Search

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]

Warehouse design and planning involve complex decisions on receiving, storage, order picking and shipping products (i.e., stock-keeping units - SKUs) and can affect the performance of entire supply chains. With the advancement of Industry 4.0 and increased data availability, high-computing power, and ample storage capacity, Machine Learning (ML) has become an appealing technology to address warehouse planning challenges such as Storage Location Assignment Problems (SLAP) and Order Picking Problems (OPP) for intelligent warehousing management. This paper presents a state-of-the-art review of ML applied to Warehouse Management Systems (WMS) through the analysis of recent research application articles. A mapping to classify the scientific literature in this new research area, including ML methods, algorithms, data sources and use cases of ML-aided WMS, as well as further research perspectives and challenges, are introduced. Preliminary results suggest that the possible research areas in ML-WMS are still incipient and need to be further explored. [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]

Industry 4.0 uses artificial intelligence and machine learning algorithms to optimize processes. The main goal of the manuscript is to analyze the possibilities of applying automated machine learning to predict company bankruptcy. The data sample consists of financial data of 9,771 manufacturing companies for the years 2020 and 2021 collected from the Finstat database. Two methods of automated machine learning, AutoML and H2O, were tested. The results were compared with five other methods - linear discriminant analysis, logistic regression, naive Bayes classifier, CatBoost and XGBoost. The resulting model was cross-validated through the 10-fold approach. The best results were achieved by H2O automated machine learning algorithm with an AUC of 90.13%, followed by the gradient boosting methods CatBoost with AUC of 90.05% and XGBoost (AUC of 88.61%) and another automated learning algorithm AutoML with AUC of 81.17%. The findings of this paper indicate possibilities to apply automated machine learning methods in predicting bankruptcy. However, it is necessary to distinguish between individual automated machine learning algorithms since they provide a different range of results. [ABSTRACT FROM AUTHOR]

A neural network (NN) is known to be an efficient and learnable tool supporting decision-making processes particularly in Industry 4.0. The majority of NNs are data-driven and, therefore, depend on training data quantity and quality. The current trend in enhancing data-driven models with knowledge-based models promises to enable effective NNs with less data. So-called physics-informed NNs use additional knowledge from computational science to improve NN training. Quite much of the knowledge is available as logical constraints from domain ontologies, and NNs may benefit from using it. In this paper, we study the concept of Taxonomy-Informed NN (TINN), which combines data-driven training of NNs with ontological knowledge. We study different patterns of NN training with additional knowledge on class-subclass hierarchies and instance-class relationships with potential for federated learning. Our experiments show that additional knowledge, which influences TINNs' training process through the loss function at backpropagation, improves the quality of trained models. [ABSTRACT FROM AUTHOR]

Manufacturing processes have undergone tremendous technological progress in recent decades. To meet the agile philosophy in industry, data-driven algorithms need to handle growing complexity, particularly in Computer Numerical Control machining. To enhance the scalability of machine learning in real-world applications, this paper presents a benchmark dataset for process monitoring of brownfield milling machines based on acceleration data. The data is collected from a real-world production plant using a smart data collection system over a two-years period. In this work, the edge-to-cloud setup is presented followed by an extensive description of the different normal and abnormal processes. An analysis of the dataset highlights the challenges of machine learning in industry caused by the environmental and industrial factors. The new dataset is published with this paper and available at: https://github.com/boschresearch/CNC_Machining. [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]

Cloud Manufacturing matches supplier capacities and customer demands via an algorithmic agent. The agent aims to maximize its own and the platform participant's revenue, hence the platform's gross revenue. Volatilities in supply and demand, as well as individual pricing models and willingness to pay, exacerbate the problem for deterministic solvers. In Revenue Management, comparable problems are approached with linear programming and regression models. Within this paper, we introduce a new Machine Learning (ML) based approach: Machine Learning for Cloud Manufacturing Cost Prediction (ML-ConCord). ML-ConCord aims to improve prediction quality for Revenue Management in Cloud Manufacturing by combining a recurrent neural network (RNN) with a linear programming pricing algorithm. We evaluate our approach on a self-generated dataset and compare its performance with standard regression-based approaches. Models and datasets are published on GitHub. [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]

