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Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-23 Chunyang Xia, Zengxi Pan, Joseph Polden, Huijun Li, Yanling Xu, Shanben Chen
WAAM has been proven a promising alternative to fabricate medium and large scale metal parts with a high depositing rate and automation level. However, the production quality may deteriorate due to the poor deposited layer surface quality. In this paper, a laser sensor based surface roughness measuring method was developed for WAAM. To improve the surface integrity of deposited layers by WAAM, different
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Diagnostics of industrial equipment and faults prediction based on modified algorithms of artificial immune systems J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-22 Galina Samigulina, Zarina Samigulina
Nowadays, industrial enterprises are equipped with sophisticated equipment, diagnostics and prediction of the state of which is an urgent task. The article presents the developed system for diagnostics of industrial equipment based on the methodology for analyzing failure modes, their influence and the degree of AMDEC criticality (l'Analyse des Modes de Défaillances, de leurs Effets et de leur Criticité)
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A blockchain technology based trust system for cloud manufacturing J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-22 Reza Vatankhah Barenji
Cloud manufacturing (CM) is a new networked manufacturing model that delivers various on-demand manufacturing capabilities to the consumers from the providers. In this model, the provider and consumer never meet each other, thus “trust” is the major enabler for starting a collaboration. In another word, a user must be sure that the requested capability will not be provided with malicious results, and
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An automatic calibration algorithm for laser vision sensor in robotic autonomous welding system J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-11 Runquan Xiao, Yanling Xu, Zhen Hou, Chao Chen, Shanben Chen
Visual sensor plays an important part in intelligentized welding systems, and the calibration of the vision sensor is the indispensable part of visual systems. Aiming at the problem of the tedious calibration process, this paper describes an automatic calibration algorithm. First, the robot motion equation and the motion range constraint equation are proposed to ensure that the collected images of
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Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-11 Michael D. T. McDonnell, Daniel Arnaldo, Etienne Pelletier, James A. Grant-Jacob, Matthew Praeger, Dimitris Karnakis, Robert W. Eason, Ben Mills
Interactions between light and matter during short-pulse laser materials processing are highly nonlinear, and hence acutely sensitive to laser parameters such as the pulse energy, repetition rate, and number of pulses used. Due to this complexity, simulation approaches based on calculation of the underlying physical principles can often only provide a qualitative understanding of the inter-relationships
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Multi-robot multi-operator collaborative assembly systems: a performance evaluation model J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-08 Giovanni Boschetti, Matteo Bottin, Maurizio Faccio, Riccardo Minto
In the last decade, collaborative assembly systems (CAS) are becoming increasingly common due to their ability to merge the flexibility of a manual assembly system with the performance of traditional robotics. Technical constraints, e.g., dedicated tools or resources, or performance requirements, e.g., throughput, could encourage the use of a CAS built around a multi-robot and multi-operator layout
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Prediction of the parameters affecting the performance of compact heat exchangers with an innovative design using machine learning techniques J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-07 Sinan Uguz, Osman Ipek
In this study, the innovative compact heat exchanger (CHE) newly designed and manufactured using metal additive manufacturing technology were numerically and experimentally investigated. Some experiments were carried out to determine the hot water (\(hw\)) and cold water (\(cw\)) outlet temperatures of CHE. As a result of the CFD analysis, the average outlet temperatures of the \(hw\) and \(cw\) flow
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Serial number inspection for ceramic membranes via an end-to-end photometric-induced convolutional neural network framework J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-07 Feiyang Li, Nian Cai, Xueliang Deng, Jiahao Li, Jianfa Lin, Han Wang
The ceramic membrane plays an important role in the wastewater disposal industry. The serial number engraved on each ceramic membrane is an essential feature for identification. Here, an automatic inspection system for serial numbers of ceramic membranes is proposed to replace the manual inspection. To the best of our knowledge, this is the first attempt to automatically inspect serial numbers of ceramic
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Spatial–temporal out-of-order execution for advanced planning and scheduling in cyber-physical factories J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-05 Mingxing Li, Ray Y. Zhong, Ting Qu, George Q. Huang
