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A circular approach to foster additive manufacturing early design stages sustainability: a methodological proposal

INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2023年第2期18卷 815-836页

作者： Faveto, Alberto Lombardi, Franco Chiabert, Paolo Segonds, Frederic Politecn Torino Dept Management & Prod Engn Corso Duca Abruzzi 24 I-10129 Turin Italy HESAM Univ LCPI Arts & Metiers Inst Technol 151 Blvd Hop F-75013 Paris France

The design of new products is now influenced by shifting consumer demands and technological advancements. Products must satisfy high-quality standards and have a low environmental impact. New phenomena such as distributed and urban manufacturing are emerging to cope with this. A new manufacturing era is coming where methods that prevent waste, support small workshops and encourage do-it-yourself are crucial. In the early design stage, the process knowledge is minimal, and the decision taken is vital. For this reason, it is essential to support designers in anticipating the impact of decisions on the final product. This paper establishes the groundwork for decision-support methodologies for sustainable design in One-of-a-Kind additive manufacturing prototyping. Our proposed method is applied to a Fused Filament Fabrication case study, wherein we evaluate the impact of nine variables on factors such as process time, energy and material consumption, environmental footprint, and product quality. The initial step aims to generate fresh insights through Taguchi experimentation, while the subsequent step formulates and resolves a multi-objective optimization problem using the NSGA-II algorithm. The resulting Pareto-optimal solutions serve as the basis for a novel visual-based design support tool. The proposed approach can evaluate the trade-offs between product quality and environmental impact by offering users a visual heatmap based on quantitative data. This heatmap can guide the user in the material and production parameter selection. Integrating the decision support tool into the product design process can empower designers to create environmentally responsible products while fostering innovation.

关键词： Additive manufacturing One-of-a-Kind production Sustainability Sustainable manufacturing multi-objective genetic algorithm Design support tool

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Optimization of energy ratio, benefit to cost and greenhouses gasses using metaheuristic techniques (genetic and particular swarm algorithms) and data envelopment analysis: Recommendations for mitigation of inputs consumption (a case crop: edible onion)

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ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 2022年第6期41卷

作者： Elhami, Behzad Raeini, Mahmoud Ghasemi Nejad Taki, Morteza Marzban, Afshin Heidarisoltanabadi, Mohsen Agr Sci & Nat Resources Univ Khuzestan Dept Agr Machinery & Mechanizat Engn Mollasani Iran AREEO Agr Engn Res Dept Isfahan Agr & Nat Resources Res & Educ Ctr Esfahan Iran

The present study aimed to optimize the energy, economic, and greenhouse effects indices for edible onion production in Fereydan (85 growers), and Buin (15 growers) regions in Isfahan. Required data were collected through direct measurement, interview, and questionnaire. Total energy input was obtained before and after optimization using data envelopment analysis (DEA), multi-objective genetic algorithm (MOGA), and multi-objective particle swarm algorithm (MOPSA), and was determined as 236,335, 213,068 (8.75% saving), 125,663 (48.63% saving), and 320,657 MJ ha(-1) (35.67%), respectively. In addition, energy ratio (ER) was improved from 0.79 to 0.84, 1.81, and 0.8 using DEA, MOGA, and MOPSA, respectively. By reducing the production costs by 10.2%, 63.12%, and 29.4% using DEA, MOGA, and MOPSA, respectively, benefit-to-cost ratio (BCR) index was improved from 1.44 to 1.88, 5.21, and 1.94, respectively. Also results showed that GHG emissions were mitigated by 18.39%, 47.56%, and 27.94%, respectively. Based on the findings, MOGA showed better performance compared to DEA, and MOPSA in terms of ER, BCR, and GHG factors. Despite applying metaheuristic, and artificial intelligence methods by MOPSA, this algorithm was not able to optimize the target factors, and applied the energy, and production costs in negative status (in reverse).

