Parallel computing in automation of decoupled fluid-thermostructural simulation approach

Glänzel, Janine; Naumann, Andreas; Kumar, Tharun Suresh

doi:10.36897/jme/117785

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…3; − High-Performance Computation (HPC), as new processing capacity for sensorial data, e.g. [32], which represent a new computational paradigm that will probably substitute the actual one (small or disaggregated clusters), especially considering the new required computation, able to deal with the expected real-time processing of huge amount of data (Big Data) streaming continuously from the thousands of sensors existing in manufacturing system equipment. That will probably one of the distinctive features of any CPS, including the I n -CPS 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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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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An Indicative Model Considering Part of the Thermo-Mechanical Behaviour of a Large Grinding Machine

Mareš,

Horejš,

Nykodym

2023

Lecture Notes in Production Engineering

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Machine tool (MT) thermal errors are an important element in ma-chined workpiece inaccuracies. In the past few decades, thermal errors associated mainly with one particular source (e.g. spindle or environment), have been successfully reduced by SW compensation techniques such as multiple linear re-gression analysis, finite element method, neural network, transfer function (TF) within similar calibration and verification conditions. An approach based on TFs is used for thermal error modelling in this research. This method respects basic heat transfer mechanisms in the MT and requires a minimum of additional gauges. The approach provides insight into the share of each source in the total machine thermal error through a combination of linear parametric models. The aim of this research is to develop an indicative model for a large grinding machine with predictive functionality focused on part of the thermo-mechanical behaviour within different configurations of the headstock, tailstock and workpiece. Unlike a compensation model, an indicative model has no connection to the MT feed drives and can only provide the machine operator with information regarding the actual direction and relative magnitude along with prediction of the time constant and steady state of the non-stationary thermal error. The second aim is to compare the difficulty of measuring at the stator and rotating machine part levels, the thermal behaviour linearity at both levels and the possibility of upgrading the indicative model to a compensation model to extend industrial applicability.

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Split CFD-simulation approach for effective quantification of mixed convective heat transfer coefficients on complex machine tool models

et al. 2023

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Parallel computing in automation of decoupled fluid-thermostructural simulation approach

Cited by 4 publications

References 9 publications

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

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

An Indicative Model Considering Part of the Thermo-Mechanical Behaviour of a Large Grinding Machine

Split CFD-simulation approach for effective quantification of mixed convective heat transfer coefficients on complex machine tool models

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