Cyber-Physical System (CPS) is one of the most promising directions of Industry 4.0 smart manufacturing. Abundant manufacturing data and information are available for decision-makers in real-time thanks to the application of various frontier technologies in CPS. However, the inherent complexity and uncertainty of manufacturing optimization still plague scholars and practitioners and impede further
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A novel approach in selective assembly with an arbitrary distribution to minimize clearance variation using evolutionary algorithms: a comparative study J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-05 Lenin Nagarajan, Siva Kumar Mahalingam, Jayakrishna Kandasamy, Selvakumar Gurusamy
The minimization of surplus components with normal dimensional distributions while making selective assemblies was the only objective considered in the previous research works carried out by various researchers in different periods. Seldom works have been found on selective assembly by considering all dimensional distributions. In this proposed work, a novel method is developed for making assemblies
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A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-05 Konstantinos Mykoniatis, Gregory A. Harris
Virtual commissioning is a key technology in Industry 4.0 that can address issues faced by engineers during early design phases. The process of virtual commissioning involves the creation of a Digital Twin—a dynamic, virtual representation of a corresponding physical system. The digital twin model can be used for testing and verifying the control system in a simulated virtual environment to achieve
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An innovative hybrid algorithm for bound-unconstrained optimization problems and applications J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-05 Raghav Prasad Parouha, Pooja Verma
Particle swarm optimization (PSO) and differential evolution (DE) are two efficient meta-heuristic algorithms, achieving excellent performance in a wide variety of optimization problems. Unfortunately, when both algorithms are used to solve complex problems then they inevitably suffer from stagnation, premature convergence and unbalanced exploration–exploitation. Hybridization of PSO and DE may provide
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MWRSPCA: online fault monitoring based on moving window recursive sparse principal component analysis J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-04 Jinping Liu, Jie Wang, Xianfeng Liu, Tianyu Ma, Zhaohui Tang
This paper proposes a moving window recursive sparse principal component analysis (MWRSPCA)-based online fault monitoring scheme, aim at providing an online fault monitoring solution for large-scale complex industrial processes (e.g., chemical industry processes) with time-varying and dynamically changing characteristics. It establishes a sparse principal component analysis (SPCA) model based on the
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A novel tolerance geometric method based on machine learning J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-03 Lu-jun Cui, Man-ying Sun, Yan-long Cao, Qi-jian Zhao, Wen-han Zeng, Shi-rui Guo
In most cases, designers must manually specify geometric tolerance types and values when designing mechanical products. For the same nominal geometry, different designers may specify different types and values of geometric tolerances. To reduce the uncertainty and realize the tolerance specification automatically, a tolerance specification method based on machine learning is proposed. The innovation
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Regenerative scheduling problem in engineer to order manufacturing: an economic assessment J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-03 R. Micale, C. M. La Fata, M. Enea, G. La Scalia
The dynamic production scheduling is a very complex process that may arise from the occurrence of unpredictable situations such as the arrival of new orders besides the ones already accepted. As a consequence, companies may often encounter several difficulties to make decisions about the new orders acceptance and sequencing along with the production of the existing ones. With this recognition, a mathematical
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Comprehensive learning Jaya algorithm for engineering design optimization problems J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-03 Yiying Zhang, Zhigang Jin
Jaya algorithm (JAYA) is a recently developed metaheuristic algorithm for global optimization problems. JAYA has a very simple structure and only needs the essential population size and terminal condition for solving optimization problems. However, JAYA is easy to get trapped in the local optimum for solving complex global optimization problems due to its single learning strategy. Motivated by this
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Mathematization of experts knowledge: example of part orientation in additive manufacturing J. Intell. Manuf. (IF 4.311) Pub Date : 2021-01-03 Mouhamadou Mansour Mbow, Christelle Grandvallet, Frederic Vignat, Philippe Rene Marin, Nicolas Perry, Franck Pourroy
The use of expert knowledge by manufacturing companies to support everyday activities has become an emerging practice thanks to the new knowledge management tools. A big set of knowledge is available in the organizations but its profitable use to solve problems and assist decision making is still a challenge. This is the case of CAM operations or preprocessing steps for which various works have been
