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X-ray computed tomography (XCT) has been shown to be a reliable tool for quality inspection, material evaluation, and dimensional measurement tasks across diverse academic and industrial applications. In recent years, the integration of machine learning and deep learning techniques have ushered new advances in the industrial computed tomography domain spanning multiple facets, including image reconstruction, segmentation, and feature characterization. This review paper comprehensively surveys the current state-of-the-art machine learning and deep learning applications throughout the entire XCT workflow. Additionally, we explore relevant developments in the medical imaging domain, evaluating their implications for industrial computed tomography. In conclusion, we identify potential future research, drawing insights from existing research gaps in the domain and recent advancements in artificial intelligence. Notably, we underscore the importance of uncertainty quantification and model explainability for further acceptance of artificial intelligence techniques in the domain. [ABSTRACT FROM AUTHOR]

Industry 4.0 has brought modernization to the production system through the network integration of the constituent entities which, combined with the evolution of information technology, has enabled an increase in productivity, product quality, optimization of production costs, and product customization to customer needs. Despite the complexity of human thought, artificial intelligence tries to replicate it in algorithms, creating models capable of processing databases with a high volume of information, and generating valuable information for decision making. Within this area, there are subfields, such as Machine Learning and Deep Learning, which, through mathematical models, define patterns to predict output data from known input data. In addition to this type of algorithm, there are metaheuristic models capable of optimizing the parameters required in Machine Learning and Deep Learning algorithms. These intelligent systems have applications in various areas such as industry, construction, health, logistics processes, and maintenance management, among others. This paper focuses on Artificial Intelligence models addressing Industry 4.0 approach. [ABSTRACT FROM AUTHOR]

Addressing privacy concerns is critical in smart manufacturing where sensitive data is used for machine learning. Data protection is essential to ensure model accuracy while upholding data privacy. Homeomorphic encryption, an algorithm for privacy-preserving machine learning, achieves this by transforming data using a neural network with secret key weights. This process conceals private data while retaining the potential to learn classification models from the anonymized data. This paper introduces a comprehensive quality metric to assess homeomorphic encryption across conflicting criteria: security (regarding private data), machine learning adaptability (tolerance), and efficiency (regarding needed extra resources). Through experiments on various datasets, the metric proves its effectiveness in guiding optimal encryption parameter selection. Our findings highlight homeomorphic encryption's strong overall quality, positioning it as a valuable Industry 4.0 solution. By offering a simpler alternative to fully homomorphic encryption, it effectively addresses privacy concerns and enhances data usability in the context of smart manufacturing. [ABSTRACT FROM AUTHOR]

This paper demonstrates how the application of machine learning techniques can be used in Magnetron Sputtering (MS) processes, to detect anomalies and reduce their failure rate. Magnetron Sputtering is a widely used technique in materials science and engineering to deposit thin films of various materials for a range of applications. However, the process is complex and can be prone to various anomalies that can lead to defects in the deposited films, resulting in a non-negligible waste of coated objects. In this paper, we focus on the use of machine learning algorithms for both online and offline anomaly detection, which can help identify and diagnose process anomalies in real-time or post-process. Our results demonstrate that machine learning techniques can be used to develop anomaly detection systems, to limit failure in magnetron sputtering processes. • Simple machine learning tools can be applied to magnetron sputtering processes to detect anomalies, offline or online. • This innovative approach can limit failure in magnetron sputtering processes • Although machine learning is not common for surface scientist, this approach is reliable and based on accessible algorithms • The algorithms assist the operator in the decision making without seeking to replace him. [ABSTRACT FROM AUTHOR]

From the perspective of the Industry 4.0 paradigm, the machine learning (ML) discipline has had a significant influence on the manufacturing sector. The industry 4.0 concept promotes intelligent sensors, gadgets, and equipment to create technology infrastructure sectors that collect information constantly. By analyzing the obtained data, machine learning approaches allow actionable insight to boost industrial productivity without dramatically altering the necessary resources. Furthermore, the capacity of machine learning applications to provide actionable analytics has facilitated the detection of complex manufacturing trends and paved the path for an integrated intelligent process in various activities in the supply chain, including smart and constant inspection, preventative maintenance, quality enhancement, process optimization, supply chain advancement, and workflow scheduling. This paper aims to present recent advances in the field of quality inspection in Industry 4.0 and develop a framework for quality inspection that can be fully utilized in the Industry 4.0 context using adaptive bilateral filtering and Feature Correlated Auto encoder (FCA) machine learning technique. The suggested approach makes full use of information from all sources along the manufacturing chain. Therefore, it complies with quality management standards within the context of Industry 4.0. The suggested model makes use of corrective measures based on data patterns discovered through predictive analysis. Result analysis was shown on some pre-trained deep learning models such as ResNet18, Vgg19, Alexnet, Squeezenet, auto encoder, and FCA and observed that the proposed FCA(Feature Correlated Auto encoder) achieved a better result. [ABSTRACT FROM AUTHOR]

