Procedia CIRP最新文献_第9页

Evaluating Life Cycle Sustainability Assessment Strategies in the EU Automotive Industry: Implications for Developing Countries 评估欧盟汽车工业生命周期可持续性评估策略：对发展中国家的影响

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.01.048

Ritesh Deore , Shipra Pandey

{"title":"Evaluating Life Cycle Sustainability Assessment Strategies in the EU Automotive Industry: Implications for Developing Countries","authors":"Ritesh Deore , Shipra Pandey","doi":"10.1016/j.procir.2025.01.048","DOIUrl":"10.1016/j.procir.2025.01.048","url":null,"abstract":"<div><div>The automotive industry is pivotal in shaping sustainable practices due to its significant environmental, economic, and social impacts. This paper investigates Life Cycle Sustainability Assessment (LCSA) Strategies within the European Union (EU) automotive sector and their implications for developing countries. By reviewing the literature, we identified seven LCSA strategies and six key sustainability impact factors: environmental impact, economic feasibility, social benefits, resource efficiency, innovation potential, and regulatory compliance. We employed the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis to evaluate and rank these strategies based on their effectiveness and applicability. The results indicate that while the EU’s advanced regulatory framework and technological capabilities facilitate the adoption of robust LCSA frameworks, developing countries face challenges such as limited infrastructure, financial constraints, and weaker regulatory mechanisms. However, the transfer of knowledge and technology from the EU to developing nations can bridge these gaps, fostering global sustainability in the automotive industry. This study underscores the necessity for tailored LCSA strategy that consider regional differences and encourages collaborative efforts to enhance sustainable practices worldwide. The findings provide valuable insights for policymakers, industry stakeholders, and researchers aiming to advance sustainability in the automotive sector.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"135 ","pages":"Pages 221-225"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-fidelity machine learning framework for life cycle assessment: a manufacturing case study on aluminum rolling 生命周期评估的多保真度机器学习框架：铝轧制制造案例研究

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2024.12.014

Muhammad Umar Farooq , Daniel Cooper

{"title":"Multi-fidelity machine learning framework for life cycle assessment: a manufacturing case study on aluminum rolling","authors":"Muhammad Umar Farooq , Daniel Cooper","doi":"10.1016/j.procir.2024.12.014","DOIUrl":"10.1016/j.procir.2024.12.014","url":null,"abstract":"<div><div>Manufacturing industries are increasingly focused on achieving sustainability targets, which has driven the development of environmental impact models often based on life cycle assessment (LCA) methods and databases. However, these databases tend to be too generic to ensure accurate modelling (e.g., using global or regional average impact values per unit of mass processed). To improve accuracy, companies can generate customized data inventories through experiments or simulations, but these approaches are typically costly, time-consuming, and may disrupt daily operations. This article introduces a partial physics-based, multi-fidelity machine learning approach to generate low-cost, environmental impact models tailored to specific manufacturing systems. The framework uses reduced-order, low-fidelity, physics-based models to capture the process dynamics, followed by transfer learning with small volumes of high-fidelity (e.g., experimental) data. This allows for accurate gate-to-gate environmental impact predictions without the need for extensive experimental campaigns. The framework is demonstrated on a lab-scale metal rolling mill for predicting power consumption in gate-to-gate assessments. A simple slab analysis metal forming model trains the base learner, and adaptive boosting is used for transfer learning on experimental data. The framework achieved superior performance, requiring 13% less experimental data than a standalone machine learning model of the same accuracy trained solely on experimental data. This approach may offer a cost-effective solution for generating accurate predictive models in scenarios where data collection is challenging, either due to rigid use of standard process settings or data collection cost and time constraints.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"135 ","pages":"Pages 181-186"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian Optimal Experimental Design for Intelligent Data Collection in Material Flow Analysis 物流分析中智能数据采集的贝叶斯优化实验设计

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.128

Jiankan Liao , Xun Huan , Daniel Cooper

{"title":"Bayesian Optimal Experimental Design for Intelligent Data Collection in Material Flow Analysis","authors":"Jiankan Liao , Xun Huan , Daniel Cooper","doi":"10.1016/j.procir.2025.02.128","DOIUrl":"10.1016/j.procir.2025.02.128","url":null,"abstract":"<div><div>Material flow analyses (MFAs) are powerful tools for identifying and analyzing energy and material efficiency (resource efficiency) opportunities across a supply chain. MFAs are typically represented as directed graphs with key parameters including the mass of the material flows entering the system and the allocation of materials flowing through one node (typically representing a process or location) to other nodes in the system. Parametric uncertainty can hamper the credibility and usability of MFA results. Uncertainty may be reduced by collecting more data; however, an intelligent data acquisition strategy is needed given the limited resources available for completing a given MFA. In this article, we apply Bayesian optimal experimental design (BOED) derived from the Kullback-Leibler divergence to target the collection of high value data, which is then fed into a Bayesian framework to effectively reduce the MFA parametric uncertainty. The methodology is demonstrated using a case study on the 2012 U.S. steel sector. Bayesian inference is then used to validate the BOED results with data collected from the United States Geological Survey and the World Steel Association. This article’s methods allow efficient data collection to rapidly create MFAs with reduced and quantified parametric uncertainty, aiding decision makers in their efforts to pursue resource efficiency.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"135 ","pages":"Pages 175-180"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards trajectory-based latent space control for human-robot collaboration 基于轨迹的人机协作潜在空间控制研究