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In-situ identification of material batches using machine learning for machining operations J. Intell. Manuf. (IF 4.311) Pub Date : 2020-12-26 Benjamin Lutz, Dominik Kisskalt, Andreas Mayr, Daniel Regulin, Matteo Pantano, Jörg Franke
In subtractive manufacturing, differences in machinability among batches of the same material can be observed. Ignoring these deviations can potentially reduce product quality and increase manufacturing costs. To consider the influence of the material batch in process optimization models, the batch needs to be efficiently identified. Thus, a smart service is proposed for in-situ material batch identification
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Machine learning integrated design for additive manufacturing J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-23 Jingchao Jiang, Yi Xiong, Zhiyuan Zhang, David W. Rosen
For improving manufacturing efficiency and minimizing costs, design for additive manufacturing (AM) has been accordingly proposed. The existing design for AM methods are mainly surrogate model based. Due to the increasingly available data nowadays, machine learning (ML) has been applied to medical diagnosis, image processing, prediction, classification, learning association, etc. A variety of studies
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Gear and bearing fault classification under different load and speed by using Poincaré plot features and SVM J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-23 Rubén Medina, Jean Carlo Macancela, Pablo Lucero, Diego Cabrera, René-Vinicio Sánchez, Mariela Cerrada
This paper describes two algorithms for feature extraction from the Poincaré plot which is constructed with the vibration signals measured in roller bearings and gearboxes. The extracted features are used for classifying 10 types of fault conditions in a gearbox and 7 types of fault conditions a roller bearings. Both vibration signal datasets were acquired at different loads and speeds. The feature
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Mathematical modeling and a hybrid evolutionary algorithm for process planning J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-23 Qihao Liu, Xinyu Li, Liang Gao
Process planning is an essential part of the manufacturing system linking the designing and practical manufacturing. However, the reported process planning models are too simple to describe all characteristics because of the complexity of process planning. Therefore, a new mixed-integer linear programming (MILP) mathematical model is established based on OR-node of the network graph. In the model,
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Pitfalls and protocols of data science in manufacturing practice J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-23 Chia-Yen Lee, Chen-Fu Chien
Driven by ongoing migration for Industry 4.0, the increasing adoption of artificial intelligence, big data analytics, cloud computing, Internet of Things, and robotics have empowered smart manufacturing and digital transformation. However, increasing applications of machine learning and data science (DS) techniques present a range of procedural issues including those that involved in data, assumptions
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Dispatching method based on particle swarm optimization for make-to-availability J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-22 Robson Flavio Castro, Moacir Godinho-Filho, Roberto Fernandes Tavares-Neto
Make-to-availability (MTA) is a subtype of make-to-stock that emerged from production, planning, and control system, simplified drum-buffer-rope (S-DBR). The dispatching production order logic of the MTA does not consider the elements present in a wide range of manufacturing systems, such as sequence-dependent setup time. These characteristics generally creates difficulties in the S-DBR, thereby worsening
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Synthetic data augmentation for surface defect detection and classification using deep learning J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-18 Saksham Jain, Gautam Seth, Arpit Paruthi, Umang Soni, Girish Kumar
Deep learning techniques, especially Convolutional Neural Networks (CNN), dominate the benchmarks for most computer vision tasks. These state-of-the-art results are typically obtained through supervised learning, for which large annotated datasets are required. However, acquiring such datasets for manufacturing applications remains a challenging proposition due to the time and costs involved in their
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Prediction of cell viability in dynamic optical projection stereolithography-based bioprinting using machine learning J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-16 Heqi Xu, Qingyang Liu, Jazzmin Casillas, Mei Mcanally, Noshin Mubtasim, Lauren S. Gollahon, Dazhong Wu, Changxue Xu
Stereolithography (SLA)-based bioprinting can fabricate three-dimensional complex objects accurately and efficiently. However, the ultraviolet (UV) irradiation in the SLA-based bioprinting process is a significant challenge, which may damage the cells. Physics-based models are not able to predict cell viability with high accuracy because of the complexity of cell biological structures and cell recovery
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An effective approach for the dual-resource flexible job shop scheduling problem considering loading and unloading J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-13 Xiuli Wu, Junjian Peng, Xiao Xiao, Shaomin Wu