关键词： data envelopment analysis multi-objective genetic algorithm multi-objective particle swarm algorithm transplanting onion

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Prediction of enzymatic function with high efficiency and a reduced number of features using genetic algorithm

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期158卷 106799-106799页

作者： Reis, Diogo R. Santos, Bruno C. Bleicher, Lucas Zarate, Luis E. Nobre, Cristiane N. Pontifical Catholic Univ Minas Gerais PUC Minas 500 Dom Jose Gaspar St Bldg 20 BR-30535901 Belo Horizonte MG Brazil Univ Fed Minas Gerais Ave Pres Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil

The post-genomic era has raised a growing demand for efficient procedures to identify protein functions, which can be accomplished by applying machine learning to the characteristics set extracted from the protein. This approach is feature-based and has been the focus of several works in bioinformatics. In this work, we investigated the characteristics of proteins, representing the primary, secondary, tertiary, and quaternary structures of the protein, that improve the model's quality by applying dimensionality reduction techniques and using the Support Vector Machine classifier for predicting the enzymes' classes. During the investigation, two approaches were evaluated: feature extraction/transformation, which was performed using the statistical technique Factor Analysis, and feature selection methods. For feature selection, we proposed an approach based on a genetic algorithm to face the optimization conflict between the simplicity and reliability of an ideal representation of the characteristics of the enzymes and also compared and employed other methods for this purpose. The best result was accomplished using a feature subset generated by our implementation of a multi-objective genetic algorithm enriched with features that this work identified as relevant to represent the enzymes. This subset representation reduced the dataset by about 87% and reached 85.78% of F-measure performance, improving the overall quality of the model classification. In addition, we verified in this work a subset addressed with only 28 features out of a total of 424 that reached a performance above 80% of F-measure for four of the six evaluated classes, showing that satisfactory classification performance can be achieved with a reduced number of enzymes's characteristics. The datasets and implementations are openly available.

关键词： Bioinformatics Proteins Enzyme class prediction Support vector machine Dimensionality reduction Feature selection multi-objective genetic algorithm

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Handling swarm of UAVs based on evolutionary multi-objective optimization

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2017年第3期6卷 263-274页

作者： Ramirez-Atencia, Cristian R-Moreno, Maria D. Camacho, David Univ Autonoma Madrid C Tomas & Valiente 11 E-28049 Madrid Spain Univ Alcala De Henares Carretera Madrid BarcelonaKm 33 600 Madrid 28871 Spain

The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime.

关键词： Unmanned aerial vehicles Mission planning Constraint satisfaction problems multi-objective genetic algorithm

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Towards sustainable living in high radiation cold climates: A two-phase genetic algorithm approach for residential building optimization

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BUILDING AND ENVIRONMENT 2024年 266卷

作者： Mo, Wensheng Yao, Xingbo Liu, Zu-An Chen, Shuo Li, Qianyu Jiang, Jiahao Zhang, Gaochuan Dewancker, Bart Julien Xuzhou Univ Technol Sch Civil Engn Xuzhou 221018 Peoples R China Univ Kitakyushu Fac Environm Engn Kitakyushu 8080135 Japan Xian Univ Technol Sch Art & Design Xian 710054 Peoples R China Xianyang Normal Univ Design Inst Xianyang 712099 Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu 8080135 Japan Zhejiang Univ Sci & Technol Sch Civil Engn & Architecture Engn Hangzhou 310023 Peoples R China

The High Radiation Cold (HRC) region's unique climatic conditions, characterized by high solar radiation and low air temperatures, present distinct challenges for optimizing building climate responsiveness. In this study, residential building design is bifurcated into two phases: building geometry (Phase I) and envelope design (Phase II). In Phase II, we propose a heterogeneous vertical envelope (HVE) design to suit HRC climates. This research framework establishes multiple objectives: energy efficiency, indoor comfort, and economy. It aims to identify a balanced and sustainable design by optimizing various parameters and materials using a multi-objective genetic algorithms(MOGA). The results indicate enhanced building adaptability in HAV climates through MOGA. The optimal solution in phase I results in an annual heating load of 59.12 W/m2, 33.38 % annual indoor visual comfort hours and the adaptive model predicts 35.59 % annual comfort hours. Incorporating phase II optimization, the total annual energy demand is reduced by 34.42 % with an 11.35 % improvement in thermal comfort hours, culminating at 37.8 %.

