Optimization Technology Research Articles

Plastic Injection Molding Process Analysis: Data Integration and Modeling for Improved Production Efficiency

This paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific period at the beginning of a shift change. These data were subjected to exploratory analysis to identify correlations between important variables, such as injection time, cycle time, and mold pressures. Additionally, classification models, including Random Forest and Logistic Regression, were constructed to predict and classify the process state based on these variables. The model results demonstrated high predictive performance, with 99.5% accuracy for Random Forest and 97% for Logistic Regression. These results provide a strong foundation for the early identification of potential problems and informed decision making to improve the efficiency of the plastic injection molding process. This study contributes to the advancement of the integration of intelligent technologies in industrial process optimization, aligned with the principles of Industry 4.0.

Integrated Hybrid Modelling and Surrogate Model-Based Operation Optimization of Fluid Catalytic Cracking Process

Fluid Catalytic Cracking (FCC) is one of the most important conversion processes in oil refineries, widely used to convert high-boiling, high-molecular-weight hydrocarbon components from crude oil into more valuable products like gasoline and diesel. Advanced simulation and optimization technologies are critical for improving the operational efficiency and economic performance of the FCC process. First-principles-based simulators rely on parameter estimation and are computationally intensive, making them unsuitable for online optimization. In recent years, with the development of deep learning, data-driven models have made significant progress in FCC modeling. However, due to their black-box nature and difficulty with extrapolation, they are rarely used for optimization. To bridge this gap, we propose an integrated framework that combines hybrid modeling and surrogate model-based optimization. This approach combines plant and simulation data to train a multi-task learning prediction model, which then serves as a surrogate for operational optimization. Validated on a large-scale FCC unit in southern China, the model predicts product yields with an error margin of under 4.84% for all products. Following optimization, yields of LNG, gasoline, and diesel rose by an average of 0.10 wt%, 1.58 wt%, and 1.05 wt%, respectively, resulting in a 3.67% increase in product revenues. This highlights the substantial potential of this framework for industrial applications.

Surface flashover in 50 years: II. Material modification, structure optimisation, and characteristics enhancement

AbstractSurface flashover is a gas–solid interface insulation failure that significantly jeopardises the secure operation of advanced electronic, electrical, and spacecraft applications. Despite the widespread application of numerous material modification and structure optimisation technologies aimed at enhancing surface flashover performance, the influence mechanisms of the present technologies have yet to be systematically discussed and summarised. This review aims to introduce various material modification technologies while demonstrating their influence mechanisms on flashover performances by establishing relationships among ‘microscopic structure‐mesoscopic charge transport‐macroscopic insulation failure’. Moreover, it elucidates the effects of chemical structure on surface trap parameters and surface charge transport concerning flashover performance. The review categorises and presents structure optimisation technologies that govern electric field distribution. All identified technologies highlight that achieving a uniform tangential electric field and reducing the normal electric field can effectively enhance flashover performance. Finally, this review proposes recommendations encompassing mathematical, chemical, evaluation, and manufacturing technologies. This systematic summary of current technologies, their influence mechanisms, and associated advantages and disadvantages in improving surface insulation performance is anticipated to be a pivotal component in flashover and future dielectric theory.

Multi-Agent Approach to Political Discourse Translation: From Large Language Models to MAGIC-PTF System

