Engineering reports : open access最新文献

This review paper explores the diverse applications of polylactide or polylactic acid (PLA) and its contributions to environmental sustainability. It delves into the synthesis, properties, and numerous applications of PLA, accompanied by illustrative examples demonstrating its ability to reduce carbon emissions. The environmentally friendly characteristics of PLA, coupled with its versatility, make it a vital player in the ongoing efforts to combat climate change. PLA generally requires lower extrusion temperatures than other 3D printing materials, such as ABS (Acrylonitrile Butadiene Styrene). Lower extrusion temperatures lead to reduced energy consumption during the printing process thus the reduction in carbon dioxide emissions during production. Plants, such as corn and sugarcane, play a crucial role in absorbing carbon dioxide from the atmosphere during their growth cycle. When these plants are utilized to produce PLA, the captured CO₂ remains sequestered within the plastic material. This contributes to offsetting CO₂ emissions from other sources. In conclusion, the usage of PLA has demonstrated positive contributions to the reduction of carbon dioxide emissions through its renewable sourcing, lower extrusion temperatures, lower dependence on fossil fuels, reduced greenhouse gas emissions during production, biodegradability, and participation in a closed carbon cycle.

The increasing application of TA2 titanium profiles in marine and petrochemical industries has spurred in a growing demand for diverse forms, including U-shaped, rectangular, and tubular thin-walled profiles. Traditional methods like mechanical subtractive processing and extrusion, despite their prevalence, suffer from high production costs and low efficiency. As a metal sheet forming technology, roll forming stands out for its efficiency, accuracy, and capability of producing complex shapes continuously. Nevertheless, the application of cold rolling to TA2 profiles is challenging primarily due to its low elastic modulus and high yield strength. In view of this, this study employed finite element simulation to analyze the stress and strain distribution during the TA2 roll forming process, aiming to have a better understanding of edge wave defect formation mechanism. Orthogonal experiments were performed to assess the influence of frame spacing, forming speed, roll gap, and downhill amount, on edge wave defects. The findings revealed a predominant influence of the downhill amount. Maintaining the downhill volume at 0.6 times the tube diameter kept the longitudinal strain below 0.9%, effectively mitigating edge wave defects. Implementation of these optimized parameters in an actual TA2 roll forming process confirmed the reliability of the simulations. This study establishes a solid foundation for advancing the TA2 tube cold roll forming process, enhancing the production efficiency of titanium profiles, and shedding some light on current energy conservation and emission reduction.

Computer vision (CV) systems are crucial in various fields, such as manufacturing. However, the general feedback from small and medium-sized enterprises (SMEs) in manufacturing indicates that implementing CV systems are out of reach due to their high costs, the need for expertise, and the complexities of Machine Learning (ML). To demonstrate the feasibility and applicability for SMEs, we present a cost-effective portable CV-based demonstrator for SMEs in manufacturing. Incorporating low-cost hardware components, the system integrates open-source software and employs Transfer Learning to adapt pre-existing ML models. We present two illustrative use cases with fault detection and inventory management based on plastic brick datasets including various colors and shapes to demonstrate the system's effectiveness. Bringing this into a showable demonstrator, all components are integrated into a portable suitcase as a plug and play demonstration. We showcase the portable demonstrator at German television and four different industrial fairs, leveraging these dynamic platforms for direct interaction with SMEs and stakeholders. Firsthand insights and feedback from SMEs regarding our demonstration and their challenges, as well as opportunities for CV in manufacturing were received and summarized in this research.

Aiming at the problem of increased water injection pressure and poor water injection effect during the water injection operation of Weizhou X offshore low permeability oil reservoir. The cause of clogging was clarified using cast thin section experiments, water quality compatibility experiments and reservoir sensitivity experiments, and corresponding deblocking agents were configured based on the experimental results. The experimental results show that Weizhou X oilfield has strong velocity sensitivity (damage rate 85.35%) and strong salt sensitivity (damage rate 63.02%), and there is a phenomenon of water quality incompatibility. Analysis shows that the excessive velocity of the injected water causes the core particles to fall off and the water quality is incompatible with the calcium carbonate precipitation to cause pore throat blockage as the fluid migrates to the small roar, which is the main reason for the increase in water injection pressure. Based on the analysis results, the acid solution formula was optimized and determined to be: 12% HCl + 0.5% HF + 3% CH3COOH + 0.5% C6H12N4. Indoor test results show that the acid has good clay swelling prevention and reservoir modification effects, and the average reservoir modification rate is 129.21%. The plug-removing effect is remarkable and can be promoted in similar oil fields.

In the field of material informatics, artificial neural networks (ANNs) contribute to the investigation of the processing-structure-properties-performance relationship of materials. This inspires us to leverage the capabilities of ANNs to decode properties of hydrogels, thereby customizing these active materials for sensors or actuators. In the current work, we introduce an approach to predict discrete swelling states of temperature-responsive hydrogels, especially PNIPAAm, based on their synthesis parameters, utilizing ANN models. To build the database, we analyze literature on temperature-responsive hydrogels and compile essential synthesis parameters. The corresponding data points related to these synthesis parameters are then extracted. We propose different variants of ANN models and compare their accuracy on the acquired dataset. The selected model can predict the swelling states of hydrogel samples within the test dataset with relative prediction error of 0.11. This approach is applied to predict the expected properties. Subsequently, the hydrogels can be synthesized, and their properties can be experimentally verified. Our approach can be extended to other types of hydrogels and in the prediction of additional properties. The identified synthesis parameters serve as a valuable foundation for the expansion of the database with further literature resources. An enriched database will enhance the performance of the data-driven model, thereby improving its predictive capabilities.

