedness between different operating process windows. Hence, at a new print speed, the number of line samples for identifying a new operating process window is greatly reduced. Finally, to balance the complex relationship among SHGFR, CGFR, and print speed, a 3D operating process window is determined by an incremental classification approach. Different from experiment-based approaches adopted in 3D printing technologies for quality optimization, the proposed method is developed based on the theory of knowledge discovery and data mining. Therefore, the knowledge in different design spaces can be fully explored and transferred for printed line quality optimization. Moreover, the data-driven-based characteristics can help the proposed method develop a guideline for quality optimization in other 3D printing technologies.
Additive manufacturing (AM) has become more prominent in leading industries. Recently, there have been intense efforts to achieve a fully functional 3D structural electronic device by integrating conductive structures into AM parts. Here, we introduce a simple approach to creating a conductive layer on a polymer AM part by CO2 laser processing. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Raman spectroscopy were employed to analyze laser-induced modifications in surface morphology and surface chemistry. The results suggest that conductive porous graphene was obtained from the AM-produced carbon precursor after the CO2 laser scanning. At a laser power of 4.5 W, the lowest sheet resistance of 15.9 Ω/sq was obtained, indicating the excellent electrical conductivity of the laser-induced graphene (LIG). The conductive graphene on the AM parts could serve as an electrical interconnection and shows a potential for the manufacturing of electronics components. An interdigital electrode capacitor was written on the AM parts to demonstrate the capability of LIG. Cyclic voltammetry, galvanostatic charge-discharge, and cyclability testing demonstrated good electrochemical performance of the LIG capacitor. These findings may create opportunities for the integration of laser direct writing electronic and additive manufacturing.
With the recent expansion of additive manufacturing (AM) in industries, there is an intense need to improve the surface quality of AM parts. A functional surface with extreme wettability would explore the application of AM in medical implants and microfluid. In this research, we propose to superimpose the femtosecond (fs) laser induced period surface structures (LIPSS) in the nanoscale onto AM part surfaces with the micro structures that are fabricated in the AM process. A hierarchical structure that has a similar morphology to a lotus leaf surface is obtained by combining the advantages of liquid assisting fs laser processing and AM. A water contact angle (WCA) of 150° is suggested so that a super hydrophobic surface is achieved. The scanning electron microscopy (SEM) images and X-ray photoelectron spectroscopy (XPS) analysis indicate that both hierarchical structures and higher carbon content in the laser processed area are responsible for the super hydrophobicity.
Aortic Dissection , Coronary Vessel Anomalies , Myocardial Infarction , Aged , Aortic Dissection/diagnosis , Aortic Dissection/therapy , Coronary Angiography , Coronary Vessel Anomalies/diagnosis , Coronary Vessel Anomalies/therapy , Female , Humans , Male , Myocardial Infarction/diagnosis , Percutaneous Coronary Intervention , Vascular Diseases
This study presents usability considerations and solutions for the design of glasses-type wearable computer displays and examines their effectiveness in a case study. Design countermeasures were investigated by a four-step design process: (1) preliminary design analysis; (2) design idea generation; (3) final design selection; and (4) virtual fitting trial. Three design interventions were devised from the design process: (1) weight balance to reduce pressure concentrated on the nose, (2) compliant temples to accommodate diverse head sizes and (3) a hanger mechanism to help spectacle users hang their wearable display on their eye glasses. To investigate their effectiveness, in the case study, the novel 3D glasses adopting the three interventions were compared with two existing 3D glasses in terms of neck muscle fatigue and subjective discomfort rating. While neck muscle fatigue was not significantly different among the three glasses (p = 0.467), the novel glasses had significantly smaller discomfort ratings (p = 0.009). Relevance to Industry: A four-step design process identified usability considerations and solutions for the design of glasses-type wearable computer displays. A novel 3D glasses was proposed through the process and its effectiveness was validated. The results identify design considerations and opportunities relevant to the emerging wearable display industry.