Many manufacturing systems need more than one type of resource to co-work with. Commonly studied flexible job shop scheduling problems merely consider the main resource such as machines and ignore the impact of other types of resource. As a result, scheduling solutions may not put into practice. This paper therefore studies the dual resource constrained flexible job shop scheduling problem when loading
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A real-time defective pixel detection system for LCDs using deep learning based object detectors J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-13 Aslı Çelik, Ayhan Küçükmanisa, Aydın Sümer, Aysun Taşyapı Çelebi, Oğuzhan Urhan
The presence of pixel defects on the screens of LCD-based products (TV, tablet, phone, etc.) is unacceptable given the consumer expectations. Therefore, these defects should be detected before the product reaches the user during the production stage. Visual inspections are mostly performed by human operators in the production. These inspections are error prone and not efficient in terms of consumed
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Tolerance analysis based on Monte Carlo simulation: a case of an automotive water pump design optimization J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-13 Eduardo Umaras, Ahmad Barari, Marcos Sales Guerra Tsuzuki
Successful products are those presenting the highest quality at a fair cost. Although different approaches can be used to define the concept of quality, functional reliability is always a major requirement, due to implications such as safety and user losses regarding maintenance expenses, and product availability. Intelligent designs are robust and result in a fair cost. Robust designs are those insensitive
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The optimization of the control logic of a redundant six axis milling machine J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-12 Antonio Caputi, Davide Russo
The primary task of machine tools is simultaneously positioning and orienting the cutting tool with respect to the work piece. The mechanism must avoid positioning errors, and limit forces and torques required to the motors. A novel approach for combined design and control of manufacturing means is proposed in this work. The focus is on the optimization of the control logic of a redundant 6 axis milling
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Implementation of a novel algorithm of wheelset and axle box concurrent fault identification based on an efficient neural network with the attention mechanism J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-11 Dechen Yao, Hengchang Liu, Jianwei Yang, Jiao Zhang
With the rapid development of urban rail transit in recent years, it becomes necessary to ensure the operation safety of train wheelset axle boxes. Aiming at the problems of large model size and long diagnosis time in traditional fault diagnosis methods, this paper proposed a novel model to identify concurrent faults in wheelset axle boxes based on an efficient neural network and the attention mechanism
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Deep prototypical networks based domain adaptation for fault diagnosis J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-11 Huanjie Wang, Xiwei Bai, Jie Tan, Jiechao Yang
Due to the existence of domain shifts, the distributions of data acquired from different machines show significant discrepancies in industrial applications, which leads to performance degradation of traditional machine learning methods. In this paper, we propose a novel method that combines supervised domain adaptation with prototype learning for fault diagnosis. The proposed method consists of two
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A digital twins concept model for integrated maintenance: a case study for crane operation J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-07 Janusz Szpytko, Yorlandys Salgado Duarte
The paper presents an Integrated Maintenance Decision Making Model (IMDMM) concept for cranes under operation especially into the container type terminals. The target is to improve cranes operational efficiency through minimizing the risk of the Gantry Cranes Inefficiency (GCI) results based on the implementation of the Digital Twins concept for maintenance purposes. The proposed model makes a joint
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A novel hybrid immune clonal selection algorithm for the constrained corridor allocation problem J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-06 Junqi Liu, Zeqiang Zhang, Feng Chen, Silu Liu, Lixia Zhu
Aiming at the lack of relevant research on relationship constraints between facilities in the corridor allocation problem (CAP). In this paper, fixed position constraints and ordering constraints are considered in CAP, and the logistics cost is minimized. Considering that the existing search technology is complicated and time-consuming in dealing with such constrained CAP (cCAP), and immune clone selection
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Surface roughness stabilization method based on digital twin-driven machining parameters self-adaption adjustment: a case study in five-axis machining J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-05 Zengya Zhao, Sibao Wang, Zehua Wang, Shilong Wang, Chi Ma, Bo Yang
Surface roughness, which has a significant influence on fatigue strength and wear resistance, is an important technical parameter. In practical machining, it is unstable and may be larger than the acceptable surface roughness due to unstable machining process. This will seriously deteriorate the surface performance of the workpieces. Therefore, an effective surface roughness stabilization method is