关键词： multi-objective genetic algorithm High radiation cold climate Energy-saving Thermal comfort Climate responsiveness

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A design approach of bonding head guiding platform for die to wafer hybrid bonding application using compliant mechanism

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2023年第6期17卷 2855-2866页

作者： Duc Nam Nguyen Minh Phung Dang Dixit, Saurav Thanh-Phong Dao Ind Univ Ho Chi Minh City Fac Mech Engn Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol & Educ Fac Mech Engn Ho Chi Minh City Vietnam Uttaranchal Univ Div Res & Innovat Dehra Dun 248007 Uttarakhand India Ton Duc Thang Univ Inst Computat Sci Div Computat Mechatron Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam

In semiconductor manufacturing process, the wafer direct bonding, so-called die-to-wafer hybrid bonding, still has unexpected voids on the boning surface which decreases the bonding efficiency. Therefore, this paper proposes a design approach for the bonding head guiding platform (BHGP) based on compliant mechanism for the die-to-wafer hybrid bonding. Firstly, a bridge amplification module (BAM) is designed and simulated. Secondly, a dual parallelogram module (DPM) is designed and analyzed. Then, the BAM is combined with the DPM to create a full design of the BHGP. To investigate the parametric effect of the geometric inputs on the output behaviors of the platform, the parameter sensitivity is conducted. The multi-objective optimization study is performed using multi-objective genetic algorithm. The results determined the displacement can gain up to 191.88 mu m, the parasitic error has a small value of 1.47 mu m, and the von Mises stress is minimal value of 52 MPa. The crosstalk of the platform is about 3.82%, and this ratio ensures a linear characteristic of the platform. A small value of stress allows a high safety factor during operation. The result of this study is expected be utilized for removing voids in the die-to-wafer hybrid bonding process.

关键词： Die-to-wafer hybrid bonding Bonding head guiding platform Bridge amplification module Dual parallelogram module multi-objective genetic algorithm

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Impact of predictor variables on the performance of future occupancy prediction: Feature selection using genetic algorithms and machine learning

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BUILDING AND ENVIRONMENT 2022年 219卷

作者： Esrafilian-Najafabadi, Mohammad Haghighat, Fariborz Concordia Univ Dept Bldg Civil & Environm Engn Energy & Environm Grp Montreal PQ Canada

This study analyzes the impact of employing different features on the performance of future occupancy prediction models. The aim is to identify the most effective predictor variables for occupancy models in order to enhance the prediction performance. To this end, a multi-objective genetic algorithm (MOGA) is proposed and employed to maximize accuracy while minimizing the number of features utilized in the model. A trade-off between the mentioned objectives is provided by using the technique for order of preference by similarity to ideal solution (TOPSIS). The performance of the MOGA is compared with that of a single-objective genetic algorithm, forward sequential selection, and backward sequential selection methodologies to assess the effectiveness of the proposed method. The results reveal that the MOGA provides superior performance and is able to improve the median accuracy by up to 4.81% from 70.94% for long-term occupancy prediction based on backward sequential feature selection while utilizing fewer features. Based on the TOPSIS method, no more than six features are required for developing the occupancy models with recent occupancy states and day of week selected as the essential features. In some cases, using recent CO2 levels, occupancy duration, lighting states, and previous occupancy states also shows a potential improvement in the prediction performance.