For the last few years, one can see the emergence of a large number of intelligent products and services, their commercial availability and the socioeconomic impact, this raises the question if the present emergence of AI is just hype or does it really have the capability of transforming the world. The paper investigates the wide range of implications of artificial intelligence (AI), and delves deeper into both positive and negative impacts on governments, communities, companies, and individuals. This paper investigates the overall impact of AI - from research and innovation to deployment. The paper addresses the influential academic achievements and innovations in the field of AI; their impact on the entrepreneurial activities and thus on the global market. The paper also contributes in investigating factors responsible for the advancement of AI. For the exploration of entrepreneurial activities towards AI, two lists of top 100 AI start-ups are considered. The inferences obtained from the research will provide an improved understanding of the innovations and the impact of AI on businesses and society in general. It will also provide a better understanding of how AI can transform the business operations and thus the global economy. [ABSTRACT FROM AUTHOR]

The evolution of journal bearings, from fixed profiles to adaptive configurations, signifies a captivating progression in journal bearing technology. A notable innovation in adaptive bearings is Multi-pad Bidirectional Adjustable Bearing (MBAB), which can control film thickness in radial and circumferential directions. This paper introduces a novel data-driven approach, utilizing machine learning to model and optimize the static performance of MBABs with asymmetric bearing element adjustments. The study uses a parameter-independent Jaya algorithm coupled with machine learning models to identify optimal combinations of adjustments. Results highlight the significance of negative radial adjustments and asymmetric profiles for optimal performance. This research contributes to Industry 4.0 by bridging the physical-digital gap, offering a data-driven solution to enhance the performance of these advanced bearings. [ABSTRACT FROM AUTHOR]

Localization models, particularly for indoor applications like autonomous vehicles and smart manufacturing in Industry 4.0, have emerged as a crucial research area because of the growing need for precise positioning. In this context, distributed cell-free MIMO have demonstrated capabilities for a higher fine-grained resolution and accuracy. This paper explores different approaches to address challenges related to positioning and corresponding data transmission and model compression problems in cell-free massive MIMO systems. We present a novel deep-learning powered CSI compression model for reducing the signalization data associated with the localization task, minimizing the fronthaul capacity requirement by at least a factor of 2, without a significant loss of accuracy. In our research, we also focus on the challenges of deploying deep end-to-end neural networks on resource-constrained embedded platforms. Two approaches are proposed: model modification and magnitude-based pruning. Our results show that the pruned end-to-end localization model has a smaller size by 36% at the cost of an increase on the localization error by a factor of 1.5 (precision is reduced from approximately 5 mm to 7.8 mm). [ABSTRACT FROM AUTHOR]

Recent advancements in cloud computing and software technology have resulted in the development of powerful Artificial Intelligence (AI) tools for engineering applications. However, the impact of AI in future engineering jobs remains ambiguous. This paper discusses recent AI developments, AI applications, the influence of AI on the Engineering profession, and the productivity of engineers. In addition, ethics, and professional impacts to be considered with the introduction of AI are addressed. The results of a survey conducted among people from Engineering colleges across Ireland are also presented. [ABSTRACT FROM AUTHOR]

Industry 4.0 is largely data-driven nowadays. Owners of the data, on the one hand, want to get added value from the data by using remote artificial intelligence tools as services, on the other hand, they concern on privacy of their data within external premises. Ideal solution for this challenge would be such anonymization of the data, which makes the data safe in remote servers and, at the same time, leaves the opportunity for the machine learning algorithms to capture useful patterns from the data. In this paper, we take the problem of supervised machine learning with deep feedforward neural nets and provide an anonymization algorithm (based on the homeomorphic data space transformation), which guarantees privacy of the data and allows neural networks to learn successfully. We made several experiments to show how much the performance of the trained neural nets will suffer from the deepening of the anonymization power. [ABSTRACT FROM AUTHOR]

Production activities are generating a large amount of data in different types (i.e., text, images), that is not well exploited. This data can be translated easily to knowledge that can help to predict all the risks that can impact the business, solve problems, promote efficiency of the manufacturing to the maximum, make the production more flexible and improving the quality of making smart decisions, however, implementing the Smart Manufacturing(SM) concept provides this opportunity supported by the new generation of the technologies. Internet Of Things (IoT) for more connectivity and getting data in real time, Big Data to store the huge volume of data and Deep Learning algorithms(DL) to learn from the historical and real time data to generate knowledge, that can be used, predict all the risks, problem solving, and better decision-making. In this paper, we will introduce SM and the main technologies to success the implementation, the benefits, and the challenges. [ABSTRACT FROM AUTHOR]

