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

With the advancement of technology, a new paradigm that utilizes artificial intelligence (AI) has emerged in the smart manufacturing industry. The adaptability and flexibility of AI are gaining significant attention as they offer solutions suitable for dynamic environments and support complex decision-making processes. This intelligent trend is creating new opportunities in the global manufacturing industry and enabling more flexible and personalized production processes. This study explores a novel approach that employs multi-objective reinforcement learning to optimize two objectives, namely, production quality and yield (productivity), in non-digitalized manufacturing processes. Through this methodology, we investigate how AI and data can be leveraged to digitalize and optimize production processes in non-digital industries. Moreover, this approach can effectively derive optimal parameters for manufacturing processes through multi-objective reinforcement learning. This research has potential to address complex problems in the manufacturing industry and emphasizes the ability to find the optimal balance between production quality and yield. These findings contribute to the continuous development of intelligent manufacturing systems and are expected to enable efficient and adaptable production processes within the industry, thereby playing a crucial role in guiding the direction towards active utilization of data and AI in non-digital industries. This research achieved an 85.24% accuracy in predicting fiber strength and a 87.02% accuracy in predicting fiber elongation, resulting in a 7.25% improvement in productivity.

Prognostics and health management (PHM) has developed into a crucial discipline because of its never-ending pursuit of safety, effectiveness, and dependability. The aircraft Landing gear (LG) is one of the most significant components during takeoff and landing. Consequently, the PHM of LG is essential for the aircraft to operate safely and reliably. This paper provides an in-depth exploration of the developments, difficulties, and prospects in PHM for aircraft LG. The study begins by providing an overview of the LG parts and related faults, emphasizing their importance for the flight safety. The insights of PHM are presented based on various artificial intelligence (AI) techniques. Various approaches are discussed for fault detection and isolation (FDI) and remaining useful life (RUL). These efforts help to improve the maintenance and decision-making (MDM) process, which improves the overall effectiveness of PHM. With the aim of giving researchers a useful resource, this review addresses to fill the research gaps based on the available literature so far. It lays the foundations for future advancements by highlighting the challenges in this field.

Looking at the dynamically developing market of engineering materials, there is a need to create newer functional composites. Today's economic situation related to high energy prices and environmental threats force industry to conduct sustainable production. Polymer composites based on plant raw materials are increasingly appearing on global markets, which are light, have good mechanical properties and are also pro-ecological. This work involved the production of hybrid composites based on bio-based poly (ethylene terephthalate) by means of injection molding. Two types of fibers were used simultaneously as the reinforcement phase: basalt fibers and carbon fibers in the amount of 5, 7.5, and 10 wt% of each. The produced materials were subjected to a wide range of mechanical, thermal, and functional characteristics. The experimental data were compared with the theoretical results which were calculated from different micromodels. The studies showed that with the addition of the filler, the mechanical properties of the produced composites increased, but the optimal content was found for composites with 7.5/7.5 wt% addition of fibers, where the improvement was – 81%, 337%, and 25%, for tensile strength, Young's modulus, and impact strength, respectively. In the produced materials, the thermal properties of composites were also improved, where the shrinkage decreased by min. half, and linear coefficient at least 3 times. Sufficient adhesion between the fibers and the matrix was confirmed by SEM images and mechanical micromodels, which confirmed the highest efficiency of reinforcement with a total content of 15 wt% of fibers. To assess the influence of extreme conditions on the behavior of composites, hydrolytic degradation was carried out, which showed that the addition of fibers will not increase water absorption. The mechanical tests of the incubated materials lead to the conclusion that the produced materials could be successfully used in long-term applications because the properties obtained during the tensile test have deteriorated by only max. 5%. The work showed for the first time the modification of bioPET using two types of fibers introduced simultaneously. Hybridization of bioPET with basalt and carbon fibers has shown that it is possible to create very durable composites with a high Young's modulus. The work showed that different fibers are responsible for increasing other parameters – basalt fibers increase strength, while carbon fibers increase Young's modulus. The research may contribute to the popularization of bio-based polymer composites that have high strength for low weight and are a cheaper equivalent than polyamide-based composites.

