Adaptation to Industry 4.0 Using Machine Learning and Cloud Computing to Improve the Conventional Method of Deburring in Aerospace Manufacturing Industry

Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.

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“…[17], [40], [49], [50], [51], [63], [64], [65], [68], [70], [72], [80], [93], [94], [99], [101], [103], [104], [105], [106], [113]…”

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Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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“…Enhancements of various aspects of cloud computing have also happened through adding intelligence to existing elements like cloud and edge. Such AI-based elements have been tested on various situations like scheduling, optimization, and forecasting [102][103][104]. Along with the addition of intelligence elements to the existing cloud infrastructure, network-related advancements like software-defined network (SDN) are added to the existing Industry 4.0 network environment to improve data transmission and quality of service [105,106].…”

Section: Robots and Dronesmentioning

confidence: 99%

A Modern Approach towards an Industry 4.0 Model: From Driving Technologies to Management

et al. 2022

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Every so often, a confluence of novel technologies emerges that radically transforms every aspect of the industry, the global economy, and finally, the way we live. These sharp leaps of human ingenuity are known as industrial revolutions, and we are currently in the midst of the fourth such revolution, coined Industry 4.0 by the World Economic Forum. Building on their guideline set of technologies that encompass Industry 4.0, we present a full set of pillar technologies on which Industry 4.0 project portfolio management rests as well as the foundation technologies that support these pillars. A complete model of an Industry 4.0 factory which relies on these pillar technologies is presented. The full set of pillars encompasses cyberphysical systems and Internet of Things (IoT), artificial intelligence (AI), machine learning (ML) and big data, robots and drones, cloud computing, 5G and 6G networks, 3D printing, virtual and augmented reality, and blockchain technology. These technologies are based on a set of foundation technologies which include advances in computing, nanotechnology, biotechnology, materials, energy, and finally cube satellites. We illustrate the confluence of all these technologies in a single model factory. This new factory model succinctly demonstrates the advancements in manufacturing introduced by these modern technologies, which qualifies this as a seminal industrial revolutionary event in human history.

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“…Cloud-/Edge-Computing (cloud-edge computing) is a critical component of Industry 4.0 to ensure on-demand availability of high computing resources, e.g., as shown for aerospace manufacturing industry in [90], vehicular intelligence towards connected smart vehicles [91]. The vital strengths of high computing power, massive data storage capacity, data analytics, service-oriented architecture with a sizeable autonomous structure have led to a massive adoption of cloud computing in today's smart industries [92].…”

Section: A Cloud-/edge-computing and Industrial Dt Deploymentmentioning

confidence: 99%

Industrial Digital Twins at the Nexus of NextG Wireless Networks and Computational Intelligence: A Survey

Zeb¹,

Mahmood²,

Hassan³

et al. 2021

Preprint

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By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry 4.0 promotes integrating cyberphysical worlds through cyber-physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT is an emerging but conceptually different construct than CPS. Like CPS, DT relies on communication to create a highly-consistent, synchronized digital mirror image of the objects or physical processes. DT, in addition, uses builtin models on this precise image to simulate, analyze, predict, and optimize their real-time operation using feedback. DT is rapidly diffusing in the industries with recent advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and discussing the role and requirements of these technologies in DT-enabled industries from the communication and computing perspective. In this article, we first present the functional aspects, appeal, and innovative use of DT in smart industries. Then, we elaborate on this perspective by systematically reviewing and reflecting on recent research in next-generation (NextG) wireless technologies (e.g., 5G and beyond networks), various tools (e.g., age of information, federated learning, data analytics), and other promising trends in networked computing (e.g., edge and cloud computing). Moreover, we discuss the DT deployment strategies at different industrial communication layers to meet the monitoring and control requirements of industrial applications. We also outline several key reflections and future research challenges and directions to facilitate industrial DT's adoption.

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Adaptation to Industry 4.0 Using Machine Learning and Cloud Computing to Improve the Conventional Method of Deburring in Aerospace Manufacturing Industry

Cited by 13 publications

References 11 publications

Research Methods in Machine Learning: A Content Analysis

Research Methods in Machine Learning: A Content Analysis

A Modern Approach towards an Industry 4.0 Model: From Driving Technologies to Management

Industrial Digital Twins at the Nexus of NextG Wireless Networks and Computational Intelligence: A Survey

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