Detecting the anomalies in a large amounts of high-dimensional data has been a challenging task. In the Industry 4.0 environment, large-scale high-dimensional monitoring data features the complex pattern of high level semantics. In order to provide enterprise-wide monitoring solutions, it is necessary to identify the high-level semantic patterns of the anomalies in these data without splitting them. Existing end-to-end deep neural networks for time series are capable of recognizing the high-level semantics in natural language or speech signals, but they are barely applied in real-time anomaly detection of industrial data because of the large time costs. In this paper, we leverage the self-supervised contrastive learning methodology and propose a Composite Semantic Augmentation Encoder (CSAE) to provide an appropriate representation of industrial data and implement quick detection of anomalies in industrial application environments. CSAE is a non-sequential deep neural network with two augmentation layers and a mandatory layer. The two layers of data-augmentation are built to expand the size of samples of both low-level semantic anomalies and high-level semantic anomalies, which enables CSAE to discover diverse anomalies and improves its accuracy of high-level semantic pattern recognition. The mandatory layer is built to compress and reserve the temporal information in the industrial data to accelerate the anomaly detection. Therefore, as a non-sequential contrastive learning model, CSAE has faster training convergence than the usual sequence models. The experiment results have verified that CSAE can achieve higher prediction accuracy with less time consumption than existing machine learning models in the tasks of high dimensional anomaly pattern detection. [ABSTRACT FROM AUTHOR]

Analyzing sensor data from a production environment is quite challenging because of the high-dimensional nature of the data. In addition, the generated data is in the form of time-series, where the sequence of registrations may be of utmost significance. One of the main goals of the paper is to determine if the given time-series of feature combinations is normal or rare. This goal could successfully be achieved by combining multiple machine learning models. In this paper, a sliding window based ensemble method is proposed to detect outliers in a streaming fashion. The proposed method uses a combination of clustering algorithms to construct subgroups (clusters) representing different data structures. These structures are later used in a one-class classification algorithm to identfy the outliers. Thus, if a pattern does not belong to any of the common structures or clusters, it is an outlier. Further, based on the rare pattern classification, machine failures could be predicted in advance. [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]

A main aspect of the digitalisation of production processes is the increase of process flexibility. An option to raise the flexibility is a sufficient reaction to unplanned process interruptions. Disruption management supports decision-makers to reduce the influence of such situations on the production process. This paper describes a task prioritisation method for disruption correction, based on the maximal network plan technique. The proposed algorithm combines machine-learning techniques with expert knowledge to raise the time-cost efficiency. As a case study, the assembly and integration of Ariane 6 upper composite in the ArianeGroup plant in Bremen is used. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Human Computer Interaction (HCI) often intersect under the purview of different technologies in a plethora of application areas, wherein it becomes pivotal to identify and interpret the exact roles of both the disciplines. Stakeholders involved in the fusion of AI and HCI range from engineers, scientists, philosophers and technologists to psychologists, cognitive scientists, graphic designers and researchers across a wide spectrum of realms. While it is important to develop intelligent interactive systems owing to the ubiquitous and pervasive nature of data ushering in the next era of AI, it is all the more important to develop these systems in-tandem with the aforementioned stakeholders, and in a way that users exercise appropriate control over the AI that is been developed. Human-in-the-loop systems and operator assistive technologies, especially in the context of Industry 4.0 are examples of a fabulous fusion of AI-HCI, wherein the technology that is being used works by keeping the humans in the loop, thereby paving the way for a robust, transparent, ethical, intelligent and interactive mechanism for the co-existence of AI and HCI systems. In this paper, we aim to bank upon the state-of-the-art literature that has been written previously in this regard and derive though provoking research questions. We also put forth challenges, case studies, application areas and come up with solutions, along with few recommendations that can be viably used in developing frameworks and mechanisms for the synergic co-existence of these two challenging, yet interesting fields of AI and HCI. [ABSTRACT FROM AUTHOR]