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RUL prediction for automatic machines: a mixed edge-cloud solution based on model-of-signals and particle filtering techniques J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-04 Matteo Barbieri, Khan T. P. Nguyen, Roberto Diversi, Kamal Medjaher, Andrea Tilli
This work aims to provide useful insights into the course of action and the challenges faced by machine manufacturers when dealing with the actual application of Prognostics and Health Management procedures in industrial environments. Taking into account the computing capabilities and connectivity of the hardware available for smart manufacturing, we propose a particular solution that allows meeting
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Intelligent bearing structure and temperature field analysis based on finite element simulation for sustainable and green manufacturing J. Intell. Manuf. (IF 4.311) Pub Date : 2020-11-04 Jinhai Chen, Wenyuan Zhang, Heng Wang
Intelligent manufacturing is a new mode and trend of sustainable manufacturing development. It optimizes the design and manufacturing process of products and greatly reduces the consumption of resources and energy by virtue of the huge potential of computer modeling and simulation and information communication technology. Under the background of intelligent manufacturing, intelligent bearing is proposed
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Digital twin of functional gating system in 3D printed molds for sand casting using a neural network J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-29 Ahmed Ktari, Mohamed El Mansori
The filling stage is a critical phenomenon in sand casting for making reliable castings. Latest research has demonstrated that for most liquid engineering alloys, the critical meniscus velocity of the melt at the ingate is in the range of 0.4–0.6 m s−1. The work described in this research paper is to use neural network (NN) technology to propose digital twin approach for gating system design that allow
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An estimation distribution algorithm for wave-picking warehouse management J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-29 Jingran Liang, Zhengning Wu, Chenye Zhu, Zhi-Hai Zhang
Recently, market has witnessed a tremendous growth in E-commerce sales, which bring tons of opportunities as well as challenges. Warehouses have to handle unique characteristics of customer orders in the era of E-commerce which consists of small order scales, large items count, unexpected irregular order arrival patterns, seasonality demand peeks, and high service level expectations. Warehouses are
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Machine learning model to predict welding quality using air-coupled acoustic emission and weld inputs J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-27 Kaiser Asif, Lu Zhang, Sybil Derrible, J. Ernesto Indacochea, Didem Ozevin, Brian Ziebart
Weld evaluation processes are usually conducted in the post-weld stage. In this way, defects are found after the weld is completed, often resulting in disposal of expensive material or lengthy repair processes. Simultaneously, weld quality inspections tend to be performed manually by a human, even for an automated weld. Therefore, a proper real-time weld quality monitoring method associated with a
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Bagging for Gaussian mixture regression in robot learning from demonstration J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-26 Congcong Ye, Jixiang Yang, Han Ding
Robot learning from demonstration (LfD) emerges as a promising solution to transfer human motion to the robot. However, because of the open-loop between the learner and task constraints, the precision of the reproduction at the desired task constraints cannot always be guaranteed and the model is not robust to changes of the training data. This paper proposes a closed-loop framework of LfD based on
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Prescribed performance fuzzy back-stepping control of a flexible air-breathing hypersonic vehicle subject to input constraints J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-22 Hanqiao Huang, Chang Luo, Bo Han
The design of prescribed performance fuzzy back-stepping tracking control for a flexible air-breathing hypersonic vehicle (FAHV) with actuator constraints is discussed. Fuzzy logic systems (FLSs) are applied to approximate the lumped uncertainty of each subsystem of the FAHV model. Every FLS contains only one adaptive parameter that needs to be updated online with a minimal-learning-parameter scheme
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Detecting voids in 3D printing using melt pool time series data J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-22 Vivek Mahato, Muhannad Ahmed Obeidi, Dermot Brabazon, Pádraig Cunningham
Powder Bed Fusion (PBF) has emerged as an important process in the additive manufacture of metals. However, PBF is sensitive to process parameters and careful management is required to ensure the high quality of parts produced. In PBF, a laser or electron beam is used to fuse powder to the part. It is recognised that the temperature of the melt pool is an important signal representing the health of
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A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-22 Shaohua Huang, Yu Guo, Nengjun Yang, Shanshan Zha, Daoyuan Liu, Weiguang Fang