This research addresses the automated translation of Chinese political discourse into Russian utilizing Large Language Model (LLM) optimization technologies and a multi-agent approach. The study focuses on developing the MAGIC-PTF system, which implements multi-stage text processing through the interaction of four specialized agents. The system's key component is the Style Agent, which ensures stylistic uniformity and terminological accuracy based on a specifically trained LLM. The Translator Agent performs the primary translation work and is responsible for the final text formatting. The Editor Agent conducts multi-level verification and correction of translations, considering linguistic, semantic, and cultural aspects. The Reader Agent analyzes the text from the target audience's perspective, evaluating its reception by native Russian speakers. The methodology integrates LLM optimization technologies and a multi-agent approach, with experimental testing conducted on the fourth volume of "Xi Jinping: The Governance of China" and its official Russian translation. The study includes a comprehensive analysis of system effectiveness using the COMET metric and comparative testing with existing machine translation platforms. The research's scientific novelty lies in developing a methodology for applying LLMs to specialized translation tasks and creating an effective coordination mechanism for intelligent agents in the translation process. Experimental research demonstrated MAGIC-PTF's superiority over traditional machine translation systems in key parameters: terminological accuracy, stylistic consistency, and preservation of culture-specific elements in political discourse. The developed system opens new possibilities for automated translation of political discourse and can be adapted for translating other specialized text types, confirming its significance for modern translation technology development. Of particular value is the system's scalability and adaptability to various language pairs and discourse types, creating prospects for further development in automated specialized text translation. The research findings also contribute to advancing the theory and practice of LLM application in professional translation.

Optimization of Composite Wheel Fender for Manned Lunar Vehicle

Abstract Weight reduction is the eternal theme of aerospace optimization. In the process of weight reduction, the main ways are material change, structural change, and processing method change. In the process of aerospace structure design, the boundary and load conditions are more complicated, which restricts the development of optimization technology in the aerospace field to some extent. At present, the research object of optimization technology in the aerospace field is mainly the large parts of the aerospace craft, and the structural optimization of small aerospace parts is rarely involved. Therefore, the research of structural optimization design method has important theoretical and engineering significance. A complete optimization design process is proposed, the structural performance of aviation products is simulated by finite element software, and the feasibility of the optimization theory is verified. This article mainly compares several schemes for weight reduction design of composite material wheel fenders in the process of design.

Optimizing talent management in creative industries: Theoretical insights into effective database utilization

This review paper explores the optimization of talent management in creative industries through the utilization of advanced database systems. Traditional talent management methods often lack the structure and objectivity provided by modern database-driven approaches, which leverage comprehensive talent profiles, advanced search algorithms, and robust analytics to enhance decision-making processes. Case studies from industry leaders like Pixar, Wieden+Kennedy, and Ubisoft illustrate the practical benefits of these technologies in recruitment, development, and overall talent optimization. The paper also discusses challenges such as data quality, privacy concerns, and resistance to change, providing practical recommendations for overcoming these obstacles. Creative industries can significantly improve their talent management practices by fostering a culture of data-driven decision-making and investing in comprehensive talent databases. Future research directions include the impact of AI and machine learning, integration of emerging technologies, and longitudinal studies on the effectiveness of database-driven approaches.

Efficient Vehicle Detection and Optimization in Multi-Graph Mode Considering Multi-Section Tracking Based on Geographic Similarity

Vehicle detection is an important part of modern intelligent transportation systems. At present, complex deep learning algorithms are often used for vehicle detection and tracking, but high-precision detection results are often obtained at the cost of time, and the existing research rarely considers optimization algorithms for vehicle information. Based on this, we propose an efficient method for vehicle detection in multi-graph mode and optimization method considering multi-section tracking based on geographic similarity. In this framework, we design a vehicle extraction method based on multi-graph mode and a vehicle detection technology based on traffic flow characteristics, which can cope with the challenge of vehicle detection under an unstable environment. Further, a multi-section tracking optimization technology based on geographic similarity at a high video frame rate is proposed, which can efficiently identify lane change behavior and match, track, and optimize vehicles. Experiments are carried out on several road sections, and the model performance and optimization effect are analyzed. The experimental results show that the vehicle detection and optimization algorithm proposed in this paper has the best effect and high detection accuracy and robustness. The average results of Recall, Precision, and F1 are 0.9715, 0.979, and 0.9752, respectively, all of which are above 0.97, showing certain competitiveness in the field of vehicle detection.

