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multi-objective optimisation of fused filament fabrication of acrylonitrile butadiene styrene for enhancing mechanical performance and build time

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第11-12期137卷 5431-5449页

作者： Nguyen, Phan Quoc Khang Zhang, Yixia Sarah Zhang, Zhongpu Yang, Richard Chunhui Western Sydney Univ Ctr Adv Mfg Technol Sch Engn Design & Built Environm Locked Bag 1797 Penrith NSW 2751 Australia

Fused filament fabrication (FFF), a 3D printing technique, has gained prominence due to its diverse application in rapid prototyping, custom tooling, architectural modelling, and medical device fabrication. The current literature shows extensive works on single-objective optimisation but limitation in studies on multi-objective optimisation that determines a set of process parameters to attain a balance between conflicting properties such as mechanical properties and build time. Therefore, this study experimentally investigates mechanical properties and build time of FFF printed acrylonitrile butadiene styrene (ABS), considering the influences of five key FFF printing process parameters on mechanical properties, including extrusion temperature, layer thickness, printing speed, number of contours, and infill density. Response surface methodology (RSM) and artificial neural network (ANN) are both adopted for pattern recognition before the genetic algorithm (GA) and multi-criteria decision-making (MCDM) algorithm are applied for optimisation. Results reveal that the infill density is the main contributor to tensile strength while the layer thickness has the highest impact on build time. Both RSM-GA and ANN-GA approaches succeed at achieving a balance between tensile strength and build time. In comparison to RSM, ANN proves to be a superior tool with remarkable accuracy in predicting responses across diverse parameter settings. The findings of this study hold significant implications for designers and manufacturers of domestic and small industrial components.

关键词： Acrylonitrile butadiene styrene (ABS) Build time Fused filament fabrication (FFF) Machine learning Mechanical properties multi-objective optimisation

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multi-objective optimisation of frequency offset and quality factor in semi-conical microwave resonant cavity for syrup brix measurement

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JOURNAL OF FOOD ENGINEERING 2025年 391卷

作者： Yang, Yongbin Liu, Yetong Meng, Yanmei Li, Wenjun Guangxi Univ Coll Mech Engn Nanning 530004 Peoples R China Guangxi Yuchai Special Purpose Vehicle Co Ltd Nanning 530007 Peoples R China

Accurate measurement of syrup brix is essential for automated sucrose production because it significantly influences the quality and efficiency of the production process. The microwave resonant cavity method, an effective approach for syrup brix measurement, imposes special performance requirements on the cavity structure design, potentially necessitating unique shapes to capture the properties of the target substance. Traditional empirical design methods for these sensors have significant limitations with nonconventional shapes, often requiring further tuning and analysis of the parameters to adapt to complex geometries and achieve optimal performance. Previous studies have indicated that syrup brix variation can be predicted by the electromagnetic field characteristics of the resonant cavity, specifically the resonant frequency and quality factor, thereby allowing this relationship to be leveraged for sensor optimisation. Therefore, this study presents a systematic multi-objective optimisation approach using electromagnetic simulation software (ANSYS Electronic HFSS) and an Adaptive Reference-based Non-Dominated Sorting Genetic Algorithm (ARNSGA-II), which optimises the frequency offset and quality factor to enhance the sensor sensitivity and accuracy. Experimental comparisons between the optimised and conventional cavity designs demonstrate substantial improvements, with a 60.8% increase in the frequency offset and a 67.2% enhancement in the quality factor. Furthermore, applying the optimised design to a predictive model reduced the mean absolute error to 1.43 degrees Bx, representing a 38.4% improvement compared to the empirical design-based resonant cavity. Results demonstrated that this study provides a robust framework for optimising complex cavity structures and offers valuable insights for the development of high-precision microwave sensors tailored for syrup brix measurements.

