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A fourth industrial revolution is occurring in global manufacturing. It is based on the introduction of Internet of things and servitization concepts into manufacturing companies, leading to vertically and horizontally integrated production systems. The resulting smart factories are able to fulfill dynamic customer demands with high variability in small lot sizes while integrating human ingenuity and automation. To support the manufacturing industry in this conversion process and enhance global competitiveness, policy makers in several countries have established research and technology transfer schemes. Most prominently, Germany has enacted its Industrie 4.0 program, which is increasingly affecting European policy, while the United States focuses on smart manufacturing . Other industrial nations have established their own programs on smart manufacturing, notably Japan and Korea. This shows that manufacturing intelligence has become a crucial topic for researchers and industries worldwide. The main object of these activities are the so-called cyber-physical systems (CPS): physical entities (e.g., machines, vehicles, and work pieces), which are equipped with technologies such as RFIDs, sensors, microprocessors, telematics or complete embedded systems. They are characterized by being able to collect data of themselves and their environment, process and evaluate these data, connect and communicate with other systems, and initiate actions. In addition, CPS enabled new services that can replace traditional business models based solely on product sales. The objective of this paper is to provide an overview of the Industrie 4.0 and smart manufacturing programs, analyze the application potential of CPS starting from product design through production and logistics up to maintenance and exploitation (e.g., recycling), and identify current and future research issues. Besides the technological perspective, the paper also takes into account the economic side considering the new business strategies and models available.

Recent economic transformations have forced companies to redefine their value propositions, increasing traditional product offerings with supplementary services—the so-called Product-Service System (PSS). Among them, the adoption of Industry 4.0 technologies is very common. However, the directions that companies are undertaking to offer new value to their customers in the Industry 4.0 have not yet been investigated in detail. Based on a focus group, this paper contributes to this understanding by identifying the main trajectories that would shape a future scenario in which PSS and Industry 4.0 would merge. In addition, future research directions addressing (a) the transformation of the PSS value chain into a PSS ecosystem, (b) the transformation inside a single company towards becoming a PSS provider, and (c) the digital transformation of the traditional PSS business model are identified.

Manufacturing, an industry set in the physical world, is undergoing its digital transformation, also known as the fourth industrial revolution. Sensors, connectivity, and platforms provide unprecedented access to quantify the quality and diversity of manufacturing data. Progress in data-driven modeling is exponential across all industries. This leads to the question of how physics-based and data-driven modeling can be utilized in a hybrid modeling approach to advance our understanding of processes, materials, and systems in manufacturing. In this review, we focus on discrete manufacturing based on the understanding that hybrid modeling is more mature in process manufacturing. This paper aims to provide an overview of projects where hybrid modeling was used in manufacturing and introduce various ways of composing hybrid models. We provide examples highlighting the implementation of models, structure and expand on metrics to test and validate hybrid models, discuss challenges, and future research directions of hybrid modeling in manufacturing.

In recent years, the advancement of Industry 4.0 and smart manufacturing has made a large amount of industrial process data attainable with the use of sensors installed on machines. This paper proposes an experimental predictive maintenance framework for an industrial drying hopper so that it can detect any unusual event in the hopper, which reduces the risk of erroneous fault diagnosis in the manufacturing shop floor. The experimental framework uses Deep Learning (DL) algorithms to classify Multivariate Time-Series (MTS) data into two categories—failure or unusual events and regular events—thus formulating the problem as a binary classification. The raw data extracted from the sensors contained missing values, suffered from imbalancedness, and were not labeled. Therefore, necessary preprocessing is performed to make them usable for DL algorithms and the dataset is self-labeled after defining the two categories precisely. To tackle the imbalanced data issue, data balancing techniques like ensemble learning with undersampling and Synthetic Minority Oversampling Technique (SMOTE) are used. Moreover, along with DL algorithms like Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), Machine Learning (ML) algorithms like Support Vector Machine (SVM) and K-nearest neighbor (KNN) have also been used to perform a comparative analysis on the results obtained from these algorithms. The result shows that CNN is arguably the best algorithm for classifying this dataset into two categories and outperforms other traditional approaches as well as deep learning algorithms.

Integration of sensors into various kinds of products and machines provides access to in-depth usage information as basis for product optimization. Presently, this large potential for more user-friendly and efficient products is not being realized because (a) sensor integration and thus usage information is not available on a large scale and (b) product optimization requires considerable efforts in terms of manpower and adaptation of production equipment. However, with the advent of cloud-based services and highly flexible additive manufacturing techniques, these obstacles are currently crumbling away at rapid pace. The present study explores the state of the art in gathering and evaluating product usage and life cycle data, additive manufacturing and sensor integration, automated design and cloud-based services in manufacturing. By joining and extrapolating development trends in these areas, it delimits the foundations of a manufacturing concept that will allow continuous and economically viable product optimization on a general, user group or individual user level. This projection is checked against three different application scenarios, each of which stresses different aspects of the underlying holistic concept. The following discussion identifies critical issues and research needs by adopting the relevant stakeholder perspectives.