A study of the Dynamic Relationship Between Optimal Global Population and Technological Progress

It is the quantity that depends on the interconnection between population and technology, the latter being capable of advancing to support the growth of the population. Taking into consideration the findings of Malthus, Harding, and other authors, this paper elaborates on the multifaceted and developmental connection between population growth and technological advancement within the context of the optimal population. It critiques the fallacy that population growth will always result in the depletion of resources. This paper seeks to establish the fact that a large enough optimal population can foster the generation of a positive feedback loop of technological advancement and population expansion but, at the same time, results in vices such as pollution of the environment and depletion of resources. In contrast, the population can increase the market needs, encourage the advancement of renewable energy sources, and decrease the energy depletion rate. Technological advancements, boosted by population pressure, have let human beings overcome perceived environmental and resource barriers. Thus, the optimal population is not the specific number but the goal that is set to change depending on the level of technology development. Lastly, this paper stresses the importance of technological equity on the international level.

Innovative design and process simulation of aviation bracket based on topology optimization and additive manufacturing

Abstract In this paper, topology optimization(TO) and laser powder bed fusion (LPBF) technology were combined to explore the key technology of parts integration design and manufacturing oriented to performance, structure and manufacturing process. With the help of topology optimization(TO), the lightweight design of the aviation bracket achieved the weight loss goal of 87.69% in this work. The thermophysical properties of TC4 were calculated by thermodynamic calculation, and the temperatue interval of Laser Powder Bed Fusion for TC4 is 1800 ℃- 3000 ℃ was determined . Additionally, the LPBF macro-scale simulation was carried out. The results showed that the maximum temperature in the printing process is lower than 2500℃, and the maximum displacement is 4.87 mm, which only appears in the non-design space. Finally, the safety factor and maximum displacement of aviation bracket are 1.3 and 0.8 mm, and the maximum Mises stress is 654 MPa, all of which meet the design requirements.

Open Innovation and Regulatory Challenges in New Modality Development: The Pivotal Role of Contract Development and Manufacturing Organisations in Advancing Antibody Drugs.

Ensuring regulatory-compliant manufacturing capability is an essential challenge for new treatment modalities, but its internalisation is not easy for pharmaceutical companies, especially start-ups. This study examines the functions and requirements of contracted development and manufacturing organisations (CDMOs) using the development process of antibody medicines as a case study. Utilizing PubMed, Cortellis and Patent Integration databases, this study delves into publication and contractual trends in monoclonal antibody drugs (mAbs) development, alongside an analysis of patent filings by CDMOs, offering a comprehensive overview of the evolving landscape in mAbs innovation. In the early stages of mAbs development, dedicated bio firms (DBFs) led R&D with superior drug discovery technology but lacked manufacturing capability, which was complemented by CDMOs. This collaboration was an opportunity for CDMOs to expand their capabilities beyond manufacturing technology into antibody drug candidate discovery and structural optimisation technology. From mid-development onwards, it established a technology platform based on these capabilities and developed and established partnerships with existing pharmaceutical companies, including mega pharma. The impact of institutions and regulations on the innovation process was assessed during this development process. These findings are expected to provide valuable insights into the innovation system for new modalities.

Special Issue on Robotics and Mechatronics Technology for Aerial Robots

Aerial robot technologies, such as drones and unmanned aerial vehicles, are rapidly advancing in various domains. These robots now possess enhanced autonomous flying abilities and use sensor technology, Global Navigation Satellite System (GNSS), Light Detection and Ranging (LiDAR) system, and cameras to avoid obstacles, automatically land, and track flights. Evolving technologies in airspace management and route optimization ensure that flights are efficient and safe. Aerial robots have a wide range of applications, such as agriculture, disaster response, environmental monitoring, and inspection. These devices excel at precise data collection thanks to their high-resolution cameras and high-precision sensors. Swarm robotics, which allows collaboration between multiple aerial and ground robots, is also progressing. This enables quick coverage of large areas, which benefits disaster relief and land management. In addition, research and development of flying robots modeled after flying animals is underway. The evolution of aerial robots results in their efficient use in a variety of fields and new applications. This special issue includes the latest research papers and development reports on aerial robotic technology from the aforementioned perspectives. We hope that this special issue will pique researchers’ and engineers’ interest in aerial robots, thereby encouraging additional research and development. Finally, we would like to express our gratitude to all authors, all reviewers, the editorial board of the Journal of Robotics and Mechatronics, and Fuji Technology Press Ltd.