Application of lightweight material like aluminum alloy is increasing its importance in various industries due to effective reduction of structure weight and sequential advantages like reduction of greenhouse gas emission and carbon footprint. However, deflection of aluminum thin-walled blank during production by machining is a challenge that merits further studies. Burnishing as a non-metal removal finish-machining process is usually used as a final treatment in the production chain of samples. However, in burnishing of thin-walled structure, machining-induced residual stress causes dimensional and geometrical distortion followed by problems in manufacturing accuracy and mismatch in assembly. Therefore, to minimize the consequence of the abovementioned errors, the source of the distortion should be identified and minimized during machining since usually no further operation is placed in the production chain after burnishing. To effectively tackle this challenge, in the present study an analytical model is developed to find how the burnishing process factors i.e. pass number and static force together with initial blank size impact the distortion of thin-walled 6061-T6 plates. The curvatures which were derived from analytical model were compared to those of burnished samples measured by coordinate measuring machine. It was found from the results that the burnishing pass number because of its impact on work hardening and regeneration of stress together with blank size play crucial role on determining the sample’s distortion. It was obtained that with 2 pass burnishing results in minimizing the distortion of material. Moreover, the blank’s length to width ratio due to its impact on material stiffness in corresponding direction significantly impacts the deformation after unclamping. The results which were derived from analytical model were compatible well with experimental values in term of final distribution of residual stress and maximum height of distorted parts.

The recycling potential of Carbon Fiber Reinforced Thermoplastics (CFRTP) significantly surpasses that of traditional Carbon Fiber Reinforced Plastics, positioning CFRTP as a preferable choice for fabricating lightweight, recyclable composite materials through heterogeneous adhesion with aluminum alloys. The employment of adhesives in crafting CFRTP-metal composites emerges as an efficient strategy, wherein the strength and performance of adhesive joints are heavily reliant on the surface characteristics of the materials involved. As such, the implementation of suitable surface treatment at the joint interface emerges as a pivotal factor in defining the quality of the joint during the bonding process. Laser surface treatment of carbon fiber composites introduces an innovative, environmentally friendly technique for effective removal of surface coatings and impurities. Furthermore, laser microtexturing modifies the surface microstructure of the material, exploiting the advantages of mechanical interlocking at the joint, thus substantially improving the shear strength of the adhesive interface. This investigation embarked on laser surface processing to elevate the joint quality of CFRTP and metals, affirming the efficacy of laser processing on enhancing the bonding of treated specimens. The experimental findings showed a significant increase in shear strength at the joint interface due to the laser processing patterns. The post-laser treated materials exhibited a maximum shear strength of 17.29 MPa, which is approximately three times stronger than the untreated specimens.

Hydraulic cylinders are typical actuators that are used in many industries, including manufacturing and construction machinery. Due to the wide application of cylinders, cylinder failures could increase maintenance costs, reduce productivity, and raise safety issues. Therefore, estimating and predicting the condition of cylinders is necessary for cost reduction and safety. This paper reviews various methods that have been proposed to estimate and predict cylinder failures. The paper first investigates the types of failures that can occur in cylinders and their causes. The failures include internal leakage, external leakage, and seal wear. The sensors used to identify each type of failure are then introduced. Since the failure information of the cylinder is implicitly embedded in the measured data, different diagnostics methods for isolating the failure information have been developed for each sensor. The diagnostic methods vary from traditional feature engineering to recent artificial intelligence-based methods. The prognostics that provide the remaining useful life of the cylinder are then reviewed. Finally, the paper discusses the challenges associated with the fault prognosis of hydraulic cylinders and future prospects.

