Ubiquitous Control of a CNC Machine: Proof of Concept for Industrial IoT Applications

Aebersold, Stefan A.; Akinsolu, Mobayode O.; Monir, Shafiul; Jones, Martyn

doi:10.3390/info12120529

Cited by 5 publications

(3 citation statements)

References 32 publications

(36 reference statements)

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“…This is primarily because the deployment of these technologies is often application-specific (as mentioned already in Section III-A), and there is no unified methodology for their appraisal. For instance, to remotely monitor a piece of industrial equipment via an online HMI, internet connectivity is often required as demonstrated in [49], [50]. As a result, the industrial equipment leaves a digital footprint that can be viewed or accessed illegitimately, if resilient firewalls and cybersecurity measures are not put into place.…”

Section: Technological Considerationsmentioning

confidence: 99%

Applied Artificial Intelligence in Manufacturing and Industrial Production Systems: PEST Considerations for Engineering Managers

Akinsolu

2023

IEEE Eng. Manag. Rev.

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Presently, artificial intelligence (AI) is playing a leading role in our contemporary world via numerous applications. Despite its many advantages, analytical frameworks highlighting the implications of AI applications are still evolving. Particularly, in manufacturing and industrial production where novel technologies are continuously being harnessed. Consequently, AI and the implications of its applications have relatively remained a grey area for many engineering managers who are key players in the gravitation of manufacturing and industrial production towards the fourth industrial revolution and more recently, the fifth industrial revolution, generally termed as Industry 4.0 (I4.0) and Industry 5.0 (I5.0), respectively. In this study, the implications of AI applications in the general context of manufacturing and industrial production, are presented to provide insight for engineering managers. These implications are discussed via PEST (political, economic, social, and technological) considerations of the broad implications of the adoption of AI techniques in manufacturing and industrial production systems. A new engineering management model has not been proposed in this paper. Rather, a discussion aimed at serving as a tool for the appraisal of the implications of the general applications of AI by engineering managers, who may not be AI specialists or data science experts is presented.

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Section: Technological Considerationsmentioning

confidence: 99%

Applied Artificial Intelligence in Manufacturing and Industrial Production Systems: PEST Considerations for Engineering Managers

Akinsolu

2023

IEEE Eng. Manag. Rev.

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“…Electronic control on CNCs can be provided on computers via embedded electronic systems [7][8][9][10]. During this control phase, communication can be carried out using wired, Wi-Fi, or IOT (Internet of Things) systems [11][12].…”

Section: Literature Reviewmentioning

confidence: 99%

Production, and Analysis of a Two-Axis Food Liquid Pouring Machine

ERDOĞAN

OKATAN

UZ³

et al. 2022

IJET

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“…However, up until recent times, manufacturing data has not really been used to "automagically" improve or enhance the manufacturing processes that generated them in the first place [9,10]. In other words, other than process monitoring and regulation that are mostly achieved via traditional process visualization and process control using human-machine interfaces (HMIs), manufacturing data is still yet to be used extensively to support data-driven paradigms such as predictive modeling (usually takes the form of predictive maintenance when machine behavior and the life cycle is the context [11,12]) and ubiquitous monitoring and control (usually incorporated in the industrial internet of things (IIoT) where higher levels of connectivity, interoperability and robustness is the goal [13,14]). This apparent gap can be mainly attributed to the purpose-built or fit-for-purpose nature of most of the computational paradigms and analytic frameworks that support manufacturing processes [5].…”

Section: Introductionmentioning

confidence: 99%

A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

Akinsolu

Zribi

2023

Inventions

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In this paper, the growing significance of data analysis in manufacturing environments is exemplified through a review of relevant literature and a generic framework to aid the ease of adoption of regression-based supervised learning in manufacturing environments. To validate the practicality of the framework, several regression learning techniques are applied to an open-source multi-stage continuous-flow manufacturing process data set to typify inference-driven decision-making that informs the selection of regression learning methods for adoption in real-world manufacturing environments. The investigated regression learning techniques are evaluated in terms of their training time, prediction speed, predictive accuracy (R-squared value), and mean squared error. In terms of training time (TT), k-NN20 (k-Nearest Neighbour with 20 neighbors) ranks first with average and median values of 4.8 ms and 4.9 ms, and 4.2 ms and 4.3 ms, respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of prediction speed (PS), DTR (decision tree regressor) ranks first with average and median values of 5.6784×106 observations per second (ob/s) and 4.8691×106 observations per second (ob/s), and 4.9929×106 observations per second (ob/s) and 5.8806×106 observations per second (ob/s), respectively, for the first stage and second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, respectively, over 50 independent runs. In terms of R-squared value (R2), BR (bagging regressor) ranks first with average and median values of 0.728 and 0.728, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 0.746 and 0.746, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. In terms of mean squared error (MSE), BR (bagging regressor) ranks first with average and median values of 2.7 and 2.7, respectively, over 50 independent runs, for the first stage of the predictive modeling of the multi-stage continuous-flow manufacturing process, and RFR (random forest regressor) ranks first with average and median values of 3.5 and 3.5, respectively, over 50 independent runs, for the second stage of the predictive modeling of the multi-stage continuous-flow manufacturing process. All methods are further ranked inferentially using the statistics of their performance metrics to identify the best method(s) for the first and second stages of the predictive modeling of the multi-stage continuous-flow manufacturing process. A Wilcoxon rank sum test is then used to statistically verify the inference-based rankings. DTR and k-NN20 have been identified as the most suitable regression learning techniques given the multi-stage continuous-flow manufacturing process data used for experimentation.

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Ubiquitous Control of a CNC Machine: Proof of Concept for Industrial IoT Applications

Cited by 5 publications

References 32 publications

Applied Artificial Intelligence in Manufacturing and Industrial Production Systems: PEST Considerations for Engineering Managers

Applied Artificial Intelligence in Manufacturing and Industrial Production Systems: PEST Considerations for Engineering Managers

Production, and Analysis of a Two-Axis Food Liquid Pouring Machine

A Generalized Framework for Adopting Regression-Based Predictive Modeling in Manufacturing Environments

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