Research on the Application of Intelligent Technologies in Equipment Management of Vocational Logistics Training Rooms

As the importance of vocational logistics training rooms in education continues to grow, issues such as low equipment management efficiency, untimely maintenance, and underutilization of resources have become increasingly apparent, severely impacting teaching effectiveness. The rapid development of intelligent technologies offers new solutions in this field. Technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics are widely applied in the logistics industry and gradually provide efficient and refined management methods for vocational logistics training room equipment management. This study analyzes the shortcomings of existing management models, explores key applications of intelligent technologies in real-time equipment monitoring, maintenance optimization, and resource scheduling, and constructs an intelligent equipment management system aimed at improving management efficiency, reducing maintenance costs, and enhancing teaching effectiveness. The research provides theoretical support and practical references for the intelligent transformation of equipment management in vocational logistics training rooms.

Promoting coordinated spatial governance of mega-cities in China via spatial organization of metropolitan areas

Super large-sized cities and very large-sized cities (“mega-cities” for short) have become the backbone of China's socio-economic development, but they also face outstanding challenges of "urban diseases" due to their high concentration. In comparison to their foreign counterparts, China's mega-cities possess unique characteristics such as rapid growth, high population density, and large numbers of residents. They naturally have the attributes of metropolitan areas due to an administrative management system of an urban system within city territory. Promoting coordinated spatial governance of mega-cities via spatial organization of metropolitan areas not only helps mega-cities better allocate resources at a larger scope and helps expedite the transformation of development patterns, but is also crucial for better implementing new-type urbanization strategies. In response to the issues in China’s previous research and practices related to mega-cities and metropolitan areas, leveraging the “Critical Technology for Spatial Optimization of Metropolitan Areas of City Clusters” of the national key R&D plan in the “14th Five-year Plan” period, we have employed a scientific approach that combines data and models to propose spatial governance strategies. These strategies encompass both the overall metropolitan area level and the cross-border regional level. Our primary focus lies in optimizing the spatial layout of the “three domains” – ecological space, production space, and living space, facilitating functional dispersal, enhancing spatial patterns, adjusting land use structures and spatial resource allocation, coordinating rail transit and the organization of functions along transit lines, and innovating service management for improvement of administrative efficiency. Through the implementation of these diverse measures, we aim to establish a comprehensive composite collaborative system.

Advanced AI Technologies for Defect Prevention and Yield Optimization in PCB Manufacturing

The integration of Advanced Artificial Intelligence (AI) technologies in Printed Circuit Board (PCB) manufacturing has revolutionized the industry, offering significant improvements in defect prevention and yield optimization. As PCB manufacturing processes become increasingly complex and intricate, traditional methods of defect detection and quality assurance are often insufficient to meet the high demand for precision and efficiency. This paper explores the application of cutting-edge AI methodologies, including machine learning, computer vision, and neural networks, to address critical challenges in PCB production. The primary focus of this research is on the role of AI in enhancing automated defect detection systems and optimizing production yield. AI-based systems such as predictive maintenance, real-time process monitoring, and intelligent decision-making have been shown to drastically reduce defects while improving overall production throughput. Machine learning algorithms can identify subtle defects in real time, often undetectable by conventional methods, while neural networks can analyze historical data to predict potential failures before they occur. Additionally, AI-driven optimization techniques help manufacturers adjust production parameters dynamically, ensuring higher yields and minimizing waste. Through a combination of theoretical analysis and case studies, this paper highlights the effectiveness of AI-driven solutions in PCB manufacturing. Results from industry applications indicate significant improvements in both quality control and yield rates, providing a competitive edge to manufacturers adopting these technologies. Furthermore, this paper discusses future trends, including the integration of AI with the Internet of Things (IoT), edge computing, and sustainable manufacturing practices, which will further enhance the capabilities of AI systems in this field. The findings suggest that advanced AI technologies are not only capable of overcoming existing challenges in defect prevention but also hold the potential to reshape the future of PCB manufacturing by offering highly adaptive, precise, and efficient solutions. This research underscores the transformative impact of AI in modernizing manufacturing processes, making it an indispensable tool for the future of the PCB industry.

