Article
Engineering, Industrial
Shimin Liu, Jinsong Bao, Yuqian Lu, Jie Li, Shanyu Lu, Xuemin Sun
Summary: High-performance aerospace component manufacturing requires strict quality control, and Digital Twin technology can be used to optimize machining strategies. A Digital Twin modeling method based on biomimicry principles was proposed to adaptively construct a multi-physics Digital Twin of the machining process.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Sheng Dai, Gang Zhao, Yong Yu, Pai Zheng, Qiangwei Bao, Wei Wang
Summary: The Digital Twin concept is significant in the Industry 4.0 era, with the key technology being the information modeling of physical products. This paper addresses modeling challenges in the machining process and proposes an ontology-based information modeling method for creating Digital Twins of as-fabricated parts. The proposed methodology is effective and validated through a case study in an aviation manufacturing plant.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2021)
Article
Computer Science, Interdisciplinary Applications
Tongming Xu, Jianxun Li, Zhuoning Chen
Summary: This study investigates a systematic AFR method for machining parts based on process semantics, which converts the part MBD model to a structuralized feature model (SFM). It provides a new definition of machining features and proposes detailed data extraction and preprocessing methods. The study also presents a new classification methodology for identifying feature geometries and discusses the recognition methods for high-level composite features. The results of feature recognition can be directly applied for downstream machining process planning.
COMPUTERS IN INDUSTRY
(2022)
Article
Computer Science, Artificial Intelligence
Jinfeng Liu, Xiaojian Wen, Honggen Zhou, Sushan Sheng, Peng Zhao, Xiaojun Liu, Chao Kang, Yu Chen
Summary: This paper presents a multidimensional modeling approach for machining processes using Digital Twin technology, which supports the design and execution phases of intelligent machining. The effectiveness of the applied framework and the proposed method is verified through testing key components of diesel engines.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Automation & Control Systems
Yayun Liu, Jianxin Deng
Summary: The study investigates the material removal mechanisms and cutting force in machining green alumina ceramics using mechanical model and DEM. Experimental results validate good agreement between the two models in chip formation and tangential cutting force. Friction force contributes more than 50% of the tangential cutting force, leading to serious flank wear of the cutting tool.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2021)
Review
Chemistry, Multidisciplinary
Aleksandar Kondinski, Jiaru Bai, Sebastian Mosbach, Jethro Akroyd, Markus Kraft
Summary: Knowledge engineering is the process of converting human knowledge into machine-readable form, which is the foundation of artificial intelligence and knowledge engineering. In the field of chemistry, knowledge engineering began with the development of expert systems that simulate the thinking process of chemists. With the help of tools like semantic webs, knowledge graphs, and cognitive software agents, more complex systems can now be represented, and new knowledge can be synthesized.
ACCOUNTS OF CHEMICAL RESEARCH
(2023)
Article
Construction & Building Technology
Lu Jia, Yanfeng Jin, Yang Liu, Jing Lv
Summary: This paper introduces an ontological method for information modeling and management of the construction process. The proposed method uses machine readable language to integrate process knowledge in a structured way, improving the relevance of information and promoting knowledge reuse, sharing, and retrieval. It also utilizes semantic web rule language (SWRL) to model relevant laws and regulations, ensuring the compliance of building products and the accuracy of process information.
Review
Thermodynamics
Wuyi Ming, Shengfei Zhang, Guojun Zhang, Jinguang Du, Jun Ma, Wenbin He, Chen Cao, Kun Liu
Summary: This study provides a systematic review of the progress in modelling the electrical discharge machining (EDM) process. It compares and analyzes the existing finite element method (FEM) models, molecular dynamics (MD) models, and flow field models, and suggests future research areas of interest.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2022)
Article
Engineering, Mechanical
Lingrong Kong, Yu Wang, Xin Lei, Chao Feng, Zhiqiao Wang
Summary: This paper presents the integral modeling process of abrasive water jet micro-machining (AWJM), connecting the three-phase fluid dynamics model inside the nozzle with the surface evolution model by theoretically calculating the jet erosive efficacy distribution. Several improvements over previously presented models, such as taking into account the divergence angle of abrasive particles and the effect of the nonuniform distribution of each phase on the jet erosive efficacy, have been made in this study. The model successfully predicted the surface evolution of micro-channel profiles in 316L stainless steel and 6061-T6 aluminium, showing that the erosive efficacy of 6061-T6 is two orders of magnitude higher than that of 316L.
Article
Computer Science, Artificial Intelligence
Chen Ding, Fei Qiao, Juan Liu, Dongyuan Wang
Summary: This study proposes a knowledge graph modeling method for the product manufacturing process, which uses deep learning and reasoning rules to automatically extract knowledge from manufacturing data and infer new knowledge, providing rapid and accurate manufacturing knowledge for the demanders.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Menglei Zheng, Ling Tian
Summary: With the advancement of information and communication technology, a significant amount of data is generated during the lifecycle of mechanical products. Digital twins offer a method to integrate and rebuild this data into a virtual model, providing a comprehensive data management approach that can be applied throughout the entire product lifecycle.