In the Industry 4.0 era, a myriad of sensors of diverse nature (temperature, pressure, etc.) is spreading throughout the entire value chain of industries, being potentially exploitable for multiple purposes, such as Predictive Maintenance (PdM): the just-in-time maintenance of industrial assets, which results in reduced operating costs, increased operator safety, etc. Nowadays, industrial processes require to be highly configurable, in order to proactively adapt their operation to diverse factors such as user needs, product updates or supply chain uncertainties. This limits current Industry 4.0-PdM solutions, typically consisting of ad-hoc developments intended for specific scenarios, i.e. they are designed to operate under certain conditions (configurations, employed sensors, etc.), being unable to manage changes in their setup. This paper presents a general Deep Learning (DL) architecture, MachNet, which deals with such heterogeneity and is able to address PdM problems of a diverse nature. The modularity of the proposed architecture enables it to deal with an arbitrary number of sensors of different types, also allowing the integration of prior information (age of assets, material type, etc.), which clearly affects performance and is often neglected. In practice, our architecture effortlessly adapts to the assets' specifications and to different PdM problems. That is, MachNet becomes an architectural template that can be instantiated for a given scenario. We tested our proposal in two different PdM-related problems: Health State (HS) and Remaining-useful-Life (RuL) estimation, achieving in both cases comparable or superior performance to other state-of-the-art approaches, with the additional advantage of the generality that MachNet offers. [ABSTRACT FROM AUTHOR]

In the railway domain, rolling stock maintenance affects service operation time and efficiency. Minimizing train unavailability is essential for reducing capital loss and operational costs. To this aim, prediction of failures of rolling stock equipment is crucial to proactively trigger proper maintenance activities. Indeed, predictive maintenance is a golden example of the digital transformation within Industry 4.0, which affects several engineering processes in the railway domain. Nowadays, it may leverage artificial intelligence and machine learning algorithms to forecast failures and schedule the optimal time for maintenance actions. Generally, rail systems deteriorate gradually over time or fail directly, leading to data that vary extremely slowly. Indeed, ML approaches for predictive maintenance should consider this type of data to accurately predict and forecast failures. This paper proposes a methodology based on Long Short-Term Memory deep learning algorithms for predictive maintenance of railway rolling stock equipment. The methodology allows us to properly learn long-term dependencies for gradually changing data, and both predicting and forecasting failures of rail equipment. In the framework of an academic-industrial partnership, the methodology is experimented on a train traction converter cooling system, demonstrating its applicability and benefits. The results show that it outperforms state-of-the-art methods, reaching a failure prediction and forecasting accuracy over 99%, with a false alarm rate of ∼ 0. 4 % and a mean absolute error in the order of 1 0 − 4 , respectively. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution creates ubiquitous sensor data in production plants. To generate maximum value out of these data, reliable and precise time series-based machine learning methods like temporal neural networks are needed. This paper proposes a novel sequence-to-sequence deep learning architecture for time series segmentation called PrecTime which tries to combine the concepts and advantages of sliding window and dense labeling approaches. The general-purpose architecture is evaluated on a real-world industry dataset containing the End-of-Line testing sensor data of hydraulic pumps. We are able to show that PrecTime outperforms five implemented state-of-the-art baseline networks based on multiple metrics. The achieved segmentation accuracy of around 96% shows that PrecTime can achieve results close to human intelligence in operational state segmentation within a testing cycle. [ABSTRACT FROM AUTHOR]

This paper presents a comparative assessment of two-dimensional convolutional neural networks (2DCNN) and boosting methods for regression-based textile whiteness estimation, applied to high resolution images of textiles of an industrial cotton textiles producer, labeled with whiteness values, thus enabling supervised learning. The images were taken under various lighting conditions. Concerning the machine learning methods, Random Forest and XGBoost were the selected and tested boosting techniques on which model hyper-parameter tuning was applied, whereas regarding the 2DCNN architectures, the known from literature ColorNet architecture was selected and a more shallow one, called WERegNet, was introduced. Data augmentation was applied during pre-processing, due to the limited amount of available data. Based on the simulation results, the WERegNet architecture surpasses ColorNet and XGBoost in terms of performance, while it is comparable with Random Forest on test set, based on model selection measure Normalized Root Mean Squared Error. [ABSTRACT FROM AUTHOR]

Industry 4.0 is the new industrial revolution. By connecting every machine and activity through network sensors to the Internet, a huge amount of data is generated. Machine Learning (ML) and Deep Learning (DL) are two subsets of Artificial Intelligence (AI), which are used to evaluate the generated data and produce valuable information about the manufacturing enterprise, while introducing in parallel the Industrial AI (IAI). In this paper, the principles of the Industry 4.0 are highlighted, by giving emphasis to the features, requirements, and challenges behind Industry 4.0. In addition, a new architecture for AIA is presented. Furthermore, the most important ML and DL algorithms used in Industry 4.0 are presented and compiled in detail. Each algorithm is discussed and evaluated in terms of its features, its applications, and its efficiency. Then, we focus on one of the most important Industry 4.0 fields, namely the smart grid, where ML and DL models are presented and analyzed in terms of efficiency and effectiveness in smart grid applications. Lastly, trends and challenges in the field of data analysis in the context of the new Industrial era are highlighted and discussed such as scalability, cybersecurity, and big data. [ABSTRACT FROM AUTHOR]