A review on model predictive control strategies for AC motor drives

AbstractIn the past decade, model predictive control (MPC) has been widely used in AC motor drives. Although a series of improved MPC strategies have been proposed, researchers are still seeking more effective solutions for different application backgrounds and application requirements to achieve the goals such as algorithm simplification, weighting factor design, parameter robustness enhancement, current/torque ripple suppression, and so on. In this paper, aiming at the above problems, the MPC strategy and its optimisation technology for AC motor drive system are comprehensively reviewed. Firstly, this paper introduces the classical MPC technology with the permanent magnet synchronous motor drive system fed by two‐level voltage source inverter as the control object. Secondly, this paper introduces and discusses the existing MPC optimisation methods from the aspects of current/torque ripple suppression, algorithm simplification, weight factor design and parameter robustness enhancement. Finally, based on the current industrial development requirements for AC motor drive systems and the research status of MPC optimisation methods in the existing literature, the current challenges and future trends of MPC technology are discussed.

Investigation on Effects of the Laser‐Enhanced Contact Optimization Process With Ag Paste in a Boron Emitter for n‐TOPCon Solar Cell

ABSTRACTTOPCon solar cell with boron (B)‐doped emitters plays an important role in photovoltaic cell technology. However, a major challenge to further improving the metallization‐induced recombination and electrical contact of B‐doped emitters. Laser‐enhanced contact optimization (LECO) technology is one of ideal candidates for reducing the metallization recombination and contact resistivity. In this study, we investigate the influence of LECO technology using special Ag paste with a decreased Pb content on the performance of the metallization‐induced recombination (J0,metal), contact resistivity (ρc), microtopography of the contact, the I–V parameters, and possible conductive mechanisms. The results showed that the linear resistivity is reduced from 3.56 to 2.60 μΩ·cm owing to special Ag paste, and after LECO treatment, it also has lower ρc about 0.91 mohm·cm2. Both of them have a large contribution to the FF enhancement. Meanwhile, the J0,metal drops from 500 to 200 fA/cm2, which provides a great contribution to the improvement in open‐circuit voltage. The efficiency improved by 0.26% absolute to 25.94%, mainly because of the increased open‐circuit voltage (Voc) of 4 mV and a fill factor (FF) of 0.26%. Simulated by COMSOL, the electron concentration rises to 4 × 1019 cm−3 after LECO treatment, which can generate a larger reverse current to provide a melting temperature for the glass frit, increasing the interface glass phase conductivity. The possible current transport mechanism of LECO is current tunneling effect, resulting in the decrease in the metallization recombination. After the optimization of the LECO process with low‐corrosion paste, we manufactured industrial‐grade TOPCon cells with Eff, Voc, Jsc, and FF values as high as 26.5%, 736 mV, 42.1 mA/cm2, and 85.5%, respectively.

Cold Coastal City Neighborhood Morphology Design Method Based on Multi-Objective Optimization Simulation Analysis

In the context of global warming and the frequent occurrence of extreme weather, coastal cities are more susceptible to the heat island effect and localized microclimate problems due to the significant influence of the oceanic climate. This study proposes a computer-driven simulation optimization method based on a multi-objective optimization algorithm, combined with tools such as Grasshopper, Ladybug, Honeybee and Wallacei, to provide scientific optimization decision intervals for morphology control and evaluation factors at the initial stage of coastal city block design. The effectiveness of this optimization strategy is verified through empirical research on typical coastal neighborhoods in Dalian. The results show that the strategy derived from the multi-objective optimization-based evaluation significantly improves the wind environment and thermal comfort of Dalian neighborhoods in winter and summer: the optimization reduced the average wind speed inside the block by 0.47 m/s and increased the UTCI by 0.48 °C in winter, and it increased the wind speed to 1.5 m/s and decreased the UTCI by 0.59 °C in summer. This study shows that the use of simulation assessment and multi-objective optimization technology to adjust the block form of coastal cities can effectively improve the seasonal wind and heat environment and provide a scientific basis for the design and renewal of coastal cities.