关键词： Syrup brix Coaxial resonant cavity multi-objective optimisation Electromagnetic simulation Resonance parameters

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multi-objective optimisation of automotive drag coefficient and lift coefficient via artificial neural network and genetic algorithm

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PROGRESS IN COMPUTATIONAL FLUID DYNAMICS 2025年第2期25卷 79-94页

作者： Yuan, Zihou Zhang, Hongwei Xiang, Wangyang Du, Yanming Zheng, Xingren Wuhan Text Univ Hubei Key Lab Digital Text Equipment Wuhan 430200 Hubei Peoples R China

In terms of combining traditional methods with others, group method of data handling and radial basis function neural networks have been applied to the study of automotive aerodynamics. Backpropagation neural networks have relatively few applications. The aerodynamic performance of a vehicle is improved by optimising its shape parameters. In this paper, a shape parameter optimisation method is proposed which employs backpropagation neural network model and genetic algorithm to reduce the air resistance and air lift. Five form parameters of the car are taken as design variables and 50 sets of sample data are designed using optimal Latin hypercube experimental design method. After performing CFD simulation, all the data are used for backpropagation neural network learning. Genetic algorithm is then used to perform multi-objective optimisation on all the data and the Pareto front is developed. The results show that the C-D and C-L of the optimised car are reduced by about 15.28% and 25.85%, respectively.

关键词： shape parameters computational fluid dynamics CFD BP neural network NSGA-II multi-objective optimisation Pareto front

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multi-objective optimisation of 2-D anti-recoil control scheme via a new 2-D recoil analysis model for a deep-water drilling riser after ED

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SHIPS AND OFFSHORE STRUCTURES 2025年

作者： Zhao, Guixin Meng, Shuai Che, Chidong Shanghai Jiao Tong Univ State Key Lab Ocean Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

Distinct from former studies which typically focus on 1-D anti-recoil control based on axial-only recoil dynamics of deep-water drilling risers after emergency disconnection (ED), this study tries to perform a multi-objective optimisation of 2-D anti-recoil control scheme employing a newly developed 2-D recoil analysis model accounting for the internal flow-structure coupled lateral dynamics. At first, a 2-D model is developed where the recoiling riser is modelled as a flow-discharging cantilevered pipe attached to an axially moving base immersed in a fluid. The optimised 1-D control scheme in [Meng S, Zhao GX, Che CD. 2025. optimisation of an anti-recoil control scheme based on improved nsga-II for a deep-water drilling riser after emergency disconnection. Ocean Eng. 316:119823. doi: 10.1016/***.2024.119823] fails as the upper flex joint angle exceeds the allowable limits. Recalling that the dynamic position operation can be continued for a period owing to the redundant system after dynamic position system failures, a 2-D anti-recoil control scheme is then proposed that the recoil process is regulated by altering high-pressure air volume, adjusting anti-recoil valves and tuning platform offset within a small distance for a short time. Finally, the 2-D anti-recoil issue is converted into a multi-objective optimisation problem which can be solved using improved NSGA-II, and the effectiveness has been verified adopting a deep-water drilling riser. The 2-D anti-recoil scheme and recoil analysis model can be a workbench for further optimisation of anti-recoil schemes.

关键词： Drilling riser ED 2-D recoil analysis model 2-D anti-recoil control multi-objective optimisation

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multi-objective optimisation of integrated grey-green infrastructure in response to climate change from a life cycle perspective

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JOURNAL OF CLEANER PRODUCTION 2025年 498卷

作者： Liu, Ming Lai, Chengguang Zhang, Yu Chen, Biyi Wang, Mo South China Univ Technol Sch Civil Engn & Transportat State Key Lab Subtrop Bldg & Urban Sci Guangzhou 510641 Peoples R China Guangzhou Univ Coll Architecture & Urban Planning Guangzhou 510006 Peoples R China Guangzhou Univ Architectural Design & Res Inst Guangzhou 510499 Peoples R China Nanjing Univ Sch Architecture & Urban Planning 22 Hankou Rd Nanjing 210093 Peoples R China Chiang Mai Univ Multidisciplinary & Interdisciplinary Sch 239 Huay Kaew Rd Tumbol Suthep Amphoe Mueang Chiang Mai 50200 Thailand

