International Journal of Precision Engineering and Manufacturing-Green Technology最新文献

Laser-induced graphene (LIG) has been extensively researched due to its facile fabrication on various carbon-containing substrates using simple laser scribing. In recent years, advancements have enabled the production of LIG on environmentally friendly substrates, opening new possibilities for designing sustainable electronics that minimize adverse environmental effects. This paper provides an overview of the latest advancements in manufacturing technologies for LIG on eco-friendly substrates, such as paper, wood, lignin biomass, cloth, food, and biocompatible parylene-C. Furthermore, a comparative analysis is conducted between LIG generated on eco-friendly substrates and graphene patterns printed using commercially available graphene ink. This analysis emphasizes the potential efficacy of LIG as an efficient manufacturing technique for producing conductive graphene patterns. The review also outlines the remaining challenges requiring attention to advance these manufacturing processes and outlooks future opportunities, which can serve as a valuable guide for both novice researchers unfamiliar with LIG and experienced researchers aiming to utilize eco-friendly substrates in their study.

Colloidal quantum dot (CQD) solar cells have drawn a lot of attention because of their potential for bandgap engineering, which enables broad and powerful absorption in the wavelength of sunlight, and low-cost process based on the solution phase production. However, the interfacial problems resulting from the heterojunction structure containing electron and hole transport layers cause a hysteresis phenomenon that weakens the device stability. We used the dry-transfer technique to implement a hole transport layer (HTL) with enhanced interfacial properties in devices. This approach is highly reproducible and allows for precise thickness control of the HTL. It also uses substantially less environmentally harmful organic solvents for the ligand exchange process than those required by the previous layer-by-layer (LbL) deposition technique. Additionally, about 400 nm thick CQD film could be deposited without the ligand exchange process, and a power conversion efficiency of 10% with minimized hysteresis was achieved using this method. Moreover, by improving the interfacial properties over the traditional LbL approach, it was feasible to lower the charge transfer resistance related to the device's hysteresis by a factor of up to four or more.

This work adopts solvothermal synthesis to fabricate PtNi nanoparticles as thin film cathodes with superior resistance against thermally driven agglomeration for low temperature solid oxide fuel cells (LT-SOFCs) operating at 450 ºC. Metal-based porous electrodes are common choices for thin film LT-SOFCs, but pure metals with high density nanoscale porosities are vulnerable to thermal agglomeration, which imposes challenges to maintaining high performance with long-term stability. Typical Pt-based thin film cathodes are previously reported to sustain a record high 600 ºC of thermal annealing with acceptable morphological stability, but the temperature is still too low for practical LT-SOFC application. In this work, the solvothermal synthesized PtNi nanoparticle thin films show superior thermal stability, sustaining 10 h of annealing at 800 ºC without significant agglomeration observed. By controlling the length of synthesis time, the particle sizes and Pt loading ratio can be varied. The cost-effective solvothermal synthesis process for the fabrication of PtNi thin film cathode is a promising way for LT-SOFC manufacturing in scale as it involves no vacuum process like typical sputtering.

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Laser-induced backside wet etching (LIBWE) has been proposed to fabricate high-quality micromachined components on transparent materials. However, the process is limited by poor repeatability when fabricating high-aspect-ratio structures, even under the same conditions due to uncertainties arising from the thermal process and the complex mechanisms associated with the indirect irradiation of the etching process. Such errors could lead to redundant trials and wastages when trying to achieve the desired dimension. To identify the factors causing these variations, we targeted the process sounds generated during the etching. This study uses a microphone to measure factors that result in variations in material removal quantity during the etching process under the same conditions. The sound was filtered at frequencies between 3 and 6 kHz, which were selected as characteristic frequencies for the process under various laser conditions. By integrating the root mean squared value of the detail coefficient of the wavelet transform, the depth estimation closely matched the measured depth of the fabricated part. This finding suggests that determining the etching rate from sound at a certain characteristic frequency during the LIBWE process is feasible; this approach can improve the accuracy and repeatability of the process. Based on this estimation mechanism, we designed a closed-loop feedback control system capable of fabricating highly accurate microchannels in the range of 80–120 μm with a maximum error of 5.6%.

In response to the insufficient detection capability of laser paint stripping effects for a single modality and the high operational and cost requirements of existing multi-monitoring technologies, a method is proposed to integrate visual and auditory signals for evaluating laser paint stripping effects. Utilizing wavelet packet transformation for a more detailed understanding of the variations in paint-stripping sound signals, more representative energy features are extracted. The EfficientNetv2 network, optimized with an attention mechanism, further enhances the focus on crucial features. The image feature vectors are concatenated with the energy features extracted from the sound signals, forming a new and more informative feature vector for paint stripping effect discrimination. Experimental results demonstrate that the multi-feature fusion detection algorithm significantly improves the accuracy of paint stripping effect discrimination, reaching 98.7%. The 98.9% F1-Score and the smoothly converging loss curve also indicate the algorithm's effective control over category imbalance and training stability. This research is of paramount importance for improving the evaluation of laser cleaning technology effects and provides insights into multi-modal feature fusion for other relevant fields of study.