Product variety and its induced manufacturing complexity remains to increase and therefore greatens challenges for design of joining elements. Historically, joining element design was a paper-based process with incomplete variety documentation and is digitalized only by replacing paper for 3D space. Currently, joining element design remains an ambiguous manual task with limited automation, resulting in long iterative, error prone development trajectories and costly reworks. Thus, processes in practice conflict with required capabilities. Artificial intelligence helps to solve such conflicts by taking over repetitive tasks, preventing human errors, optimizing designs and enabling designers to focus on their core competencies. This paper proposes a novel artificial intelligence method toolbox as a foundation to automate joining element design in manufacturing industries. The methodology aims to incorporate multiple lifecycle requirements including large product variety. [ABSTRACT FROM AUTHOR]

Abstract With the arrival of Industry 4.0, the overall transformation using digital integration and intelligent engineering has taken a giant leap towards futuristic technology. All devices today are equipped with machine learning, automation has become a priority and thus another industrial revolution is in the making. In this state-of-the-art paper, we have performed bibliometric analysis and an extensive survey on recent developments in the field of "Industry 4.0". In bibliometric analysis, different performance metrics are extracted, such as: total papers, total citations, and citation per paper. Further, top 10 of the most productive and highly cited authors, major subject areas, sources or journals, countries, and institutions are evaluated. A list of highly influential papers is also assessed. Later on, a detailed discussion of the most cited papers is analysed and a sectional classification is provided. This paper summarizes the growth structure of Industry 4.0 during the last 5 years and provides the concise background overview of Industry 4.0 related works and various application areas. [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]

Advanced manufacturing processes generate large number of parameters and sequences collected from multiple stages of the production pipeline. Consequently, the data dimension explodes due to the high number of features and sequence length. In such a configuration, it is extremely challenging for machine learning algorithms to perform well in limited datasets of few thousand data points. In this paper, we discuss suitable state-of-the-art techniques and propose the application and comparison of few most promising methodologies for effective feature transformation to address this challenge. The results are presented on real-world use cases from the manufacturing sector in Luxembourg. [ABSTRACT FROM AUTHOR]

With the rise of the Internet of Things and Industry 4.0, the number of digital devices and their produced data increases tremendously. Due to the heterogeneity of devices, the generated data is mostly heterogeneous and unstructured. This challenges established approaches for knowledge discovery, which typically consume structured data from one source. The paper first describes aspects of data heterogeneity and their relevance for Industry 4.0 systems. Following, the upcoming challenges for different steps inside the knowledge discovery process for Industry 4.0 systems, such as for data integration and data mining, are discussed. Additionally, it mentions approaches to tackle them. [ABSTRACT FROM AUTHOR]

The production industry is in a transformation towards more autonomous and intelligent manufacturing. In addition to more flexible production processes to dynamically respond to changes in the environment, it is also essential that production processes are continuously monitored and completed in time. Video-based methods such as object detection systems are still in their infancy and rarely used as basis for process monitoring. In this paper, we present a framework for video-based monitoring of manufacturing processes with the help of a physical smart factory simulation model. We evaluate three state-of-the-art object detection systems regarding their suitability to detect workpieces and to recognize failure situations that require adaptations. In our experiments, we are able to show that detection accuracies above 90% can be achieved with current object detection methods. [ABSTRACT FROM AUTHOR]

Modern production systems are composed of complex manufacturing processes with highly technology specific cause-effect relationships. Developments in sensor technology and computational science allow for data-driven decision making that facilitate effcient and objective production management. However, process data may only be beneficial if it is enriched with meta information and process expertise, reduced to relevant information and modelling results interpreted correctly. The importance of data integration in the heterogeneous industrial environment rises at the same momentum as new metrology techniques are deployed. In this paper, we focus on optimizing analytics, containing data-driven decision making for predictive quality and maintenance. We summarize key requirements for data analytics and machine learning application in industrial processes. With a use case from automotive component manufacturing we characterize industrial production, categorize process data and put requirements in context to a real-world example. [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]

