Energy Consumption Estimation for Machining Processes Based on Real-time Shop Floor Monitoring via Wireless Sensor Networks

Mourtzis, Dimitris; Vlachou, Ekaterini; Milas, Nikolaos; Dimitrakopoulos, George

doi:10.1016/j.procir.2016.11.110

Cited by 56 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The monitoring managing module plays an important role in monitoring the status of resources and orders on factory floors and provides real-time status information (e.g. machine availability) of resources and orders that is necessary for achieving optimised scheduling (Wang 2013;Mourtzis, Vlachou, Milas, and Dimitrakopoulos 2016;Mourtzis, Vlachou, Milas, and Xanthopoulos 2016;). …”

Section: International Journal Of Production Researchmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

221

View full text Add to dashboard Cite

For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand manufacturing services to consumers over the Internet. Scheduling is one of the critical means for achieving the aim of cloud manufacturing. Thus far, about 158 articles have been published on scheduling in cloud manufacturing. However, research on scheduling in cloud manufacturing faces numerous challenges. Thus, there is an urgent need to ascertain the current status and identify issues and challenges to be addressed in the future. Covering articles published on the subject over the past eight years, this article aims to provide a state-of-the-art literature survey on scheduling issues in cloud manufacturing. A detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier's Scopus abstract and citation database. Typical characteristics of scheduling issues in cloud manufacturing are systematically summarised. A comparative analysis of scheduling issues in cloud manufacturing and other scheduling issues such as cloud computing scheduling, workshop scheduling and supply chain scheduling is also carried out. Finally, future research issues and challenges are identified.

show abstract

Section: International Journal Of Production Researchmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

221

View full text Add to dashboard Cite

show abstract

“…Defects can refer to either product unwanted characteristics, or resources' unwanted behaviour. Methods that have been previously used-regardless of the application-are Case Based Reasoning [32], pattern recognition [33,34], Analysis of Variance (ANOVA) [35], neural networks [36], Hypothesis testing [37], Time Series [38], and many others. However, none of these methods is quick enough to give the results from a deterministic point of view and without using previous measurements for training.…”

Section: Similar Methods and Constrains On Applicabilitymentioning

confidence: 99%

A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT

Papacharalampopoulos,

Giannoulis,

Stavropoulos

et al. 2020

Applied Sciences

Self Cite

View full text Add to dashboard Cite

A dashboard application is proposed and developed to act as a Digital Twin that would indicate the Measured Value to be held accountable for any future failures. The current study describes a method for the exploitation of historical data that are related to production performance and aggregated from IoT, to eliciting the future behavior of the production, while indicating the measured values that are responsible for negative production performance, without training. The dashboard is implemented in the Java programming language, while information is stored into a Database that is aggregated by an Online Analytical Processing (OLAP) server. This achieves easy Key Performance Indicators (KPIs) visualization through the dashboard. Finally, indicative cases of a simulated transfer line are presented and numerical examples are given for validation and demonstration purposes. The need for human intervention is pointed out.

show abstract

“…As presented in Section 1, commercial CNCs have only recently included communication capabilities that have been studied in very recent works. Mourtzis et al [10], proposed an approach for machine-tool energy consumption estimation, based on real-time monitoring measurements using wireless sensor networks. This solution overcomes the lack of data acquisition and transmission in machine-tools previously installed in real workshops.…”

Section: Data Acquisition Platforms In Iot Solutionsmentioning

confidence: 99%

Using Ensembles for Accurate Modelling of Manufacturing Processes in an IoT Data-Acquisition Solution

Garrido-Labrador

Puente-Gabarri²,

Ramírez-Sanz

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

The development of complex real-time platforms for the Internet of Things (IoT) opens up a promising future for the diagnosis and the optimization of machining processes. Many issues have still to be solved before IoT platforms can be profitable for small workshops with very flexible workloads and workflows. The main obstacles refer to sensor implementation, IoT architecture, and data processing, and analysis. In this research, the use of different machine-learning techniques is proposed, for the extraction of different information from an IoT platform connected to a machining center, working under real industrial conditions in a workshop. The aim is to evaluate which algorithmic technique might be the best to build accurate prediction models for one of the main demands of workshops: the optimization of machining processes. This evaluation, completed under real industrial conditions, includes very limited information on the machining workload of the machining center and unbalanced datasets. The strategy is validated for the classification of the state of a machining center, its working mode, and the prediction of the thermal evolution of the main machine-tool motors: the axis motors and the milling head motor. The results show the superiority of the ensembles for both classification problems under analysis and all four regression problems. In particular, Rotation Forest-based ensembles turned out to have the best performance in the experiments for all the metrics under study. The models are accurate enough to provide useful conclusions applicable to current industrial practice, such as improvements in machine programming to avoid cutting conditions that might greatly reduce tool lifetime and damage machine components.

show abstract

Energy Consumption Estimation for Machining Processes Based on Real-time Shop Floor Monitoring via Wireless Sensor Networks

Cited by 56 publications

References 15 publications

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Scheduling in cloud manufacturing: state-of-the-art and research challenges

A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT

Using Ensembles for Accurate Modelling of Manufacturing Processes in an IoT Data-Acquisition Solution

Contact Info

Product

Resources

About