Integrated grey-green infrastructure is widely recognized as a promising approach to addressing urban flooding resulting from climate change and many scholars have conducted optimisation studies to reduce its economic costs and enhance its functional performance. However, as the concept of sustainable development garners increasing attention and the pace of global warming accelerates, it is clear that these traditional optimisation objectives of saving economic costs and improving hydrological performance cannot meet the requirements of the Sustainable Development Goals. Therefore, a representative site in Guangzhou, China, was used to carry out a multi-objective optimisation to achieve the trade-off between economic and environmental costs. Furthermore, a multi-criterion decision-making method was also employed to find the optimal layout based on the economic cost, environmental cost, and corresponding hydrological performance under climate change in the life-cycle of each layout in the optimised solution set. The results indicated that: (1) Decentralised layouts require lower economic and environmental costs, and as decentralisation increases, the similarity of cost levels among optimised layouts increases. (2) Integrating a higher percentage of green infrastructure improves hydrological performance and reduces carbon emission costs by up to 80%. (3) Optimised IGGI systems outperform single grey infrastructure systems in economic, environmental, and hydrological aspects, achieving maximum improvements of 75%. (4) The use of permeable pavement may be cost-effective and effective in managing runoff, but it may exacerbate carbon emissions. This framework offers a comprehensive, scientific, and sustainable methodology for urban regions to adaptively manage stormwater in light of climate change. It lays the groundwork for future investigations into optimising such frameworks for diverse urban settings, exploring scalability, and integrating technological advancements.

关键词： Green infrastructure Integrated grey-green infrastructure Climate change Life cycle assessment Life cycle cost multi-objective optimisation

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multi-objective optimisation research of ship form in waves based on Kernel principal component analysis

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SHIPS AND OFFSHORE STRUCTURES 2025年第4期20卷 505-518页

作者： Jie, Liu Bao-ji, Zhang Yu-yang, Lai Li-qiao, Fang Shanghai Maritime Univ Merchant Marine Coll Shanghai Peoples R China Shanghai Maritime Univ Coll Marine Sci & Engn Shanghai 201306 Peoples R China Beijing Soyotec Co Ltd Beijing Peoples R China

In the process of multi-objective optimisation design of ship forms, subjective weighting methods rely on experts' experience and have a certain impact on the scientificity of results. The objective weighting method can effectively solve this drawback and reduce the interference of human factors. Therefore, this paper proposes a multi-objective optimisation design programme for ship forms based on KPCA method, and constructs a multi-objective optimisation design framework. In this paper, the KPCA method is used to calculate the contribution rate of each indicator for objective weighting, give the comprehensive evaluation score of the optimal solution set and make decisions, solve the technical drawbacks of subjective weighting, and obtain a satisfactory optimal hull form. The results show that the automatic multi-objective optimisation design method proposed in this paper has certain engineering application value, and can provide theoretical basis and technical support for green design and manufacturing of ships.

关键词： multi-objective optimisation decision-making KPCA objective weighting resistance seakeeping

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multi-objective optimisation for AGV and machine integrated scheduling problem considering battery consumption rate

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INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL 2025年第2期19卷 133-163页

作者： Wu, Bin Ding, Yuchao Nanjing Tech Univ Sch Econ & Management Nanjing 211816 Peoples R China

With the advancement of eco-friendly manufacturing and smart production, researchers have increasingly focused on the automatic guide vehicle (AGV) and machine integrated scheduling problem, considering energy consumption. However, the current research overlooks the varying battery consumption rates of AGV under different operational conditions. This paper addresses this gap by dissecting AGV battery usage into load and no-load scenarios and develops a multi-objective optimisation model for the integrated scheduling problem. An improved Non-Dominated Sorting Genetic Algorithm (I-NSGA-II) is presented to solve the model. In the algorithm, a novel two-segment real number encoding approach for machine/AGV assignment and process operations is proposed. The Taguchi analysis was used to discuss the key parameters of the algorithm, and experiments were conducted to perform sensitivity analysis on the model. Simulation results demonstrate that the proposed algorithm outperforms three other widely recognised algorithms in the benchmark.