Introduction to Advanced Manufacturing was written by two experienced and passionate engineers whose mission is to make the subject of advanced manufacturing easy to understand and a practical solution to everyday problems. Harik, Ph.D. and Wuest, Ph.D., professors who have taught the subject for decades, combined their expertise to develop both an applied manual and a theoretical reference that addresses many different needs. Introduction to Advanced Manufacturing covers the following topics in detail: • Composites Manufacturing • Smart Manufacturing • Additive Manufacturing • Computer Aided Manufacturing • Polymers Manufacturing • Assembly Processes • Manufacturing Quality Control and Productivity • Subtractive Manufacturing • Deformative Manufacturing Introduction to Advanced Manufacturing offers a new, refreshing way of studying how things are made in the digital age. With academics and industry professionals in mind, Introduction to Advanced Manufacturing paves the ground for those interested in the new opportunities of Industry 4.0.

Against the sweeping trend of mass customization, the importance of product platform design is becoming increasingly recognized by the manufacturers. Module design is the foundation of product platform design, and module partition determines the effectiveness of module design. Traditionally, the vast majority of existing module partition methods ignored the design factor of customer preferences. This study proposes to employ the basic principles of robust design to make the module partition schemes less sensitive to the dynamically changing customer preferences by considering them as a noise factor. A criterion function and a noise function are each established based on the component-component correlation matrix and component-function contribution matrix, respectively. The criterion and noise functions, when combined, lead to a unique multi-objective optimization problem. Furthermore, an improved Pareto archive particle swarm optimization (PAPSO) algorithm is introduced to solve the multi-objective optimization problem in order to prevent the premature selections of non-optimal solutions. A case study is presented to showcase how the proposed new method is followed to conduct the module partition on an electric-traction drum shearer. The improved algorithm demonstrates highly competitive performance in comparison to the existing multi-objective optimization algorithms.

This paper presents a framework to utilize multivariate time series data to automatically identify reoccurring events, e.g., resembling failure patterns in real-world manufacturing data by combining selected data mining techniques. The use case revolves around the auxiliary polymer manufacturing process of drying and feeding plastic granulate to extrusion or injection molding machines. The overall framework presented in this paper includes a comparison of two different approaches towards the identification of unique patterns in the real-world industrial data set. The first approach uses a subsequent heuristic segmentation and clustering approach, the second branch features a collaborative method with a built-in time dependency structure at its core (TICC). Both alternatives have been facilitated by a standard principle component analysis PCA (feature fusion) and a hyperparameter optimization (TPE) approach. The performance of the corresponding approaches was evaluated through established and commonly accepted metrics in the field of (unsupervised) machine learning. The results suggest the existence of several common failure sources (patterns) for the machine. Insights such as these automatically detected events can be harnessed to develop an advanced monitoring method to predict upcoming failures, ultimately reducing unplanned machine downtime in the future.

Several applications exist for a whole brain positron-emission tomography (PET) brain imager designed as a portable unit that can be worn on a patient’s head. Enabled by improvements in detector technology, a lightweight, high performance device would allow PET brain imaging in different environments and during behavioral tasks. Such a wearable system that allows the subjects to move their heads and walk—the Ambulatory Microdose PET (AM-PET)—is currently under development. This imager will be helpful for testing subjects performing selected activities such as gestures, virtual reality activities and walking. The need for this type of lightweight mobile device has led to the construction of a proof of concept portable head-worn unit that uses twelve silicon photomultiplier (SiPM) PET module sensors built into a small ring which fits around the head. This paper is focused on the engineering design of mechanical support aspects of the AM-PET project, both of the current device as well as of the coming next-generation devices. The goal of this work is to optimize design of the scanner and its mechanics to improve comfort for the subject by reducing the effect of weight, and to enable diversification of its applications amongst different research activities.

Increasing market demand towards higher product and process quality and efficiency forces companies to think of new and innovative ways to optimize their production. In the area of high-tech manufacturing products, even slight variations of the product state during production can lead to costly and time-consuming rework or even scrapage. Describing an individual product’s state along the entire manufacturing programme, including all relevant information involved for utilization, e.g., in-process adjustments of process parameters, can be one way to meet the quality requirements and stay competitive. Ideally, the gathered information can be directly analyzed and in case of an identified critical trend or event, adequate action, such as an alarm, can be triggered. Traditional methods based on modelling of cause-effect relations reaches its limits due to the fast increasing complexity and high-dimensionality of modern manufacturing programmes. There is a need for new approaches that are able to cope with this complexity and high-dimensionality which, at the same time, are able to generate applicable results with reasonable effort. Within this paper, the possibility to generate such a system by applying a combination of Cluster Analysis and Supervised Machine Learning on product state data along the manufacturing programme will be presented. After elaborating on the different key aspects of the approach, the applicability on the identified problem in industrial environment will be discussed briefly.