More and more manufacturers, as part of the transition towards Industry 4.0, are using Internet of Things (IoT) networks for more efficient production. The wide and extensive expansion of IoT devices and the variety of applications generate different challenges, mainly in terms of reliability and energy efficiency. In this paper, we propose an approach to optimize the performance of IoT networks by making the IoT devices intelligent using machine learning techniques. We formulate the optimization problem as a massive multi-player multi-armed bandit and introduce two novel policies: Decreasing-Order-Reward-Greedy (DORG) focuses on the number of successful transmissions, while Decreasing-Order-Fair-Greedy (DOFG) also guarantees some measure of fairness between the devices. We then present an efficient way to manage the trade-off between transmission energy consumption and packet losses in Long-Range (LoRa) networks using our algorithms, by which LoRa nodes adjust their emission parameters (Spreading Factor and transmitting power). We implement our algorithms on a LoRa network simulator and show that such learning techniques largely outperform the Adaptive Data Rate (ADR) algorithm currently implemented in LoRa devices, in terms of both energy consumption and packet losses. [ABSTRACT FROM AUTHOR]

The wider availability of low-cost sensing and data acquisition technologies means that real-time sensing of tribological parameters is becoming increasingly viable. Consequently, the potential to use these technologies to monitor in-service tribological components has increased significantly. This paper presents a review of a number of state-of-the-art in sensors for measuring friction, wear and lubricant properties. It also elaborates on the use of sensor coatings as an emerging area for directly probing the tribological interface. It is concluded that sensors will find ever increasing uses in condition monitoring" applications. However, sensing and tribology is beginning to evolve towards "Tribotronics" where combining the sensing of machine elements that have conventionally been passive with computational capability, or even embedded intelligence, along with actuation can create active machine elements, optimised to operate with say minimum power loss in all situations of duty. Additionally, it is noted that by incorporating sensing and responsive capabilities, functional surfaces can also become part of a bigger connected systems particularly in association with Industry 4.0. Increased use of sensors in tribological components alongside machine learning and artificial intelligence, will also support the shift in industrial tribological analytics. [ABSTRACT FROM AUTHOR]

The evaluation of process improvements measures in offsite construction shop floors often relies on experts' opinion, with limited use of empirical data gathered by sensors in real-time. To address this issue, there is a need for methods that integrate expert's tacit knowledge with robust data analysis techniques. This paper describes the application of exploratory data analysis techniques to evaluate improvement suggestions proposed by expert's, supported by data collected by sensors on the shop floor and building information models. The presented method involves a quantitative and qualitative digitalization-based approach where improvement suggestions are modelled and validated though machine learning algorithms and hypothesis testing. The contribution of this study is a method that combines real-time data, building information models, and knowledge modeling from experts to evaluate process improvement on offsite construction shop floors. • A method to assess improvements based on experts input and real-time data. • Machine learning is applied to analyze data from RFID sensors and BIM models. • The automation in workstations is rated based on production balance and efficiency. • Strategies to increase production flexibility are rated using statistical analysis. [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]

This paper analyzes the process variable data from injection molding processes to identify the key process variables, which can be predicted by other process variables, which highlights the interdependence among different process variables in various production scenarios. The available data from injection molding machines provide information for the run-time, setup parameters of machines, and measurements of different process variables through sensors. For predictive modeling, we employed different linear regression models with the recursive backward feature selection and SVM regression models using a radial kernel to predict nonlinear process variables. We also applied the linear and SVM regression models for outlier data in the process variables assuming that the upper bound outliers represent the perturbed state of process variables during production. These perturbations are affected by material type, machine type (age and performance), regime changes, or other external effects and subsequently affect the predictability of process variables and production output. Such cases are different compared to the normal or controlled range of process variables. Our analysis shows that the predictability varies for different material types due to the interdependence of the associated process variables. We further highlight that various process variables exhibit nonlinear relationships and cannot be predicted using linear models. We additionally look for the interdependence of process variables used previously by three studies as input features to predict product quality. [ABSTRACT FROM AUTHOR]

There can be many reasons for anyone to make a digital copy (clone) of own decision-making behavior. This enables virtual presence of a professional decision-maker simultaneously in many places and processes of Industry 4.0. Such clone can be used as one's responsible representative when the human is not available. Pi-Mind ("Patented Intelligence") is a technology, which enables "cloning" cognitive skills of humans using adversarial machine learning. In this paper, we present a cyber-physical environment as an adversarial learning ecosystem for cloning image classification skills. The physical component of the environment is provided by the logistic laboratory with camera-surveillance over the conveyors. The digital component of the environment contains special modifications of Generative Adversarial Networks, which include a human-operator as a trainer, an autonomous Pi-Mind clone as a trainee (a discriminator) and a smart digital adversary as a challenger (generator of sophisticated decision situations, emergencies and attacks, which supposedly catalyzes the cloning process). [ABSTRACT FROM AUTHOR]