Computers , Eyeglasses , Muscle Fatigue , Neck Pain/etiology , Wearable Electronic Devices , Adult , Analysis of Variance , Electromyography , Equipment Design , Ergonomics , Female , Humans , Imaging, Three-Dimensional , Male , Students , Universities , User-Computer Interface , Young Adult
Recently, some smartphones have introduced index finger interaction functions on the rear surface. The current study investigated the effects of task type, phone width, and hand length on grasp, index finger reach zone, discomfort, and muscle activation during such interaction. We considered five interaction tasks (neutral, comfortable, maximum, vertical, and horizontal strokes), two device widths (60 and 90 mm) and three hand lengths. Horizontal (vertical) strokes deviated from the horizontal axis in the range from -10.8° to -13.5° (81.6-88.4°). Maximum strokes appeared to be excessive as these caused 43.8% greater discomfort than did neutral strokes. The 90-mm width also appeared to be excessive as it resulted in 12.3% increased discomfort relative to the 60-mm width. The small-hand group reported 11.9-18.2% higher discomfort ratings, and the percent maximum voluntary exertion of their flexor digitorum superficialis muscle, pertaining to index finger flexion, was also 6.4% higher. These findings should be considered to make smartphone rear interaction more comfortable. Practitioner Summary: Among neutral, comfortable, maximum, horizontal, and vertical index finger strokes on smartphone rear surfaces, maximum vs. neutral strokes caused 43.8% greater discomfort. Horizontal (vertical) strokes deviated from the horizontal (vertical) axis. Discomfort increased by 12.3% with 90-mm- vs. 60-mm-wide devices. Rear interaction regions of five commercialised smartphones should be lowered 20 to 30 mm for more comfortable rear interaction.
Fingers , Hand Strength , Muscle, Skeletal , Range of Motion, Articular , Smartphone , Biomechanical Phenomena , Female , Hand , Humans , Male , Young Adult
Amyloidosis is defined as the presence of extra-cellular deposits of an insoluble fibrillar protein, amyloid. The pulmonary involvement of amyloidosis is usually classified as tracheobronchial, parenchymal nodular, or diffuse alveolar septal. A single nodular lesion can mimic various conditions, including malignancy, pulmonary tuberculosis, and fungal infection. To date, only one case of nodular pulmonary amyloidosis has been reported in Korea, a case involving multiple nodular lesions. Here, we report and discuss the case of a patient having single nodular amyloidosis.
Various α-Fe2 O3 hollow structures, such as wormlike shapes, ellipsoids, and quasicubes, were synthesized successfully by a halide-ion-assisted solvothermal method. The self-assembly assisted by selective absorption of halide ions and Ostwald ripening speeded up by acidic etching commonly determine the final unique structures. The electrochemical performance of the α-Fe2 O3 with different structures in reversible lithium-ion storage was investigated, and showed that the hollow Fe2 O3 quasicubes exhibit the best cycling performance with a charge capacity of 401.6â mAh g-1 after 100â cycles at 0.2â C. The superior cycling stability and high specific capacity can be ascribed to the higher specific surface area, lower charge-transfer resistance, and adequate alleviation of volume variation.
This paper presents a novel method to calculate magnetic inductance with a fast-computing magnetic field model referred to as the extended distributed multi-pole (eDMP) model. The concept of mutual inductance has been widely applied for position/orientation tracking systems and applications, yet it is still challenging due to the high demands in robust modeling and efficient computation in real-time applications. Recently, numerical methods have been utilized in design and analysis of magnetic fields, but this often requires heavy computation and its accuracy relies on geometric modeling and meshing that limit its usage. On the other hand, an analytical method provides simple and fast-computing solutions but is also flawed due to its difficulties in handling realistic and complex geometries such as complicated designs and boundary conditions, etc. In this paper, the extended distributed multi-pole model (eDMP) is developed to characterize a time-varying magnetic field based on an existing DMP model analyzing static magnetic fields. The method has been further exploited to compute the mutual inductance between coils at arbitrary locations and orientations. Simulation and experimental results of various configurations of the coils are presented. Comparison with the previously published data shows not only good performance in accuracy, but also effectiveness in computation.
Catheterization, Peripheral/adverse effects , Catheters/adverse effects , Foreign Bodies/etiology , Radial Artery , Aged , Anesthesia, General , Appendectomy , Device Removal , Equipment Failure , Foreign Bodies/diagnostic imaging , Foreign Bodies/surgery , Humans , Laparoscopy , Male , Tomography, X-Ray Computed , Ultrasonography
