Semantic communications between distributed cyber-physical systems towards collaborative automation for smart manufacturing

Machine-to-machine (M2M) communication is a crucial technology for collaborative manufacturing automation in the Industrial Internet of Things (IIoT)-empowered industrial networks. The new decentralized manufacturing automation paradigm features ubiquitous communication and interoperable interactions between machines. However, peer-to-peer (P2P) interoperable communications at the semantic level between industrial machines is a challenge. To address this challenge, we introduce a concept of Semantic-aware Cyber-Physical Systems (SCPSs) based on which manufacturing devices can establish semantic M2M communications. In this work, we propose a generic system architecture of SCPS and its enabling technologies. Our proposed system architecture adds a semantic layer and a communication layer to the conventional cyber-physical system (CPS) in order to maximize compatibility with the diverse CPS implementation architecture. With Semantic Web technologies as the backbone of the semantic layer, SCPSs can exchange semantic messages with maximum interoperability following the same understanding of the manufacturing context. A pilot implementation of the presented work is illustrated with a proof-of-concept case study between two semantic-aware cyber-physical machine tools. The semantic communication provided by the SCPS architecture makes ubiquitous M2M communication in a network of manufacturing devices environment possible, laying the foundation for collaborative manufacturing automation for achieving smart manufacturing. Another case study focusing on decentralized production control between machines in a workshop also proved the merits of semantic-aware M2M communication technologies.     

The HORSE framework: A reference architecture for cyber-physical systems in hybrid smart manufacturing

In the Industry 4.0 era, manufacturers strive to remain competitive by using advanced technologies such as collaborative robots, automated guided vehicles, augmented reality support and smart devices. However, only if these technological advancements are integrated into their system context in a seamless way, they can deliver their full potential to a manufacturing organization. This integration requires a system architecture as a blueprint for positioning and interconnection of the technologies. For this purpose, the HORSE framework, resulting from the HORSE EU H2020 project, has been developed to act as a reference architecture of a cyber-physical system to integrate various Industry 4.0 technologies and support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. The architecture has been created using design science research, based on well-known software engineering frameworks, established manufacturing domain standards and practical industry requirements. The value of a reference architecture is mainly established by application in practice. For this purpose, this paper presents the application and evaluation of the HORSE framework in 10 manufacturing plants across Europe, each with its own characteristics. Through the physical deployment and demonstration, the framework proved its goal to be basis for the well-structured design of an operational smart manufacturing cyber-physical system that provides horizontal, cross-functional management of manufacturing processes and vertical control of heterogeneous technologies in work cells. We report on valuable insights on the difficulties to realize such systems in specific situations. The experiences form the basis for improved adoption, further improvement and extension of the framework. In sum, this paper shows how a reference architecture framework supports the structured application of Industry 4.0 technologies in manufacturing environments that so far have relied on more traditional digital technology.     

Key human factors and their effects on human centered assembly performance

Today in the increasingly competitive market, consumers prefer to have a great variety of products to choose from; this preference is often coupled with demands for a relatively smaller lot size, shorter lead time, higher quality and lower cost. Consequently, manufacturing companies are being forced to consistently increase flexibility and responsiveness of their production systems in order to accommodate changes of the fluctuating market. Among various forms of production systems, human-centred manufacturing systems can offer such a capability in dealing with product variations and production volumes as human workers can always adapt themselves to perform multiple tasks after a learning process. However, human performance can also be unpredictable and it may alter due to varying psychological and physiological states, which are often overlooked by researchers when designing, implementing or evaluating a manufacturing system. This paper presents a study aiming to address these issues by exploring human factors and their interactions that may affect human performance on human-centred assembly systems. The study was carried out based on a literature review and an industrial survey. Critical system performance indicators, which are affected by human factors, were evaluated and the most significant human factors were identified using the fuzzy extent analysis method. The research findings show that experience is the most significant human factor that affects individual human performance, compared to age and general cognitive abilities in human-centred assembly. By contrast, both human reaction time and job satisfaction have the least effect on human performance. The significance of ageing on human performance was also studied and it was concluded that average assembly time of human workers rises by average 1% per year after the age of 38 years old.     

Edge-cloud orchestration driven industrial smart product-service systems solution design based on CPS and IIoT

The rapid booming of advanced information and communication technologies (ICT) has promoted an encouraging smart, connected product (SCP) market that further triggers the development of manufacturing towards the servitization proposition, viz. smart product-service systems (PSS). Smart PSS aims to provide a solution (product-service) with high satisfaction and less environmental influence by leveraging SCP as the media tool. Its solution design should not just focus on the physical world nor only be enabled by the cloud side, while the cyber world and the edge side must be included in the Industry 4.0. However, only few current researches investigate about the smart PSS, let alone an overall cyber-physical and edge-cloud discussion to support its solution design. In order to fill this gap, this work proposes an edge-cloud orchestration driven solution design based on the cyber-physical systems (CPS) and industrial Internet of Things (IIoT). To make our ideas concrete, a real-life milling process was conducted as an illustrative example. It is hoped that this study can furnish industrial enterprises with meaningful sights in the process of servitization and value co-creation.     