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.143

Raza Saeed, Tadele Belay Tuli, Michael Weikum, Martin Manns

{"title":"Towards trajectory-based latent space control for human-robot collaboration","authors":"Raza Saeed, Tadele Belay Tuli, Michael Weikum, Martin Manns","doi":"10.1016/j.procir.2025.02.143","DOIUrl":"10.1016/j.procir.2025.02.143","url":null,"abstract":"<div><div>In manual assembly, human-robot collaboration (HRC) research activities aim to improve the manual handling of parts. For this purpose, latent space control has been investigated by several research groups. However, control problems have shown limited applicability for various frame-based latent space models. The objective of this work is to determine if positional control is principally possible for trajectory-based latent spaces. Therefore, we investigate how ‘reach’ trajectories that are constrained in the target position map to a latent space that has been derived from functional principal component analysis (fPCA). A multivariate normality test is conducted to analyze the target-constrained position in the latent space, followed by mapping between the latent and Cartesian spaces. Results show that for linear shifts in the target ‘reach’ position, Gaussian modes in the latent space move along a vector in the latent space. Additionally, Gaussian mixtures occur at the target point where different motion styles are chosen for reaching. Results suggest that positional control may prove feasible for the considered type of latent space.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"134 ","pages":"Pages 431-436"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Condition monitoring of machine tool feed drives in aero engine manufacturing 航空发动机制造中机床进给传动的状态监测

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.03.026

Maximilian Benker , Christian Zeller

引用次数: 0

A Knowledge Based System for Intelligent Planning of e-motors Remanufacturing Activities 基于知识的电机再制造活动智能规划系统

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.03.063

Matteo Zanovello , Tullio Tolio , Maria Chiara Magnanini

引用次数: 0

Normal Behavior Modelling for the Mixing Process in Battery Cell Production 电池生产中混合过程的正常行为建模

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.171

Antje Fitzner , Thomas Ackermann , Melanie Lübke

引用次数: 0

Interoperability requirements for Zero Defect Manufacturing within Industry4.0 工业4.0中零缺陷制造的互操作性要求

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.167

John Lindström , Petter Kyösti , Wolfgang Birk , Erik Lejon , Germar Schneider , Alf Andersson , Oskar Lindblad , Jens Eliasson , Karl Andersson

引用次数: 0

Converting Relational Databases to Manufacturing Knowledge Graph for Product Quality Tracing in Additive Manufacturing 面向增材制造产品质量跟踪的关系数据库到制造知识图谱的转换

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.172

Laiyi Li , Maolin Yang , Pingyu Jiang

{"title":"Converting Relational Databases to Manufacturing Knowledge Graph for Product Quality Tracing in Additive Manufacturing","authors":"Laiyi Li , Maolin Yang , Pingyu Jiang","doi":"10.1016/j.procir.2025.02.172","DOIUrl":"10.1016/j.procir.2025.02.172","url":null,"abstract":"<div><div>Product quality tracing (PQT) in additive manufacturing (AM) is a significant and complex challenge crucial for fully understanding the processes, tracing quality issues, and optimizing process technologies. Typically, tracing relies on production data from historical relational databases, but these data lack semantic information and integration with the service context of manufacturing processes. A manufacturing knowledge graph (MKG) offers a solution by adding manufacturing semantic information to production data. This paper constructed a PQT model and an MKG database for AM. The PQT model and the schema layer of the MKG originate from the swimlane flowchart. Subsequently, a relational database was mapped to instantiate the MKG, and the natural language model was used to complete it. This integration constructs a PQT model and an MKG database centered on manufacturing resources, events, data, and states. The tracing subgraphs extracted from the MKG database demonstrate that this method effectively traces manufacturing process issues and anomalies. The proposed method provides a viable database and method for PQT in AM, enabling comprehensive tracing of manufacturing processes, identifying quality anomalies, and providing potential cause references for AM experts.</div></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":"134 ","pages":"Pages 544-549"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental setup for motion capturing two humans jointly handling an object 动作捕捉两个人共同处理一个物体的实验装置

Procedia CIRP Pub Date : 2025-01-01 DOI: 10.1016/j.procir.2025.02.145

Raza Saeed, Tadele Belay Tuli, Martin Manns

引用次数: 0