Accurate anomaly detection is the premise of production process control and normal execution of production plan. The implementation of Internet of Things (IoT) provides data foundation and guarantee for real-time perception and detection of production state. Taking abundant IoT data as support, a density peak (DP)-weighted fuzzy C-means (WFCM) based clustering method is proposed to detect abnormal
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A post-quantum secure communication system for cloud manufacturing safety J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-17 Haibo Yi
In recent years, as one of the new advanced manufacturing modes, cloud manufacturing has been received wide attentions around the world. The technology of cloud manufacturing intergrades the services-oriented techniques as well as manufacturing processes based on cloud computing. With the aid of the cloud computing platforms, the manufacturing services are provided in manufacturing clouds. However
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Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-17 Chia-Yu Hsu, Ju-Chien Chien
Wafer bin maps (WBM) provides crucial information regarding process abnormalities and facilitate the diagnosis of low-yield problems in semiconductor manufacturing. Most studies of WBM classification and analysis apply a statistical-based method or machine learning method operating on raw wafer data and extracted features. With increasing WBM pattern diversity and complexity, the useful features for
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An end-to-end fault diagnostics method based on convolutional neural network for rotating machinery with multiple case studies J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-16 Yiwei Wang, Jian Zhou, Lianyu Zheng, Christian Gogu
The fault diagnostics of rotating components are crucial for most mechanical systems since the rotating components faults are the main form of failures of many mechanical systems. In traditional diagnostics approaches, extracting features from raw input is an important prerequisite and normally requires manual extraction based on signal processing techniques. This suffers of some drawbacks such as
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Decision rule mining for machining method chains based on rough set theory J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-15 Rui Wang, Xiangyu Guo, Shisheng Zhong, Gaolei Peng, Lin Wang
Decision rules for machining method chains mined from historical machining documents can help technologists quickly design new machining method chains. However, the main factor that limits the practical application of existing rough set models is that the boundary regions are too large. Therefore, a decomposition-reorganization method (DRM) is proposed to mine rules for machining method chains. First
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Field-synchronized Digital Twin framework for production scheduling with uncertainty J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-15 Elisa Negri, Vibhor Pandhare, Laura Cattaneo, Jaskaran Singh, Marco Macchi, Jay Lee
Research on scheduling problems is an evergreen challenge for industrial engineers. The growth of digital technologies opens the possibility to collect and analyze great amount of field data in real-time, representing a precious opportunity for an improved scheduling activity. Thus, scheduling under uncertain scenarios may benefit from the possibility to grasp the current operating conditions of the
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Heuristic techniques for modelling machine spinning processes J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-15 Roman Stryczek, Kamil Wyrobek
In spite of many efforts made a complete model of machine spinning processes, due to its complexity, multidimensionality of the decision space and the present state of knowledge, is unachievable. The paper addresses the issues of constructing a local process model to enable the search for a locally optimal course of the process, within a short time and with the cost as low as possible. Comparison was
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An integrated SMED-fuzzy FMEA model for reducing setup time J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-14 Kübra Yazıcı, Seda Hatice Gökler, Semra Boran
Today, the companies apply lean or customized production methods, which enable the production of different kinds of products in small quantities, to meet different customer demands. But, the increase in the product variety leads to an increase in the number of setups and thus production time. The companies aim to reduce the setup time by improving activities and by eliminating the problems causing
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A self-organized approach for scheduling semiconductor manufacturing systems J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-14 Qingyun Yu, Haolin Yang, Kuo-Yi Lin, Li Li
In semiconductor manufacturing industry, traditional scheduling rules are not conducive to improving production capacity to autonomously adjust based on real-time status. To fill this gap, this study proposes a dynamic dispatching rule based on self-organization (DDRSO) to autogenerate optimal scheduling scheme through mechanisms of interaction, coordination and competition. Besides, an extended DDRSO
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Bayesian network for integrated circuit testing probe card fault diagnosis and troubleshooting to empower Industry 3.5 smart production and an empirical study J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-13 Wenhan Fu, Chen-Fu Chien, Lizhen Tang