The laser powder bed fusion process for metals (LPBF-M) results in the development of stochastic surface features that significantly influence the interactions between parts and their surrounding environment, as well as their mechanical properties. The process parameters influence the surface quality, which is quantified by the surface roughness. Therefore, customizing the surface roughness during the build process can significantly contribute to obtaining ready-to-use parts, reducing the need for extensive surface posttreatments. This paper utilizes theoretical estimations of melt pool depth under iso-linear energy density, iso-power, and iso-temperature manufacturing process parameter conditions. These estimations are then compared with experimental evaluations of surface roughness and tensile strength in upright-built specimens to extract the trends in terms of the input energy versus roughness, and the input energy versus tensile behavior. The results show that iso-energy values yield similar roughnesses due to the consistent expected melt pool depth. Moreover, an increase in melt pool depth generates higher surface roughness, while smaller melt pool dimensions result in improved roughness. Additionally, a comparison between the melt pool size and tensile test performance reveals a detrimental impact on the tensile strength for specimens estimated to have smaller melt pool depths.

Advanced diagnostic methods are necessary for the prompt and reliable identification of tuberculosis (TB), which continues to be a worldwide health problem. Globally, there were projected to be 10 million new cases of tuberculosis in 2021, of which 9.8 million affected adults and 0.2 million children. About 15% of fatalities worldwide are attributable to tuberculosis (1.5 million deaths for every 10 million infections). To create a reliable model for tuberculosis (TB) identification using chest X-ray pictures, we use deep learning approaches in this work, namely Convolutional Neural Networks (CNNs) and a combination of transfer learning and hyperparameter tuning. The dataset provides a varied selection of 3500 normal and 700 TB-infected patients. It consists of 4200 photos that were obtained from the “Tuberculosis (TB) Chest X-ray Database” on Kaggle. By utilizing the benefits of a trained model, the suggested methodological approach incorporates transfer learning. To maximize the performance of the suggested model, hyperparameter adjustment is also used. Using the VGG19 pre-trained neural network, the model design is based on the concepts of transfer learning. The architecture makes use of task-specific layers, regularization methods, and deliberate layer freezing to enable sophisticated categorization. Training and assessment stages demonstrate encouraging outcomes, with an accuracy of almost 98% attained on a different test dataset. A more thorough examination highlights the need for caution when interpreting high accuracy, nevertheless, by highlighting possible difficulties.

The requirement for temperature-controlled storage can be challenging and expensive for the transportation and storage of biologics. Light-assisted drying (LAD) is a new processing technique to prepare biologics for storage in a trehalose amorphous solid matrix at ambient temperatures. Samples are illuminated with near-infrared laser light to speed dehydration. Previous work has shown LAD can prepare small-volume (40 μL) samples, but the feasibility of applying LAD to larger samples remains unexplored. Here, LAD is applied to large-volume samples (250 μL). Samples of a trehalose solution with an embedded protein were LAD processed and stored for 1 month. The end moisture contents of samples were determined after processing and storage. Thermal histories were monitored to determine optimal drying times. The trehalose matrix was characterized using polarized light imaging and Raman spectroscopy. Karl-Fischer (KF) titration was used to measure the water content. The end moisture contents and thermal histories show high repeatability for LAD processing. Polarized light imaging demonstrates that the trehalose matrix was resistant to crystallization. Raman spectroscopy indicates uniform water distribution and KF titration reveals a low average water content (2.5%). LAD can stabilize large-volume samples for dry-state storage at ambient temperatures and offers a potential solution for cold-chain storage challenges.

This article introduces an innovative non-volatile associative memory (AM) that leverages spintronic synapses, employing magnetic tunnel junctions (MTJ) in conjunction with neurons constructed using carbon nanotube field-effect transistors (CNTFETs). Our proposed design represents a significant advancement in area optimization and outperforms prior designs. We adopt MTJ-based spintronic devices due to their remarkable attributes, including dependable reconfigurability and nonvolatility. Simultaneously, CNTFETs effectively address the longstanding limitations traditionally associated with MOSFETs. In this work, our proposed design undergoes rigorous simulations that account for process variations. The results demonstrate that our AM system closely approximates its ideal mathematical model, even with significant process variations. Furthermore, we investigate the impact of Tunnel Magnetoresistance (TMR) on the performance of our proposed AM system. Our investigations reveal that, even with a TMR as low as 100%, our design matches and often surpasses the performance of its counterparts operating with a TMR of 300%. This achievement holds profound significance from a fabrication standpoint, as fabricating MTJs with high TMR values can be intricate and costly. Overall, our novel AM system represents a significant breakthrough in emerging technologies, harnessing the unique strengths of spintronic synapses and advanced carbon nanotube transistors while robustly addressing challenges in performance and variability.

In the context of drilling operations, the study investigated the ability of a combination of rigid mineral particles and composite plugging agents to seal simulated cracks effectively. The study used a neural network model to predict the outcomes of experiments using this combination, based on data collected during the research. Initially, a backpropagation (BP) neural network was used to establish the prediction model, which was later optimized using the particle swarm optimization (PSO) algorithm to improve its accuracy, stability, and learning abilities. As a result, the optimized prediction model was found to be capable of providing accurate and compliant drilling plugging formulas quickly. This feature helped guide targeted formula experiments and significantly reduced experimental time and costs. In five practices in a well area in the southern Sichuan region of China, the application success rate was as high as 60%, and the time spent on plugging was reduced by an average of 36%. Overall, this study contributes to the development of effective and efficient drilling techniques, which are essential in the exploration and production of hydrocarbon resources.