Industry 4.0 is associated with numerous technologies, which offer great potential for optimizing linear winding processes as used in electric motor manufacturing. To further increase flexibility and quality in the production of mass products like coils, data-driven techniques such as machine learning (ML) are increasingly moving into focus. To generate the required data, however, linear winding machines must be equipped with suitable sensors. Accordingly, this paper will first present all known influencing and quality parameters in linear winding based on earlier research work. Based on this, suitable data sources and sensor systems are identified and a concept for applying ML techniques is derived. [ABSTRACT FROM AUTHOR]

Normalisation is a preprocessing technique widely employed in Machine Learning (ML)-based solutions for industry to equalise the features' contribution. However, few researchers have analysed the normalisation effect and its implications on the ML algorithm performance, especially on Euclidean distance-based algorithms, such as the well-known K-Nearest Neighbours and K-means. In this sense, this paper formally analyses the effect of normalisation yielding results significantly far from the state-of-the-art traditional claims. In particular, this paper shows that normalisation does not equalise the contribution of the features, with the consequent impact on the performance of the learning process for a particular problem. More concretely, this demonstration is made on K-Nearest Neighbours and K-means Euclidean distance-based ML algorithms. This paper concludes that normalisation can be viewed as an unsupervised Feature Weighting method. In this context, a new metric (Normalisation weight) for measuring the impact of normalisation on the features is presented. Likewise, an analysis of the normalisation effect on the Euclidean distance is conducted and a new metric referred to as Proportional influence that measures the features influence on the Euclidean distance is proposed. Both metrics enable the automatic selection of the most appropriate normalisation method for a particular engineering problem, which can significantly improve both the computational cost and classification performance of K-Nearest Neighbours and K-means algorithms. The analytical conclusions are validated on well-known datasets from the UCI repository and a real-life application from the refinery industry. [ABSTRACT FROM AUTHOR]

New developments in domains like mathematics and statistical learning and availability of easy-to-use, often freely accessible software tools offer great potential to transform the manufacturing domain and their grasp on the increased manufacturing data repositories sustainably. One of the most exciting developments is in the area of machine learning. Time series clustering could be utilized in machine state detection which can be used in predictive maintenance or online optimization. This paper presents a comparison of freely available time series clustering algorithms, by applying several combinations of different algorithms to a database of public benchmark technical data. [ABSTRACT FROM AUTHOR]

Discrete Event Simulation (DES) provides a far-reaching set of methods for planning and improving structures and processes of real factories. However, the use of DES is hampered by the high effort required for creating and parameterizing the corresponding models. Regarding the material flow of production systems with different products and small lot sizes, the expenditure for identifying current and historical processes and their parameters in the available data is time-consuming. Therefore, this paper presents an approach where machine learning algorithms identify these process specific parameters. Following that, the algorithms are integrated into the material flow simulation to parameterize the elements in the model. [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]

The world is moving at a fast pace with IoT and AI. Industry 4.0 is becoming a mandatory standard in many industries round the globe. It is the need of the hour to bring about a drastic change in the educational system. In this paper academia 4.0 is proposed to bring out best graduates with the required skill sets to match the industry. The proposed model is a replica of the Industry 4.0 standard, but customized for engineering education. AI and machine learning are applied to make decisions at various stages. The database of the students would be collected for a period of 7 years. The expected outcomes would be measured in terms of the students being hired in the relevant industries and their continuous growth in their area of choice. The system would be trained using AI to take right decisions at different points in the professional career of the student. The deciding factors are based on the Indian educational system. [ABSTRACT FROM AUTHOR]