An artificial intelligence based self-adaptive dynamic process control system for enhancing in-situ bioremediation of benzene-contaminated groundwater – Part I methods

This study develops a new AI-based self-adaptive dynamic process control (SADPC) system based on stepwise inference combined with genetic algorithm optimization technologies, including a filtered-clustering inference prediction model (FCI simulator), a stepwise inference controller (SI emulator), a model predictive control controller, and two optimizers. This system effectively reflects the dynamics and complexity of the bioremediation process and controls the remediation system based on the feedback information. To achieve this goal, a statistical model for simulating the enhanced bioremediation process through the FCI simulator is proposed, which can predict the resulting contamination situation based on the previous contamination situation and pumping/injecting flow rate. Then a bridge is established through the SI emulator, which can generate a control action based on a given contamination situation. This suggested decision makers that guidelines and policies on remediation-oriented SADPC systems could be tentatively investigated, developed, and applied in the future effort.

Study on the Effect of Nitrogen Bubble Foam Flooding in Deep Heavy Oil Reservoirs with Edge And Bottom Water

Abstract The oil recovery rate is low, the degree of recovery is low and the water content rises fast after water flooding in deep edge and bottom water heavy oil reservoir in Lukeqin oilfield. In order to explore the method to improve the production effect, the integrated foam system of high temperature and high pressure resistant viscosity reduction and oil displacement is optimized. On the basis of clarifying the mechanism of improving the production effect of nitrogen foam flooding for deep heavy oil, taking the west area of Lukeqin oilfield as the research object, the parameter optimization design of nitrogen foam flooding for deep edge and bottom water heavy oil is carried out, such as injection section plug, gas-liquid ratio and injection-recovery ratio. The optimization results are as follows: the injected nitrogen foam slug is 0.2pv, the gas-liquid ratio is 1-1.2, and the injection production ratio is 1:1. The pilot test of 4 well groups was carried out on the site. After foam flooding, the injection pressure was increased by 5.2mpa, the daily oil production of the oil well was increased by 43T / D, the water cut was decreased by 45%, the accumulated oil production was increased by 32000 tons, and the oil recovery was expected to be increased by more than 10%. The purpose of improving the development efficiency of this type of oil reservoir was achieved. Using this optimization technology for scheme design and field implementation, the oil enhancement effect is remarkable and improves the benefit of deep thick oil development, which can provide technical support for the development of similar domestic reservoirs.

Study on pressure fluctuation characteristics of the mixed-flow pump with the tandem misaligned blade structure

Abstract As supporting equipment for deep-sea oil and gas resource exploitation, how to improve the transmission performance and stability of the impeller of the mixed-flow pump (MFP) has become a key technical problem. This paper focuses on studying the impeller blade of the MFP. Based on the blade optimization technology, four different tandem misaligned blade (TMB) angle schemes of α=0°,15°,45°,75° are proposed. The SST k-ω turbulence model is used to simulate the different blade angles under the pure liquid condition. The pressure pulsation of the MFP and the blade load distribution are studied. This result show that the TMB structure increases the pressure fluctuation inside the MFP. The pressure coefficient inside the impeller exhibits a more consistent pattern at an angle of α=45° compared to other angles, and the internal flow field of the MFP exhibits most stable. From this analysis of blade load distribution, TMBs have a greater effect on the pressure surface of the front blade of the MFP. When α=15°, the best pressurization performance of the MFP. This study may serve as a benchmark for subsequent iterations of optimizing the MFP’s model and hydraulic design.