Article
Engineering, Manufacturing
Jian Zhang, Sugrim Sagar, Tejsh Dube, Xuehui Yang, Hyunhee Choi, Yeon-Gil Jung, Dan Daehyun Koo, Jing Zhang
Summary: This work presents a new discrete element model (DEM) for simulating the machining process of thermal barrier coatings. The effects of cutting depth and cutting speed on cutting force and chip morphology are studied. The results show a transition from ductile mode to brittle mode as the cutting depth increases, and the critical cutting depth is determined based on fracture criterion. The cutting force is found to be correlated with cutting depth and the period of the cutting force is consistent with the diameter of the column grain. Cutting speed has little effect on cutting force and chip morphology due to no strain rate sensitivity.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2022)
Article
Engineering, Manufacturing
H. A. Kishawy, W. Ahmed, A. Mohany
Summary: This paper investigates the use of rotary cutting tools for machining difficult materials, presenting an analytical-based model to predict cutting forces and tool rotational speed. The model is validated using previously published experimental results.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Manufacturing
Zhan Wang, Sheng-Wen Zhang, Nan Wang, Jing-Ying Xu, De-Jun Cheng
Summary: This paper proposes a feature-based assembly information model to address the issues in complex product assembly design, and verifies its performance and applicability.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE
(2023)
Article
Automation & Control Systems
Yuanhao Fan, Junxue Ren, Zihua Hu, Yiran Tang
Summary: This paper discusses the causes of thread milling error and establishes the machining error model. The model is proven through thread milling experiments, showing good agreement with the experimental results.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Francesco Pistolesi, Michele Baldassini, Beatrice Lazzerini
Summary: More than one in four workers worldwide suffer from back pain, resulting in the loss of 264 million work days annually. In the U.S., it costs $50 billion in healthcare expenses each year, rising up to $100 billion when accounting for decreased productivity and lost wages. The impending Industry 5.0 revolution emphasizes worker well-being and their rights, such as privacy, autonomy, and human dignity. This paper proposes a privacy-preserving artificial intelligence system that monitors the posture of assembly line workers. The system accurately assesses upper-body and lower-body postures while respecting privacy, enabling the detection of harmful posture habits and reducing the likelihood of musculoskeletal disorders.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Xavier Boucher, Camilo Murillo Coba, Damien Lamy
Summary: This paper explores the new business strategies of digital servitization and smart PSS delivery, and develops conceptual prototypes of smart PSS value offers for early stages of the design process. It presents the development and experimentation of a modelling language and toolkit, and applies it to the design of a smart PSS in the field of heating appliances.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Dieudonne Tchuente, Jerry Lonlac, Bernard Kamsu-Foguem
Summary: Artificial Intelligence (AI) is becoming increasingly important in various sectors of society. However, the black box nature of most AI techniques such as Machine Learning (ML) hinders their practical application. This has led to the emergence of Explainable artificial intelligence (XAI), which aims to provide AI-based decision-making processes and outcomes that are easily understood, interpreted, and justified by humans. While there has been a significant amount of research on XAI, there is currently a lack of studies on its practical applications. To address this research gap, this article proposes a comprehensive review of the business applications of XAI and a six-step framework to improve its implementation and adoption by practitioners.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Francois-Alexandre Tremblay, Audrey Durand, Michael Morin, Philippe Marier, Jonathan Gaudreault
Summary: Continuous high-frequency wood drying, integrated with a traditional wood finishing line, improves the value of lumber by correcting moisture content piece by piece. Using reinforcement learning for continuous drying operation policies outperforms current industry methods and remains robust to sudden disturbances.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Luyao Xia, Jianfeng Lu, Yuqian Lu, Wentao Gao, Yuhang Fan, Yuhao Xu, Hao Zhang
Summary: Efficient assembly sequence planning is crucial for enhancing production efficiency, ensuring product quality, and meeting market demands. This study proposes a dynamic graph learning algorithm called assembly-oriented graph attention sequence (A-GASeq), which optimizes the assembly graph structure to guide the search for optimal assembly sequences. The algorithm demonstrates superiority and broad utility in real-world scenarios.
COMPUTERS IN INDUSTRY
(2024)
Article
Computer Science, Interdisciplinary Applications
Mutahar Safdar, Padma Polash Paul, Guy Lamouche, Gentry Wood, Max Zimmermann, Florian Hannesen, Christophe Bescond, Priti Wanjara, Yaoyao Fiona Zhao
Summary: Metal-based additive manufacturing can achieve fully dense metallic components, and the application of machine learning in this field has been growing rapidly. However, there is a lack of framework to manage these machine learning models and guidance on the fundamental requirements for a cross-disciplinary platform to support process-based machine learning models in industrial metal AM.
COMPUTERS IN INDUSTRY
(2024)