Industry 4.0 is the most recent industrial revolution that aims to improve not only the productivity in the 21st century, but also the flexibility, adaptability, and resilience of the industrial systems. It enables the collection of real-time data from industrial systems, Thanks to the development of Internet of Things (IoT) technology. Hence, analyzing online collected data enables to deal with several industrial issues in real-time such as machines' break- or slow-downs, quality crisis, flows disruptions, etc. In traditional industrial systems, previous works focused on both scheduling and rescheduling schemes in order to improve the system performance. However, few works dealt with system disruption monitoring due to the lack of real-time data about the system running. Furthermore, the remote and constant monitoring amenities were not established yet, properly. In this paper, we propose a system disruption monitoring tool in Industry 4.0 system. Our tool focuses on system disruption related to resources localization, or when a resource is in an unexpected location. Thus, a machine learning algorithm is used to generate a prediction model of resources localization by considering the real tasks scheduling in terms of resources localization. Therefore, as real resources localization can be collected from the industrial system through the IoT network, our tool enables to detect system disruption, risk, in real-time when comparing predicted localization to the real one. Moreover, our tool is executed in a Fog computing architecture which is emerging as an extension of cloud computing to provide local processing support with good latency. The experimental results show the efficiency of our tool in terms of prediction accuracy and time complexity when compared to other machine learning algorithms, in addition to its ability to control and detect system disruption in real-time. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, an emerging trend is to increase the reliability of industrial process by using machine learning (ML) to detect anomalies of production machines. The main advantages of ML are in the ability to (1) capture non-linear phenomena, (2) adapt to many different processes without human intervention and (3) learn incrementally and improve over time. In this paper, we take the perspective of IT system architects and analyse the implications of the inclusion of ML components into a traditional anomaly detection systems. Through a prototype that we deployed for chemical reactors, our findings are that such ML components are impacting drastically the architecture of classical alarm systems. First, there is a need for long-term storage of the data that are used to train the models. Second, the training and usage of ML models can be CPU intensive and may request using specific resources. Third, there is no single algorithm that can detect machine errors. Fourth, human crafted alarm rules can now also include a learning process to improve these rules, for example by using active learning with a human-in-the-loop approach. These reasons are the motivations behind a microservice-based architecture for an alarm system in industrial machinery. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0, predictive maintenance is regarded as a key factor for reaching business objectives. Cost-effective deployment of Cyber Physical Production Systems raises the question whether data-driven and knowledge-based maintenance affects profitability of smart factories. Several studies reveal that an appropriate data-driven maintenance strategy should not only focus on increasing availability but also should consider economic parameters. This paper presents a novel approach and a proof-of-concept demonstrator using Dynamic Bayesian Networks (DBN). The proposed DBN model enables identifying and predicting the economic impact of maintenance on profitability as well as support planning and monitoring of maintenance activities. [ABSTRACT FROM AUTHOR]

Highlights • We overview the state of art in data fusion and analysis for industrial prognosis. • Descriptive, predictive and prescriptive prognostic models are reviewed. • Future trends and challenges are provided to stimulate research on this topic. Abstract The so-called "smartization" of manufacturing industries has been conceived as the fourth industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and progressive maturity of new Information and Communication Technologies (ICT) applied to industrial processes and products. From a data science perspective, this paradigm shift allows extracting relevant knowledge from monitored assets through the adoption of intelligent monitoring and data fusion strategies, as well as by the application of machine learning and optimization methods. One of the main goals of data science in this context is to effectively predict abnormal behaviors in industrial machinery, tools and processes so as to anticipate critical events and damage, eventually causing important economical losses and safety issues. In this context, data-driven prognosis is gradually gaining attention in different industrial sectors. This paper provides a comprehensive survey of the recent developments in data fusion and machine learning for industrial prognosis, placing an emphasis on the identification of research trends, niches of opportunity and unexplored challenges. To this end, a principled categorization of the utilized feature extraction techniques and machine learning methods will be provided on the basis of its intended purpose: analyze what caused the failure (descriptive), determine when the monitored asset will fail (predictive) or decide what to do so as to minimize its impact on the industry at hand (prescriptive). This threefold analysis, along with a discussion on its hardware and software implications, intends to serve as a stepping stone for future researchers and practitioners to join the community investigating on this vibrant field. [ABSTRACT FROM AUTHOR]