Data-driven cyber-physical system framework for connected resistance spot welding weldability certification

A cyber-physical system is one of the integral parts of the development endeavor of the smart manufacturing domain and the Industry 4.0 wave. With the advances in data analytics, smart manufacturing is gradually transforming the global manufacturing landscape. In the Resistance Spot Welding (RSW) domain, the focus has been more on the physical systems, compared to the virtual systems. The cyber-physical system facilitates the integrated analysis of the design and manufacturing processes by converging the physical and virtual stages to improve product quality in real-time. However, a cyber-physical system integrated RSW weldability certification is still an unmet need. This research is to realize a real-time data-driven cyber-physical system framework with integrated analytics and parameter optimization capabilities for connected RSW weldability certification. The framework is based on the conceptualization of the layers of the cyber-physical system and can incorporate the design and machine changes. It integrates data from the analytics lifecycle phases, starting from the data collection operation, to the predictive analytics operation, and to the visualization of the design. This integrated framework aims to support decision-makers to understand product design and its manufacturing implications. In addition to data analytics, the proposed framework implements a closed-loop machine parameter optimization considering the target product design. The framework visualizes the target product assembly with predicted response parameters along with displaying the process parameters and material design parameters simultaneously. This layer should help the designers in their decision-making process and the engineers to gain knowledge about the manufacturing processes. A case study on the basis of a real industrial case and data is presented in detail to illustrate the application of the envisioned cyber-physical systems framework.     

Credit rating by hybrid machine learning techniques

It is very important for financial institutions to develop credit rating systems to help them to decide whether to grant credit to consumers before issuing loans. In literature, statistical and machine learning techniques for credit rating have been extensively studied. Recent studies focusing on hybrid models by combining different machine learning techniques have shown promising results. However, there are various types of combination methods to develop hybrid models. It is unknown that which hybrid machine learning model can perform the best in credit rating. In this paper, four different types of hybrid models are compared by ‘Classification + Classification’, ‘Classification + Clustering’, ‘Clustering + Classification’, and ‘Clustering + Clustering’ techniques, respectively. A real world dataset from a bank in Taiwan is considered for the experiment. The experimental results show that the ‘Classification + Classification’ hybrid model based on the combination of logistic regression and neural networks can provide the highest prediction accuracy and maximize the profit.     

Analyzing the risks of digital servitization in the machine tool industry

The machine tool industry plays a major role in the execution of high-quality and efficient complex manufacturing processes. The adoption of digital technologies can transform production systems into more connected, adaptive, efficient, and potentially sustainable systems. A key enabler of this transformation is servitization, a business model that builds on digitalization and data capture to deliver value through services. Digital services for machine tools typically use data obtained through highly connected manufacturing environments, providing visibility of complex lifecycles, and enabling better decision-making. However, an understanding of digital servitization to support the machine tools industry is still emerging and for most industrial actors the potential risks are unclear. The findings of this study describe potential applications of digital servitization in the machine tool industry, synthesize the identified risks from practitioners’ perspectives, and provide mitigation and contingency activities. This study contributes to bridging the gap between theory and practice by clarifying companies’ needed considerations before implementing digital services in the machine tool industry.     

A flexible data schema and system architecture for the virtualization of manufacturing machines (VMM)

Future factories will feature strong integration of physical machines and cyber-enabled software, working seamlessly to improve manufacturing production efficiency. In these digitally enabled and network connected factories, each physical machine on the shop floor can have its ‘virtual twin’ available in cyberspace. This ‘virtual twin’ is populated with data streaming in from the physical machines to represent a near real-time as-is state of the machine in cyberspace. This results in the virtualization of a machine resource to external factory manufacturing systems. This paper describes how streaming data can be stored in a scalable and flexible document schema based database such as MongoDB, a data store that makes up the virtual twin system. We present an architecture, which allows third-party integration of software apps to interface with the virtual manufacturing machines. We evaluate our database schema against query statements and provide examples of how third-party apps can interface with manufacturing machines using the VMM middleware. Finally, we discuss an operating system architecture for VMMs across the manufacturing cyberspace, which necessitates command and control of various virtualized manufacturing machines, opening new possibilities in cyber-physical systems in manufacturing.     

Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.     