关键词： multi-objective optimisation scheduling automatic guide vehicle flexible job shop NSGA-II

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multi-objective optimisation of building planning energy saving based on genetic algorithm

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INFRASTRUCTURE ASSET MANAGEMENT 2025年第1期12卷 1-14页

作者： Chen, Ningjing Wang, Juanfen Southeast Univ Architecture & Art Design Dept Chengxian Coll Nanjing Peoples R China

The construction industry itself has led to a large amount of energy consumption;this paper reports on a genetic algorithm for optimising the planning and design of rural buildings with energy saving and multiple objectives. Firstly, the multi-objective optimisation problem and Pareto concept are discussed, the non-dominated sorting genetic algorithm II (NSGA-II) is applied to solve the problem of building-energy-saving-integrated optimisation and the algorithm implementation of building-energy-saving-integrated optimisation design based on NSGA-II is given. Incremental costs and incremental benefits are used as objective functions, and relevant assumptions are made to keep the model realistic while simplifying calculations. Secondly, a series of constraint functions is set up to ensure the superiority of the results. By constructing model assumptions, objective functions and constraint functions, the energy-saving optimisation model is formed and solved using the genetic algorithm. From the optimisation results, it can be seen that the energy consumption of all the design schemes in the Pareto solution set is between 25.0 and 31.7 kWh/m2. Therefore, in the selection of architectural planning and design schemes, simply pursuing low energy consumption and high comfort should be avoided, and there should also be comprehensive consideration of economic costs to choose the most cost-effective scheme.

关键词： architectural design energy saving genetic algorithm multi-objective optimisation Pareto

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A novel Kriging-assisted multi-objective optimisation method considering infeasibility ratio under input uncertainty

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JOURNAL OF ENGINEERING DESIGN 2025年第4期36卷 571-595页

作者： Wei, Hua Zhou, Qi Dhaene, Tom Couckuyt, Ivo Univ Ghent Dept Informat Technol INTEC IDLab IMEC B-9052 Ghent Belgium Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan Peoples R China

Many real-life engineering applications come with various sources of uncertainty. Uncertainty can also impact the feasibility of solutions in constrained optimisation. Classic robust optimisation approaches often significantly increase the computational burden and might overlook different tolerable risks, resulting in conservative sub-optimal solutions. We reformulate the classic constrained problem as a multi-objective optimisation problem and propose a Kriging-assisted multi-objective optimisation method that trades off Infeasibility Ratio (IR) with robust performance: (1) a formulation to approximate IR is developed and introduced as an additional objective and (2) an acquisition function balancing exploration (refining feasible boundaries) and exploitation of promising areas with multiple designs is proposed. Furthermore, a new quality metric has been developed to effectively measure the convergence and diversity of the obtained Pareto solution set. The proposed framework is tested on five numerical problems and applied to an engineering case: the design of a honeycomb vibration isolator. A comparison study with other optimisation methods is conducted to verify its effectiveness and performance with promising results.

关键词： multi-objective optimisation constraint handling strategy sequential sampling input uncertainty Kriging model

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Ensemble learning unlocking point load forecasting accuracy: A novel framework based on two-stage data preprocessing and improved multi-objective optimisation strategy

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 124卷

作者： Luan, Jingmin Li, Qiyang Qiu, Yuyan Liu, Weican Northeastern Univ Qinhuangdao Qinhuangdao Peoples R China Nanyang Technol Univ Singapore Singapore

Accurate point power forecasts are critical for maintaining the security and stability of the grid. However, Load data is volatile and difficult to predict with high accuracy. To improve the stability and accuracy of the model, we proposed a novel hybrid load forecasting framework consisting of three modules. Firstly, we used Successive Variational Mode Decomposition (SVMD) in the data preprocessing module to extract trend features and denoise the data. To reduce the negative impact of feature redundancy on model predictions, we employed feature selection to identify four essential features to aid model training. Secondly, in the ensemble learning module, we address the limitations of single predictive models by combining the models Back Propagation (BP), Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU) and Transformer. Finally, in the improved multi-objective optimisation algorithm module, we implemented various strategies to enhance the optimisation algorithm. We designed some experiments using three load datasets from Australia. The results demonstrated that the mean absolute percentage error values are below 1.25%, with the best value reaching 0.9256%. In contrast, the best result for the mean absolute percentage error of the baseline models was 1.4431% in New South Wales. This represents a 35.9% improvement in load forecasting performance with our proposed model, highlighting its superior accuracy compared to competing approaches. It shows that our forecasting framework is far better than other rivals.

关键词： Load forecast Ensemble learning Data preprocessing multi-objective optimisation

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