Utilisation of Iot and Sensing for Machine Tools

Fujishima, Makoto; Mori, Masahiko; Narimatsu, Koichiro; Irino, Naruhiro

doi:10.5604/01.3001.0013.0447

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…This area is dealt with, for example, in [15], where the authors describe various sensing technologies that are applicable to the area of predictive maintenance, which is dealt with by one of the world's leading machine tool manufacturers (DMG MORI). The authors follow up this paper with [16], where the accelerometer signal is used for multiple functions at the same time-both for evaluating chatter information and for machine tool condition monitoring for predictive maintenance. An overview of usable sensors is quite comprehensively covered in an article focusing on intelligent spindles [17].…”

Section: Of 25mentioning

confidence: 99%

In-Process Chatter Detection in Milling: Comparison of the Robustness of Selected Entropy Methods

Hauptfleischová

Novotný

Falta

et al. 2022

JMMP

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This article deals with the issue of online chatter detection during milling. The aim is to achieve a verification of the reliability and robustness of selected methods for the detection of chatter that can be evaluated on the machine tool in real time by using the accelerometer signal. In the introductory part of the paper, an overview of the current state of the art in the field of chatter detection is summarized. Entropic methods have been selected that evaluate the presence of chatter from the qualitative behavior of the signal rather than from the magnitude of its amplitude, because the latter can be affected by the transmission of vibrations to the accelerometer position. Another criterion for selection was the potential for practical implementation in a real-time evaluation of the accelerometer signal, which is nowadays quite commonly installed on machine tools. The robustness of the methods was tested with respect to tool compliance, which affects both chatter occurrence and vibration transfer to the accelerometer location. Therefore, the study was carried out on a slender milling tool with two different overhangs and on a rigid roughing tool. The reference stability assessment for each measurement was based on samples of the machined surface. The signals obtained from the accelerometer were then post-processed and used to calculate the chatter indicators. In this way, it was possible to compare different methods in terms of their ability to achieve reliable in-process detection of chatter and in terms of the computational complexity of the indicator.

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Section: Of 25mentioning

confidence: 99%

In-Process Chatter Detection in Milling: Comparison of the Robustness of Selected Entropy Methods

Hauptfleischová

Novotný

Falta

et al. 2022

JMMP

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“…This capacity is assumed implemented in the I n -CPS on Fig. 3; − frameworks for development CPS subsystems, such as the framework for "resilient and traceable on-machine measurements" [34] as a condition for building reliable and resilient CPSs, and building and integration of Internet-of-Things (IoT) for "utilisation of IoT and sensing for machine tools" [35], that identifies the "sensing items" for a machine tool. Another distinctive feature of any CPS, represented by multiple sensorial data channels and the corresponding feed-back loops represented on Fig.…”

Section: On Readiness Of the Present Technologies And Knowledge To Upgrade Manufacturing Systems To I N -Cpsmentioning

confidence: 99%

Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – It Does Not Mean Total Automation, Human is Still in the Centre: Part II – In-CPS and a View from Community on Industry 4.0 Impact on Society

Putnik¹,

Shah²,

Putnik³

et al. 2021

Journal of Machine Engineering

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In many discourses, popular as well as scientific, it is suggested that the "massive" use of Artificial Intelligence (AI), including Machine Learning (ML), and reaching the point of "singularity" through so-called Artificial General Intelligence (AGI), and Artificial Super-Intelligence (ASI), will completely exclude humans from decision making, resulting in total dominance of machines over human race. Speaking in terms of manufacturing systems, it would mean that the intelligence and total automation would be achieved (once the humans are excluded). The hypothesis presented in this paper is that there is a limit of AI/ML autonomy capacity, and more concretely, the ML algorithms will be not able to become totally autonomous and, consequently, the human role will be indispensable. In the context of the question, the authors of this paper introduce the notion of the manufacturing singularity and present an intelligent machine architecture towards the manufacturing singularity, arguing that the intelligent machine will always be human dependent. In addition, concerning the manufacturing, the human will remain in the centre of Cyber-Physical Systems (CPS) and in Industry 4.0. The methodology to support this argument is inductive, similarly to the methodology applied in a number of texts found in literature, and based on computational requirements of inductive inference based machine learning. The argumentation is supported by several experiments that demonstrate the role of human within the process of machine learning. Based on the exposed considerations, a generic architecture of intelligent CPS, with embedded ML functional modules in multiple learning loops, is proposed in order to evaluate way of use of ML functionality in the context of CPS. Similar to other papers found in literature, due to the (informal) inductive methodology applied, considering that this methodology does not provide an absolute proof in favour of, or against, the hypothesis defined, the paper represents a kind of position paper. The paper is divided into two parts. In the first part a review of argumentation from literature in favour of and against the thesis on the human role in future was presented, as well as the concept of the manufacturing singularity was introduced. Furthermore, an intelligent machine architecture towards the manufacturing singularity was proposed, arguing that the intelligent machine will be always human dependent _____________

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“…Measuring process -Manufacturing strategy (tool path, axes movements) -Process forces -Tool wear effects -Measuring strategy (probing points, axes movements) -Low contact forces during probing -Probing almost wear-free To overcome the environmental challenges, the following different implementation approaches can be derived. In the course of digitalization and with the help of new integrated sensor technologies, the availability of process and environmental data is increasing rapidly [6][7][8]. Similar to the VCMM approach, this data basis can be used to generate models for optimization and task-specific uncertainty predictions for dimensional measurements on machines in production environments.…”

Section: Manufacturing Processmentioning

confidence: 99%

A review on enabling technologies for resilient and traceable on-machine measurements

Dahlem¹,

Emonts²,

Sanders³

et al. 2020

Journal of Machine Engineering

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On-Machine Measurements are a key factor for shorter closed quality control loops in industrial manufacturing. Especially for the production of large components, they promote the first-time-right approach, which is highly desirable, due to small quantities and steep value chains. In contrast to measurement rooms for CMMs, the production environment conditions are unregulated and impact multiple factors along the on-machine measurement metrological chain. As presented as a keynote speech at the XXXI CIRP Sponsored Conference on Supervising and Diagnostics of Machining Systems "MANUFACTURING ACTIVE IMPROVEMEN" by Professor Dr. Robert H. Schmitt, this article reviews current research and ideas regarding on-machine measurements. The authors collect necessary process data with the help of new technologies in the course of digitalization and thus propose a holistic model for systematic error compensation and measurement uncertainty prediction. For assessing the machine's volumetric accuracy under thermal loads, the authors develop a novel modelling approach, which determines transient geometric errors by abstracting structural parts as spline curve with typical deformation modes. To address the workpiece's influence on the measurement process, a data-driven framework, fusing realtime sensor-data with the virtual component, is used to model and predict transient thermo-mechanical workpiece states. For dissemination, the authors continue working on ISO standardization and, as subjects of future research, explore new paths in terms of data-driven modelling approaches, using physical abstractions coupled with machine learning and live process data.

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Utilisation of Iot and Sensing for Machine Tools

Cited by 5 publications

References 14 publications

In-Process Chatter Detection in Milling: Comparison of the Robustness of Selected Entropy Methods

In-Process Chatter Detection in Milling: Comparison of the Robustness of Selected Entropy Methods

Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – It Does Not Mean Total Automation, Human is Still in the Centre: Part II – In-CPS and a View from Community on Industry 4.0 Impact on Society

A review on enabling technologies for resilient and traceable on-machine measurements

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