This study reports the biological synthesis of platinum nanoparticles (PtNPs) using leaf extracts of kaffir lime (Citrus hystrix DC), common basil (Ocimum basilicum Linn) and fragrant pandan (Pandanus amaryllifolius Roxb). These extracts contain sugars, terpenoids, polyphenols, alkaloids, phenolic acids, and proteins, which play an essential role in reducing Pt (IV) solutions to platinum nanoparticles (PtNPs). The PtNPs were extensively characterized, exhibiting spherical morphologies and particle sizes between 20 and 80 nm as indicated by TEM imaging. As shown by the UV–Visible Spectroscopy, the maximum absorption of platinum nanoparticles is at 256 nm. Furthermore, the electrochemical sensor exhibits good analytical performances in detecting glycated hemoglobin (HbA1c) in the real samples.

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Abstract

Friction stir extrusion is one of the most promising solid-state chip recycling techniques because of its relative simplicity and high efficiency. One of the most straightforward applications for the process is the production of recycled wires to be utilized as filler material in welding or welding-based additive manufacturing processes, in order to create an industrial symbiosis link, fostering a circular economy and enhancing the technology readiness level of the process. The scalability of the process to the thin wires needed for such applications has not been investigated so far. In this paper, an experimental and numerical analysis was developed. A dedicated numerical model was first validated and then used to design the tool geometry. The effect of tool rotation and tool force on both “standard” mechanical properties, as Ultimate Tensile Strength and microhardness, and specific properties for the envisaged application, as the wrapping around reels with different radii, was investigated. The numerical model results were used to explain the influence of the process parameters on the material flow as well as on the distribution of the primary field variables, namely temperature, strain, and strain rate. Finally, the energy demand was measured, and the specific energy consumption (SEC) was evaluated. It was found that a conical shoulder surface favors the conditions for effective solid bonding. Low values of the extrusion force have detrimental effects on the wires properties as they result either in insufficient strain, or hot cracking defects. High values of extrusion force results in lower SEC, unlocking the potential of the process as symbiotic link enabler.

Abstract

Cutting parameter optimization is considered as an effective way for energy consumption saving. In the machining process, the tool wear of cutting tools varies with the rise of the number of workpieces, which has a significant effect on cutting parameters decisions. However, most of existing approaches are conducted for a single workpiece, and cannot select the optimal cutting parameters based on the dynamic changes in tool wear. To this end, a reinforcement learning-based cutting parameter dynamic decision (RLCPDD) method is developed for each workpiece adaptive to the change of tool wear. Specifically, the correlation between the energy consumption, cutting parameters, and tool wear is analyzed, and a multi-objective optimization model considering tool wear is formulated. A Markov Decision Process (MDP) can be used for designing the decision-making of cutting parameters for the machining process. The developed RLCPDD is validated by comparative case study. The case study indicates that: (1) the different cutting parameters can be determined for the different tool wear of cutting tool, and (2) the dynamic decision of cutting parameters based on tool wear can further reduce energy consumption, production time, and production cost by 3.87%, 6.36%, and 6.83% compared with the PSO algorithm.

There are a large number of holes to be machined on aeroengine components such as blisks, casings, etc. In order to ensure position accuracy, these holes usually need to be drilled continuously in one process. To ensure the machining quality of holes, either replacing the cutting tools in advance leads to an increase in manufacturing costs, or adjusting process parameters leads to a decrease in production efficiency, which is difficult to meet the requirements of efficient and low-cost manufacturing. In response to this issue, this paper proposes a multi-objective optimization strategy for the process parameters of porous continuous drilling of superalloys alloys. A unified mathematical model for multi-objective optimization of drilling parameters has been established, and a tool life prediction model based on machining parameters and a machining process energy consumption model have been established as objective functions. The proposed optimization strategy can select different optimization strategies for different optimization objectives, including: maximum tool life, minimum machining energy consumption, and multi-objective drilling parameter optimization. Finally, experimental verification was conducted on the proposed strategy, and the results showed that the proposed optimization strategy can significantly reduce drilling processing energy consumption and increase the service life of drilling tools.

A novel cooling-assisted stationary shoulder friction stir welding (SSFSW) was employed, using Al₂O₃ nanoparticles, to achieve high-strength joints in AA6061-T6. The approach resulted in improved mechanical properties, with the optimal joint achieving an efficiency of 91%, representing a substantial increase compared to the 77% efficiency achieved in submerged FSW with rotational shoulder (RFSW). This was accomplished through narrower weld zones, finer grain structure, maintained strengthening precipitates, and more symmetrical temperature and material flow fields. In contrast to RFSW, SSFSW samples exhibited a nugget zone with a grain structure in the nanometer range (900 nm) and a higher density of strengthening precipitates. The underwater SSFSW prevented weakening in the heat-affected zone by reducing the heat input and increasing the cooling rate. As a result, the minimum hardness shifted from the heat affected zone to its boundary with the thermo-mechanically affected zone. The addition of nanoparticles significantly contributed to joint strengthening, and the specimen prepared from the stir zone of the SSFSW-optimum sample achieved a tensile strength of 494 MPa. The primary mechanism of joint strengthening in SSFSW was grain boundary hardening, while quench hardening was the primary mechanism in RFSW. Additionally, the Orowan hardening mechanism had a more significant contribution in SSFSW due to the higher concentration of strengthening precipitates that were retained during the process.