关键词： Optimal feature selection Sequential feature selection Machine learning multi-objective genetic algorithm Future occupancy prediction

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Calibration of building model based on indoor temperature for overheating assessment using genetic algorithm: Methodology, evaluation criteria, and case study

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BUILDING AND ENVIRONMENT 2022年第PartB期207卷 108518-108518页

作者： Baba, Fuad Mutasim Ge, Hua Zmeureanu, Radu Wang, Liangzhu (Leon) Concordia Univ Ctr Zero Energy Bldg Studies Dept Bldg Civil & Environm Engn Montreal PQ H3G 1M8 Canada

With the increased severity, intensity, and frequency of "heatwaves " due to climate change, it has become imperative to study the overheating risks in existing buildings. To do so, a building simulation model needs to be calibrated based on measured indoor temperatures under the current weather conditions. This paper presents a robust automated methodology that can calibrate a building simulation model based on the indoor hourly temperature in multiple rooms simultaneously with high accuracy. This methodology includes a variance-based sensitivity analysis to determine building parameters that significantly influence indoor air temperatures, the multi-objective genetic algorithm to calibrate different rooms simultaneously based on the significant parameters identified by the sensitivity analysis, and new evaluation criteria to achieve a high-accuracy calibrated model. Maximum Absolute Difference (MAD), a new metric, that calculates the maximum absolute difference between simulated and measured hourly indoor temperatures, Root Mean Square Error (RMSE), Normalized Mean Bias Error (NMBE) were used as the evaluation criteria. Another new metric is introduced, 1 degrees C Percentage Error criterion that calculates the percentage of the number of hours with an error over 1 degrees C during the calibration period, to select the best solutions from the Pareto Front solutions. 0.5 degrees C Percentage Error criterion is also used for the level of accuracy the model can achieve. It was found that the calibrated model achieved these metrics with RMSE of 0.3 degrees C, and MAD of 0.8 degrees C, and 85% of data points with an error less than 0.5 degrees C for a school building case.

关键词： multi-objective genetic algorithm Model calibration Indoor temperature Global sensitivity analysis Whole-building simulation

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LDA Pre-processing for Classification: Class-Dependent Single objective GA and multi-objective GA Approaches

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10th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2009)

作者： Khalidji, Modjtaba Moeinzadeh, Hossein Akbari, Ahmad Raahemi, Bijan Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9783642043932

Pre-processing of classification data can be helpful regardless of the type of classifier. The objective of this pre-processing step is to achieve a high degree of separation among classes before the classifier is trained or tested. This results into a trace ratio problem which is difficult to solve. Methods such as Linear Discriminant Analysis (LDA) have already been used for the solution of this problem by turning it into a simpler yet inexact problem. Also, in classical LDA, the covariances of different classes are assumed to be similar, which is not the case in real-world problems. In this paper, a class-dependent approach to finding the linear transformation is proposed. This method solves the trace ratio problem directly and also removes the requirement of similar covariance matrices. While giving good results, the method is computationally expensive. To reduce the computational cost while maintaining the benefits of the classdependent method, a multi-objective formulation is proposed and solved using NSGA-II. Simulation results show great improvement in classification using various classifiers.

关键词： Pre-processing Classification Linear Discriminate Analysis genetic algorithm multi-objective genetic algorithm

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Proportional integral controller gain tuning using real time digital simulation models and multi-objective optimization based co-simulation

Proportional integral controller gain tuning using real time...

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9th IFAC Symposium on Control of Power and Energy Systems (CPES 2015)

作者： Prabakar, Kumaraguru Li, Fangxing Univ Tennessee Knoxville TN USA

Proportional integral (PI) controller is a widely used controller strategy. It has two gain constants: proportional(k(p)) and integral(k(i)) gain constant. The dynamics of the PI controller depends on the values of the gain constant used. If gains of these control terms are not selected properly, instability Will occur. Gain tutting methodologies are available to tune these controllers to avoid instability. Hut, these gain tuning strategies and algorithms utilize simplified mathematical model of the plant ut order to obtain gain values. This approach might, not provide optimal gain values for models used in real time digital simulator. In the proposed approach, the algorithm utilizes the step response of the system to obtain optimal gain constants. The plant is modeled using the real time digital simulator (RIDS). A multi-objective optimization formulation is used to find the optimal gain constants. Results are presented here to support the effectiveness of proposed multi-objective gain tutting strategy. (C) 2015, IFAC (International Rderation or Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Proportional integral control Gain tuning multi-objective optimization multi-objective genetic algorithm

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