An evolvable model of machine tool behavior applied to energy usage prediction

Komoto, Hitoshi; Herrera, Germán; Herwan, Jonny

doi:10.1016/j.cirp.2020.04.082

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…The cutting experiments were conducted in a research environment developed by the authors (described in [32,33]), but considering that this system is intended to be applied in the future in an industrial environment, it was suggested the use of a mixture of high-end and low-cost sensors/instruments with the purpose to observe the advantages and the drawbacks of using each of them. The sensors/instruments used during this study were: 3-axis dynamometer (the workpiece is directly attached to it)…”

Section: Case Study -A Multi-sensor Tool Monitoring System For Tool W...mentioning

confidence: 99%

An experimental study of multi-sensor tool wear monitoring and its application to predictive maintenance

Granados,

Misaka,

Herwan

et al. 2024

Preprint

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Wear in cutting tools is a critical issue that can lead to reduced product quality, increased production costs, and unexpected downtime. To mitigate these challenges, the implementation of tool wear monitoring systems and predictive maintenance strategies has gained significant attention in recent years. Early detection or prediction of tool wear is vital to optimize tool life and maintain the manufacturing processes efficiently. This paper presents a method to determine the tool wear progression based on the collaboration of direct and indirect monitoring techniques. By analyzing the monitoring of data from force, vibration, sound, and current sensors to estimate the tool wear state, and correlating this information with a photographic database of the tool wear progression used to create an image recognition system that can classify the tool wear at any moment into three states: Good, Moderate and Worn. A case study was conducted to test the advantages and limitations of the proposed method. The case study also shows that the improvement of the prediction of the tool wear state might be useful in the decision-making of whether the tool life can be extended, or the tool must be replaced, as well as in the detection of anomalies during the machining process, aiming its improvement and the reduction of operational costs.

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Section: Case Study -A Multi-sensor Tool Monitoring System For Tool W...mentioning

confidence: 99%

An experimental study of multi-sensor tool wear monitoring and its application to predictive maintenance

Granados,

Misaka,

Herwan

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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“…To explain the smart factory discussed in the context of this study, this section describes the research environment of smart factory developed by the authors (Komoto and Masui 2018;Komoto et al 2020;.…”

Section: The Research Environmentmentioning

confidence: 99%

“…Smart factory evolves by adding new functions or upgrading existing functions to meet the needs of manufacturers in the use stage of the life cycle (Komoto et al 2020). For instance, production engineers and operators expect smart factory to support more advanced decision makings as a large amount of operational data of assets becomes available.…”

Section: Introductionmentioning

confidence: 99%

Integration of Health Monitoring of Cutting Tools and Production Scheduling in Smart Factory

Komoto,

Herrera,

Herwan

et al. 2023

PHMAP_CONF

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Smart factory evolves by adding new functions or upgrading existing functions to meet the needs of manufacturers in the use stage of the life cycle. To reduce complexity of smart factory, these functions must be carefully designed considering interactions with other functions. This study analyzes integration of functions situated in the different layers in the functional hierarchy of smart factory. In this study, a health monitoring system for cutting tools in a shop floor, which offers a function to manage the lifetime of cutting tools, is presented. The system is integrated with a production scheduling system, which offers a function to schedule machining processes considering efficient usage of machines as well as cutting tools, while maintaining the quality machining processes. Primary evaluation of the functional integration shows that activities in the shop floor regarding selection and replacement of cutting tools are considered in defining production schedules. It also shows that such functional integration results in increase in complexity regarding the behavioral model of humans in smart factory.

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Review on Energy Consumption Optimization Methods of Typical Discrete Manufacturing Equipment

Yao

Shao

Zhao

2021

Intelligent Robotics and Applications

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An evolvable model of machine tool behavior applied to energy usage prediction

Cited by 8 publications

References 12 publications

An experimental study of multi-sensor tool wear monitoring and its application to predictive maintenance

An experimental study of multi-sensor tool wear monitoring and its application to predictive maintenance

Integration of Health Monitoring of Cutting Tools and Production Scheduling in Smart Factory

Review on Energy Consumption Optimization Methods of Typical Discrete Manufacturing Equipment

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