Big Data Analysis Approach for Real-Time Carbon Efficiency Evaluation of Discrete Manufacturing Workshops

Zhang, Chaoyang; Ji, Weixi

doi:10.1109/access.2019.2933353

Cited by 13 publications

(11 citation statements)

References 40 publications

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“…To efficiently evaluate carbon emission from manufacturing processes, particularly machining in manufacturing shop floors and/or workshops, a big data analysis approach involving a data-driven multi-level assessment is proposed in [8] as another instance of adopting ML in manufacturing environments. The work carried out and reported in [8] primarily involves three core stages that provide adequate insights into the pre-processing, correlation analysis, and multi-level data-driven formulation of evaluation indicators or responses of production state data from typical manufacturing shop floors or workshops.…”

Section: Relevant Related Workmentioning

confidence: 99%

“…For example, timestamps and time logs on machine tools and equipment, sensor readings, operational speeds of rotating tools, and measurements of throughput and slags on shop floors have always generated vast amounts of data [6,7]. As a matter of fact, this in a way idealizes big data, where manufacturing big data can be broadly described as data collected at every stage of manufacturing and/or production, including data from operators, equipment, machine tools, process systems, and devices on the shop floor [8,9].…”

Section: Introductionmentioning

confidence: 99%

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A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

Akinsolu

Zribi

2023

Inventions

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In this paper, the growing significance of data analysis in manufacturing environments is exemplified through a review of relevant literature and a generic framework to aid the ease of adoption of regression-based supervised learning in manufacturing environments. To validate the practicality of the framework, several regression learning techniques are applied to an open-source multi-stage continuous-flow manufacturing process data set to typify inference-driven decision-making that informs the selection of regression learning methods for adoption in real-world manufacturing environments. The investigated regression learning techniques are evaluated in terms of their training time, prediction speed, predictive accuracy (R-squared value), and mean squared error. In terms of training time (TT), k-NN20 (k-Nearest Neighbour with 20 neighbors) ranks first with average and median values of 4.8 ms and 4.9 ms, and 4.2 ms and 4.3 ms, respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of prediction speed (PS), DTR (decision tree regressor) ranks first with average and median values of 5.6784×106 observations per second (ob/s) and 4.8691×106 observations per second (ob/s), and 4.9929×106 observations per second (ob/s) and 5.8806×106 observations per second (ob/s), respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of R-squared value (R2), BR (bagging regressor) ranks first with average and median values of 0.728 and 0.728, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 0.746 and 0.746, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. In terms of mean squared error (MSE), BR (bagging regressor) ranks first with average and median values of 2.7 and 2.7, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 3.5 and 3.5, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. All methods are further ranked inferentially using the statistics of their performance metrics to identify the best method(s) for the first and second stages of the predictive modeling of the multi-stage continuous-flow manufacturing process. A Wilcoxon rank sum test is then used to statistically verify the inference-based rankings. DTR and k-NN20 have been identified as the most suitable regression learning techniques given the multi-stage continuous-flow manufacturing process data used for experimentation.

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Section: Relevant Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

Akinsolu

Zribi

2023

Inventions

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“…that need high-density sensing [44], certain traffic flows corresponding to orchestration and interconnection between heterogeneous devices and networks require tremendous amounts of data [17]. Those kinds of traffic are classified as massive Machine-Type Communications (mMTC), which also lead to significant increases in network energy consumption [45]. Finally, it is noteworthy that various QoS requirements characterize industrial services, wherein flows' rates could be regular, irregular, frequent, and non-frequent.…”

Section: B Characteristics Of Industrial Servicesmentioning

confidence: 99%

Parallel Route Optimization and Service Assurance in Energy-Efficient Software-Defined Industrial IoT Networks

Njah

Cheriet

2021

IEEE Access

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In recent years, the Industrial world has been embracing new digital technology, including the internet of things (IoT) paradigm that promises revolutionizing-prospects in numerous industrial applications. However, many deployment challenges related to real-time big data analytics, service assurance, resource optimization, energy consumption, and security awareness are raised. In this work, we focus on service assurance and resource optimization, including energy consumption challenges over Industrial Internet of Things (IIoT)-based environments since the existing network routing algorithms cannot meet the strict heterogeneous quality of service (QoS) requirements of industrial communications while optimizing resources. We take advantage of the flexibility and programmability offered by the promising softwaredefined networking paradigm, and we propose a centralized route optimization and service assurance scheme, named ROSA, over a multi-layer programmable industrial architecture. The proposed solution supports a wide range of heterogeneous flows, such as ultra-reliable low-latency communications (URLLC) and bandwidth-sensitive services. The routing optimization problems are formulated as multi-constrained shortest path problems. The Lagrangian Relaxation approach is used to solve the NP-hard complexity. Hence, we deploy a pair of parallel routing algorithms run according to the flow type to ensure QoS requirements, efficiently allocate constrained resources, and enhance the overall network energy consumption. We conduct extensive simulations to validate the proposed ROSA scheme. The experimental results show promising performance in terms of reducing bandwidth utilization by up to 22%, end-to-end delay at least by 21%, packet loss by more than 19%, flow violation by about 16%, and energy consumption up to 14% as compared to well-known benchmarks in QoS provisioning and energy-aware routing problem.INDEX TERMS Industrial Internet of Things (IIoT), Software-Defined Networking (SDN), multiprogrammability, traffic engineering, Quality of Service (QoS), energy awareness, resource optimization.

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“…Considering the complexity of discrete manufacturing workshops, Zhang and Ji proposed a Big Data analysis approach for real-time carbon efficiency evaluation of discrete manufacturing workshops in an internet of things-enabled ubiquitous environment. [18] In addition, the proliferation of Internet of Things (IoT) has pushed the horizon of a new computing paradigm, edge computing, which has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy [9]. Due to these advantages, edge computing has been used in many areas, such as mobile applications [19], video analytics, smart home and smart city [9].…”

Section: A Manufacturing Energy Data Processing and Edge Computingmentioning

confidence: 99%

Edge Computing Enabled Production Anomalies Detection and Energy-Efficient Production Decision Approach for Discrete Manufacturing Workshops

Zhang

2020

IEEE Access

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Due to the complexity and dynamics of manufacturing processes, there are various production anomalies in a discrete manufacturing workshop, which have a strong impact on manufacturing quality and productivity. Meanwhile, with the rapid development of Internet of Things technology and communication technology, data store and timely response become new challenges for production anomalies detection. Thus, an edge computing enabled production anomalies detection and energy-efficient production decision approach is proposed in this study. Firstly, an architecture of edge computing enabled production anomalies detection and energy-efficient rescheduling approach is introduced. Then, considering that the raw energy data are always large, isolated and messy, an energy consumption data preprocessing algorithm is established, and production anomaly analysis model is constructed based on long short-term memory network. When an anomaly occurs, an energy-efficient production decision making will be triggered. Finally, through a case analysis of a milling manufacturing system, the results show that the anomaly detection error of the proposed method is only 3.5%. This method realizes the combination of energy consumption data and manufacturing system anomalies detection, and can further assist production process monitoring and energy conservation. INDEX TERMS Edge computing; production anomalies detection; energy-efficient; long short-term memory network; discrete manufacturing workshops.

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Big Data Analysis Approach for Real-Time Carbon Efficiency Evaluation of Discrete Manufacturing Workshops

Cited by 13 publications

References 40 publications

A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

Parallel Route Optimization and Service Assurance in Energy-Efficient Software-Defined Industrial IoT Networks

Edge Computing Enabled Production Anomalies Detection and Energy-Efficient Production Decision Approach for Discrete Manufacturing Workshops

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