Recent trends like autonomous driving, natural language processing, service robotics or Industry 4.0 are mainly based on the tremendous progress made in the field of machine learning (ML). The increased data availability coupled with affordable computing power and easy-to-use software tools have laid the foundation for using such algorithms in a wide range of industrial applications, e.g. for predictive maintenance, predictive quality or machine vision. However, a systematic guideline for identifying and implementing economically viable ML use cases in manufacturing industry is still missing. In particular, there is still a lack of a structured overview of concrete, industry-specific best practices that can be easily transferred to one' s own production. Hence, this paper aims to summarize various existing application scenarios of ML from a process and an industry sector perspective. The process point of view mainly covers the main manufacturing process groups of DIN 8580, handling operations according to VDI 2860 as well as selected cross-process approaches. From an industry sector perspective, application scenarios from various subsectors such as the production of electronics, electric motors, transmission components and medical devices are outlined. [ABSTRACT FROM AUTHOR]

3D printing technology is considered one of the emerging areas to deal with global sustainability challenges and to facilitate the Industry 4.0 adoption. However, 3D printing technology is still immature due to several limitations and negative perceptions about its quality and performance. The goal of this paper is to propose a cyber physical production system (CPPS) framework for a 3D printer to (i) monitor the process, parameters, and carbon footprint, (ii) predict the nozzle's remaining useful life (RUL), and (iii) prescribe optimum 3D printing parameters for minimizing carbon footprint and printing time, simultaneously at the targeted surface quality. Experiments were designed based on Taguchi L-27 orthogonal array to investigate the relationship between printing parameters and performance characteristics. The usefulness of the proposed framework has been demonstrated for a 3D printer to predict the remaining useful life of the printer nozzle (prognostic model), and to find an optimal combination of printing parameters for the simultaneous optimization of sustainability and productivity at the targeted surface quality (prescriptive model). Layer height was found to have a statically significant impact on the specific carbon footprint followed by scale and bed temperature. Layer height is the only statically significant contributor to the surface roughness of 3D printed parts. The scale and layer height followed by infill have significant effect on the printing time. The significance of the present work lies in enhancing the performance of a conventional 3D printer using low-cost smart sensors, devices, and open-source software. The usefulness of the proposed CPPS framework is demonstrated as a decision support tool for a 3D printer real-time monitoring, visualization, and control. The proposed CPPS framework and its application for prognostic and prescriptive analytics is generic in nature, and is transferable and applicable to other FDM 3D printers, irrespective of brand and size. • Prescriptive analytics with inbuilt optimization model. • Prognostic analytics for predicting remaining useful life of a 3D printer nozzle. • Optimizes eco-design of 3D printed parts, based on optimization of printing time and specific carbon footprint. • Dashboard for ease of decision making. • Enhancing the capabilities of a conventional 3D printer using low-cost smart sensors, devices, and open-source software. [ABSTRACT FROM AUTHOR]

• This paper reviews the Business-Management-orientated process mining (PM) literature. • PM case perspective and enhancement type deserve more attention. • PM potential should be more investigated in business functions beyond operations. • PM effectiveness in tactical and strategic decision making should be explored. • PM capabilities should tackle further sector-specific managerial issues. Process Mining is a new kind of Business Analytics and has emerged as a powerful family of Process Science techniques for analysing and improving business processes. Although Process Mining has managerial benefits, such as better decision making, the scientific literature has investigated it mainly from a computer science standpoint and appears to have overlooked various possible applications. We reviewed management-orientated literature on Process Mining and Business Management to assess the state of the art and to pave the way for further research. We built a seven-dimension framework to develop and guide the review. We selected and analysed 145 papers and identified eleven research gaps sorted into four categories. Our findings were formalised in a structured research agenda suggesting twenty-five research questions. We believe that these questions may stimulate the application of Process Mining in promising, albeit little explored, business contexts and in mostly unaddressed managerial areas. [ABSTRACT FROM AUTHOR]

In this paper we introduce the current thrust of development in machine learning and artificial intelligence, fueled by advances in statistical learning theory over the last 20 years and commercial successes by leading big data companies. Then we discuss the characteristics of process manufacturing systems and briefly review the data analytics research and development in the last three decades. We give three attributes for process data analytics to make machine learning techniques applicable in the process industries. Next we provide a perspective on the currently active topics in machine learning that could be opportunities for process data analytics research and development. Finally we address the importance of a data analytics culture. Issues discussed range from technology development to workforce education and from government initiatives to curriculum enhancement. [ABSTRACT FROM AUTHOR]

