Energy consumption monitoring for the order fulfilment in a ubiquitous manufacturing environment
01 Apr 2017-The International Journal of Advanced Manufacturing Technology (Springer London)-Vol. 89, Iss: 9, pp 1-14
TL;DR: In this article, the authors proposed a system to monitor manufacturing energy consumption (MEC) in a discrete manufacturing enterprise, where energy is consumed everywhere and anytime with creating ubiquitous MEC data.
Abstract: The objective of this study is to monitor manufacturing energy consumption (MEC) in a discrete manufacturing enterprise, where energy is consumed everywhere and anytime with creating ubiquitous MEC data. The ubiquitous manufacturing (UbiM) technology, including radio frequency identification (RFID) technique, is employed to automate real-time data acquisition and processing for an order fulfilment. An MEC model for the order fulfilment is constructed according to the bill of materials (BOM). In this model, the computation is triggered by an RFID read event (RRE) enabling a digital energy metre to acquire energy consumption value of a workstation for processing a certain material, and then the acquired value is assigned to an energy consumption event (ECE). To reflect the effect of an ECE on energy consumption of a production task, a station-material energy consumption matrix (smECM) is constructed to store the relevant event data, which plays a key role in alleviating MEC monitoring restrictions caused by big energy data. By operating the matrix, the MEC monitoring information can be effectively extracted. To assist manufacturing enterprises to better employ it, the proposed method was demonstrated by monitoring MEC of an order in an auto-part manufacturer.
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TL;DR: Smart manufacturing has received increased attention from academia and industry in recent years, as it provides competitive advantage for manufacturing companies making industry more efficient and more efficient as discussed by the authors. But, the benefits of smart manufacturing are limited.
257 citations
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...The IoT technologies have been widely applied in modern manufacturing, especially, in industrial emission and energy consumption monitoring (Hu et al., 2017; Martillano et al., 2017; Tao et al., 2014b)....
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TL;DR: The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions and lays the foundation for tool wear monitoring in real industrial settings.
168 citations
TL;DR: This paper summarises and analyses a broad range of the state-of-the-art implementation of UM systems from a holistic and comprehensive view of manufacturing technology, including UM for manufacturing processes, manufacturing control systems, logistics, remanufacturing, cloud manufacturing, production scheduling, production quality control and evaluation.
Abstract: In the past 10 years, ubiquitous manufacturing (UM) has received a growing amount of attention among researchers in the manufacturing community because ubiquitous computing technologies (UCTs) can be applied to address a wide range of issues in the manufacturing industry, e.g. manufacturing processes and equipment, manufacturing management and planning. However, to the best of the authors’ knowledge, there is a lack of comprehensive and critical review from a holistic view of the state-of-the-art UM and its systems. This paper aims to provide a concise overview of the technical features, characteristics and broad range of applications of UM systems published between 1997 and 2017. Among these selected articles, more than 70% of them were published between 2012 and 2017, and they are considered as recent pertinent works which will be discussed in detail. The unique aspects of this paper lie in that this paper summarises and analyses a broad range of the state-of-the-art implementation of UM systems from a ...
78 citations
Cites methods from "Energy consumption monitoring for t..."
...Hu et al. (2017) proposed a method to monitor manufacturing energy consumption in a UM environment....
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TL;DR: An artificial neural network (ANN) optimized by genetic algorithm (GA) is an established prediction model of bending force in hot strip rolling that can be flexibly used for on-line controlling and rolling schedule optimizing.
Abstract: An artificial neural network (ANN) optimized by genetic algorithm (GA) is an established prediction model of bending force in hot strip rolling. The data are collected from factory of steel manufacture. Entrance temperature and thickness, exit thickness, strip width, rolling force, rolling speed, roll shifting, target profile, and yield strength of strip are selected to be independent variables as network inputs. MATLAB software is utilized for establishing GA-ANN model and achieving the purpose of obtaining the bending force as results of setup model, as well as the GA method is used to optimize the initial weights and biases of the backpropagation neural network. Mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), and correlation coefficient are adapted to evaluate the performance of the model. The predictive results are compared with the measured results to verify the accuracy of the GA-ANN prediction model. It is found that the optimization effect is the best with the population size 40 crossover probability of 0.7 and the mutation probability of 0.05 at the same time, the fitness function value can reach 80.7. In addition, the ANN architecture 9-11-1 trained with Bayesian regulation “trainbr” function has the best performance with mean absolute error of 0.01 and correlation coefficient of 0.983. With a deeper understanding of neural networks through the analysis of the GA-ANN model, the proposed model can be flexibly used for on-line controlling and rolling schedule optimizing.
69 citations
TL;DR: The optimal and near-optimal sequences of features of a part which has 15 actual features and is processed by a machining centre have been found and the optimal PSFP achieves a 28.60% EFT reduction, which validates the effectiveness of the developed model and optimisation approaches.
41 citations
Cites background from "Energy consumption monitoring for t..."
...M achine tools are widely used as 3 the basic production facilities [2] in the manufacturing industry [3 ], and they are highly energy4 intensive during production [4]....
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References
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TL;DR: The applications of the technologies of IoT and CC in manufacturing are investigated and a CC- and IoT-based cloud manufacturing (CMfg) service system and its architecture are proposed, and the relationship among CMfg, IoT, and CC is analyzed.
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617 citations
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612 citations
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TL;DR: Tracking Industrial Energy Efficiency and CO2 Emissions (TIHE) as mentioned in this paper ) is a G8-funded effort to track industrial energy efficiency over the last 25 years and identify the leaders and the laggards.
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562 citations
TL;DR: The applications of IoT technologies in CMfg has been investigated and the classification of manufacturing resources and services, as well as their relationships, are presented.
Abstract: Recently, cloud manufacturing (CMfg) as a new service-oriented manufacturing mode has been paid wide attention around the world. However, one of the key technologies for implementing CMfg is how to realize manufacturing resource intelligent perception and access. In order to achieve intelligent perception and access of various manufacturing resources, the applications of IoT technologies in CMfg has been investigated in this paper. The classification of manufacturing resources and services, as well as their relationships, are presented. A five-layered structure (i.e., resource layer, perception layer, network layer, service layer, and application layer) resource intelligent perception and access system based on IoT is designed and presented. The key technologies for intelligent perception and access of various resources (i.e., hard manufacturing resources, computational resources, and intellectual resources) in CMfg are described. A prototype application system is developed to valid the proposed method.
526 citations
TL;DR: In this article, an RFID-enabled real-time manufacturing execution system (RT-MES) is proposed to track and trace manufacturing objects and collect realtime production data.
Abstract: Mass-customization production (MCP) companies must fight with shop-floor uncertainty and complexity caused by wide variety of product components. The research is motivated by a typical MCP company that has experienced inefficient scheduling due to paper-based identification and manual data collection. This paper presents an RFID-enabled real-time manufacturing execution system (RT-MES). RFID devices are deployed systematically on the shop-floor to track and trace manufacturing objects and collect real-time production data. Disturbances are identified and controlled within RT-MES. Planning and scheduling decisions are more practically and precisely made and executed. Online facilities are provided to visualize and manage real-time dynamics of shop-floor WIP (work-in-progress) items. A case study is reported in a collaborating company which manufactures large-scale and heavy-duty machineries. The efficiency and effectiveness of the proposed RT-MES are evaluated with real-life industrial data for shop-floor production management in terms of workers, machines and materials.
424 citations