Probe card that serves as the carrier of die and the transmitter of information is an indispensable test interface for integrated circuit testing. The probe card extracts the electrical signal of chip and sends it to the prober to screen defectives. In the process of interface transmission, signal disturbance or attenuation will lead to functional errors, and the output result will be different from
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Automated inspection in robotic additive manufacturing using deep learning for layer deformation detection J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-10 Omid Davtalab, Ali Kazemian, Xiao Yuan, Behrokh Khoshnevis
In this paper, an automated layer defect detection system for construction 3D printing is proposed. Initially, a step-by-step procedure is implemented to develop a deep convolutional neural network that receives images as input and is able to distinguish concrete layers from other surrounding objects through semantic pixel-wise segmentation. Using data augmentation techniques, 1M labeled images are
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Factors for choosing production control systems in make-to-order shops: a systematic literature review J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-07 Fernando José Gómez Paredes, Moacir Godinho Filho, Matthias Thürer, Nuno O. Fernandes, Charbel José Chiappeta Jabbour
Production control systems (PCSs) control the flow of jobs in a production system. The selection of a suitable PCS in the context of make-to-order (MTO) is challenging, due to the characteristics of MTO businesses and the number of parameters or factors that comprise a PCS. The literature that compares PCSs in the MTO context reported contradictory results. In fact, there is a gap in the literature
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Linguistic summarization to support supply network decisions J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-03 Sena Aydoğan, Gül E. Okudan Kremer, Diyar Akay
A supply chain network architecture is a key element of designing and modeling a supply chain to better understand the cost and time associated with the distribution of products with available resources and market locations. Due to the large size of combinations for product design and supplier choices; descriptive, predictive and prescriptive analytics are needed to design, control and then improve
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Assembly quality evaluation for linear axis of machine tool using data-driven modeling approach J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-03 Yang Hui, Xuesong Mei, Gedong Jiang, Fei Zhao, Ziwei Ma, Tao Tao
During the batch assembly analysis of linear axis of machine tool, assembly quality evaluation is crucial to reduce assembly quality fluctuations and improve efficiency. This study presented a data-driven modeling approach for evaluating assembly quality of linear axis based on normalized mutual information and random sampling with replacement (NMI-RSWR) variable selection method, synthetic minority
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Control of chaotic two-predator one-prey model with single state control signals J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-03 Uğur Erkin Kocamaz, Alper Göksu, Harun Taşkın, Yılmaz Uyaroğlu
In this paper, the complex control dynamics of a predator–prey Lotka–Volterra chaotic system are studied. The main purpose is to control the chaotic trajectories of two-predator one-prey system which was introduced by Samardzija and Greller (Bull Math Biol 50(5):465–491. https://doi.org/10.1007/BF02458847, 1988). Lyapunov based nonlinear control and sliding mode control methods are used. The other
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An effective adaptive adjustment method for service composition exception handling in cloud manufacturing J. Intell. Manuf. (IF 4.311) Pub Date : 2020-10-01 Yankai Wang, Shilong Wang, Bo Yang, Bo Gao, Sibao Wang
With the increasing market features of globalization, service and customization, the way manufacturers conduct manufacturing business is changing. Under this background, Cloud Manufacturing (CMfg) emerges as a new networked manufacturing model. However, CMfg is immature in many aspects, especially in exception handling of service composition execution. Due to the complexity of the enterprise manufacturing
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An experimental study of modified physical performance test of low-temperature epoxy grouting material for grouting joints with tenon and mortise J. Intell. Manuf. (IF 4.311) Pub Date : 2020-09-28 Huifeng Su, Renzhuang Li, Ming Yang
With the grouting material as focus, this study aims to guarantee that the joint with tenon and mortise constructing a metro station has the necessary waterproof performance, which shall not be poorer than the mechanical properties of the concrete with joints itself, and fine structural integrity. With groutability at low-temperature and available operational time as basis, low-temperature epoxy was
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Correction to: Bound smoothing based time series anomaly detection using multiple similarity measures J. Intell. Manuf. (IF 4.311) Pub Date : 2020-09-25 Wenqing Wang, Junpeng Bao, Tao Li
After publication, the authors realized that they failed to z-normalize the subsequences for BFDD, making it perform much worse than a correct implementation, such as detection on xmitdb_1108_2.
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