Today’s economic environment, the machine tool industry continually focuses on enhancing performance while reducing costs, conserving energy, and minimizing environmental impacts. This study employs a CAD-generated virtual model to assess the performance of key components in a 5-axis machine tool. The primary focus of this work on machine components' optimization design is to enhance machining performance, addressing both static and modal aspects generally leading to structural integrity of the machine and consumption conservation of energy. Initially, the modal impact experiments carried out on machine tool are verified by the mechanical numerical code to further carry out novel tests. Modal frequency differences (2.4–6.7%), as revealed by comparative analysis, validate the accuracy of the model setup and boundary conditions within a 10% threshold, allowing for the development of novel studies with accepted discrepancies. Then density-based optimization approach is employed to redesign the machine tools, aiming to raise the intrinsic oscillation frequency of the structure and minimize structural deformation from 0.021957 to 0.020864 µm respectively for the final optimized tool turret. After this, the model is forwarded for structural verification. This approach introduces a design-for-remanufacturing strategy, enhancing existing products by improving functionality and rectifying damaged components. Such optimization leads to lightweight structures and requires less material for reproducing parts. With the increasing demand in ESG (environmental, social, and governance) investments and emphasis for the potential of substantial energy savings through lattice optimization. The potential for substantial energy savings and reduction in environmental effects by optimization of a five-axis machine tool with utilization of ESG factors in considerations. The lattice optimization of machine components led to a 64.24% reduction in energy consumption, demonstrating the feasibility and benefits of integrating ESG principles into machine tool design.

Low-carbon manufacturing is an inevitable requirement for the green transformation of enterprises. For batch hobbing, continuous improvement of process parameters is an important way to achieve low-carbon optimization under the constraints of limited data and time-varying machining configurations. This is the research gap that needs to be filled. Therefore, in this paper, a dynamic modeling and continuous optimization method for comprehensive carbon efficiency (CCE) of hobbing based on data-driven discrete simulation is proposed. Specifically, the study integrates ML (meta-learning) and DEVS (discrete event system specification) in the hobbing process to create a dynamic model of CCE. The dynamic model combines the generalization of the data-driven approach and the capability to abstract events of the discrete simulation approach, which can autonomously adapt to the current machining configuration and output machining results in real time. On this basis, a modified multi-objective seagull optimization algorithm (MOSOA) is used for the continuous optimization of CCE in batch hobbing. Finally, the effectiveness and superiority of the proposed method are verified by a case study and comparative analysis. Moreover, this paper analyzes the effect of process parameters on CCE under different working conditions and provides guidance for gear hobbing.

The effective electrical conductivity (EEC) and capacity of the electrodes are altered by the composition of electrode materials, leading to a significant impact on the performance of the Li-ion battery (LIB) cells. This study aims to develop a more efficient numerical optimization method that integrates hierarchical homogenization and feedforward neural networks (FNN) to identify the optimal composition of electrode materials. Currently, this determination heavily relies on conducting multiple experiments. The cathode's EEC, as per its formulation, is assessed through hierarchical homogenization of its components. The optimization is expedited using FNN in the homogenization. The LIB cell's performance is evaluated based on the cathode formulation via the hierarchical homogenization and the Doyle/Fuller/Newman model. The multi-objective optimization problem is formulated and solved using the modified NSGA-II. The resulting Pareto-optimal solutions identify the power optimized and energy optimized cells. The power density of the former is increased by 51% while maintaining the same energy density and the latter cell's energy density is increased by 68% while maintaining the same power density, as compared to the initial cell.

Applications of graphene-based materials in wearable devices have garnered significant attention owing to their excellent mechanical and electrical properties. However, graphene fabrication is hindered by its inherent structural characteristics, which necessitates the development of alternative materials for strain sensors. In this study, a novel flexible resistive-type strain sensor composed of a unique three-dimensional conductive carbon network was fabricated using a UV pulsed laser. Using a 355-nm UV pulsed laser, composites based on UV laser-reduced graphene oxide (UV-LRGO) with CuO nanoparticles on a PDMS substrate (Cu/UV-LRGO/PDMS) were selectively fabricated via direct laser writing. This fabrication method offers a contact-free, environmentally sustainable, and cost-effective approach, providing a streamlined one-step process that eliminates the necessity for toxic chemicals, thermal reduction, and complex protocols. The composites were meticulously characterized via various spectroscopic techniques. Notably, the proposed sensor exhibited robust performance, withstanding 7,200 stretching-relaxing cycles and accommodating strains of up to 25%, while also exhibiting a high strain gauge factor (~ 1026 GF). This work introduces a straightforward strategy for fabricating flexible strain sensors with high sensitivity and remarkable repeatability for human health monitoring, and observations including wrist pulses, finger banding, and facial eyebrow movements can be effectively monitored.