Emerging technologies like Internet of Things, Data Science, Deep Learning, Augmented Reality, Edge Computing, and Digital Twins are bringing new opportunities, challenges, and solutions for many domains including agriculture, plant sciences, animal sciences, food sciences, and social sciences. These disruptive technologies are at the center of the fourth industrial revolution, but are we ready yet to educate and prepare new generations to help society, science, and humanity adapt them? How can we change the current curriculum to reflect these technological innovations? How can we help the new generation to develop not only left-brain skills but also right-brain skills? The Netherlands is the second largest food exporter in the world after the United States and the agricultural related exports generated €45 Billion in 2018 for the economy. R&D in Dutch companies and innovation in universities in the Netherlands play an important and active role in this context. In this paper, we provide a general framework for supporting education in the context of Industry 4.0. We adopt the case study of Wageningen University at which we were actively involved in designing and customizing academic courses related to Industry 4.0. Wageningen University, which has the highest rank in the field of Agriculture & Forestry according to influential university rankings and has a rank 59 according to Times Higher Education, is traditionally a life science university but has taken also an active strategy for aligning with the developments in IT and Artificial Intelligence. Apart from the content-wise shift, skills such as critical thinking, creativity, and problem-solving are addressed by applying project-based evaluations. We discuss the lessons learned and address the issues related to Industry 4.0 and education. [ABSTRACT FROM AUTHOR]

Industry 4.0 is the most recent industrial revolution that aims to improve not only the productivity in the 21st century, but also the flexibility, adaptability, and resilience of the industrial systems. It enables the collection of real-time data from industrial systems, Thanks to the development of Internet of Things (IoT) technology. Hence, analyzing online collected data enables to deal with several industrial issues in real-time such as machines' break- or slow-downs, quality crisis, flows disruptions, etc. In traditional industrial systems, previous works focused on both scheduling and rescheduling schemes in order to improve the system performance. However, few works dealt with system disruption monitoring due to the lack of real-time data about the system running. Furthermore, the remote and constant monitoring amenities were not established yet, properly. In this paper, we propose a system disruption monitoring tool in Industry 4.0 system. Our tool focuses on system disruption related to resources localization, or when a resource is in an unexpected location. Thus, a machine learning algorithm is used to generate a prediction model of resources localization by considering the real tasks scheduling in terms of resources localization. Therefore, as real resources localization can be collected from the industrial system through the IoT network, our tool enables to detect system disruption, risk, in real-time when comparing predicted localization to the real one. Moreover, our tool is executed in a Fog computing architecture which is emerging as an extension of cloud computing to provide local processing support with good latency. The experimental results show the efficiency of our tool in terms of prediction accuracy and time complexity when compared to other machine learning algorithms, in addition to its ability to control and detect system disruption in real-time. [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]

• We analyze how the Industry 4.0 literature has evolved during its 10 years. • We perform a systematic machine learning-based literature review of 4,973 papers. • We analyze 4 Smarts: S. Manufact., S.Working, S.Supply Chains and S.Product-Services. • We show the growth of these four smart dimensions and the interrelationships. • Future opportunities are highlighted for journals and specific fields of research. The Industry 4.0 literature has exponentially grown in the past decade. We aim to understand how this literature has evolved and propose future research opportunities. We focus on four smart dimensions of Industry 4.0: Smart Manufacturing, Smart Products and Services, Smart Supply Chain, and Smart Working. We perform a machine learning-based systematic literature review. Our analysis included 4,973 papers published from 2011 to 2020. We conducted a chronological network analysis considering the growth of these four dimensions and the connections between them. We also analyzed keywords and the main journals publishing on these four smart dimensions. We show that the literature has mainly been devoted to the study of Smart Manufacturing, although attention to the other smart dimensions has been growing in recent years. Smart Working is the less explored dimension, with many opportunities for future research. We show that research opportunities are concentrated in the interfaces between the different smart dimensions. Our findings support the vision of Industry 4.0 as a concept transcending the Smart Manufacturing field, thus creating opportunities for synergies with other related fields. Scholars can use our findings to understand the orientation of journals and gaps that can be fulfilled by future research. [ABSTRACT FROM AUTHOR]