Stable indirect adaptive interval type-2 fuzzy sliding-based control and synchronization of two different chaotic systems

An indirect approach to adaptive interval type-2 fuzzy sliding mode control is proposed for the stable synchronization of two different chaotic nonlinear systems with different initial conditions under the presence of uncertainties involving process noises and external disturbances. The indirect model-based approach to adaptation is promoted here as a more suitable strategy for the fast changes that occurs in chaotic systems. In other words, the usual direct adaptive strategies may be too slow to respond to the inherently fast changing dynamics of chaotic systems. Using Lyapunov analysis, the sliding mode approach illustrates the asymptotic convergence of synchronization error to zero as well as good robustness against external disturbances. The interval type-2 structure aims to remedy the undesirable chattering phenomenon that is common in most conventional sliding mode control applications. It also provides a more effective equivalent model in the indirect approach, which leads to improved handling of the chaotic variations and uncertainties. Two numerical pairs of chaotic systems, i.e. the Lorenz and Chen’s systems and the Rössler system and modified Chua’s circuit are considered. In particular, in comparison with its type-1 fuzzy counterpart, the control effort is reduced by an average of 26.25% and 17.4% for the synchronization of the two corresponding systems, respectively. Furthermore, the integral of squared error is also improved by an average of 27.2% and 25.33%. This is while convergence time is reduced to less than 0.5 s and 1.5 s.     

Making costly manufacturing smart with transfer learning under limited data: A case study on composites autoclave processing

The integration of advanced manufacturing processes with ground-breaking Artificial Intelligence methods continue to provide unprecedented opportunities towards modern cyber-physical manufacturing processes, known as smart manufacturing or Industry 4.0. However, the “smartness” level of such approaches closely depends on the degree to which the implemented predictive models can handle uncertainties and production data shifts in the factory over time. In the case of change in a manufacturing process configuration with no sufficient new data, conventional Machine Learning (ML) models often tend to perform poorly. In this article, a transfer learning (TL) framework is proposed to tackle the aforementioned issue in modeling smart manufacturing. Namely, the proposed TL framework is able to adapt to probable shifts in the production process design and deliver accurate predictions without the need to re-train the model. Armed with sequential unfreezing and early stopping methods, the model demonstrated the ability to avoid catastrophic forgetting in the presence of severely limited data. Through the exemplified industry-focused case study on autoclave composite processing, the model yielded a drastic (88%) improvement in the generalization accuracy compared to the conventional learning, while reducing the computational and temporal cost by 56%.     

Multi-agent reinforcement learning for online scheduling in smart factories

Rapid advances in sensing and communication technologies connect isolated manufacturing units, which generates large amounts of data. The new trend of mass customization brings a higher level of disturbances and uncertainties to production planning. Traditional manufacturing systems analyze data and schedule orders in a centralized architecture, which is inefficient and unreliable for the overdependence on central controllers and limited communication channels. Internet of things (IoT) and cloud technologies make it possible to build a distributed manufacturing architecture such as the multi-agent system (MAS). Recently, artificial intelligence (AI) methods are used to solve scheduling problems in the manufacturing setting. However, it is difficult for scheduling algorithms to process high-dimensional data in a distributed system with heterogeneous manufacturing units. Therefore, this paper presents new cyber-physical integration in smart factories for online scheduling of low-volume-high-mix orders. First, manufacturing units are interconnected with each other through the cyber-physical system (CPS) by IoT technologies. Attributes of machining operations are stored and transmitted by radio frequency identification (RFID) tags. Second, we propose an AI scheduler with novel neural networks for each unit (e.g., warehouse, machine) to schedule dynamic operations with real-time sensor data. Each AI scheduler can collaborate with other schedulers by learning from their scheduling experiences. Third, we design new reward functions to improve the decision-making abilities of multiple AI schedulers based on reinforcement learning (RL). The proposed methodology is evaluated and validated in a smart factory by real-world case studies. Experimental results show that the new architecture for smart factories not only improves the learning and scheduling efficiency of multiple AI schedulers but also effectively deals with unexpected events such as rush orders and machine failures.     

Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook

The recent advances in sensor and communication technologies can provide the foundations for linking the physical manufacturing facility and machine world to the cyber world of Internet applications. The coupled manufacturing cyber-physical system is envisioned to handle the actual operations in the physical world while simultaneously monitor them in the cyber world with the help of advanced data processing and simulation models at both the manufacturing process and system operational levels. Moreover, a sensor-packed manufacturing system in which each process or piece of equipment makes available event and status information, coupled with market research for true advanced Big Data analytics, seem to be the right ingredients for event response selection and operation virtualization. As a drawback, the resulting manufacturing cyber-physical system will be vulnerable to the inevitable cyber-attacks, unfortunately, so common for the software and Internet-based systems. This reality makes cybersecurity penetration within the manufacturing domain a need that goes uncontested across researchers and practitioners. This work provides a review of the current status of virtualization and cloud-based services for manufacturing systems and of the use of Big Data analytics for planning and control of manufacturing operations. Building on already developed cloud business solutions, cloud manufacturing is expected to offer improved enterprise manufacturing and business decision support. Based on the current state-of-the-art cloud manufacturing solutions and Big Data applications, this work also proposes a framework for the development of predictive manufacturing cyber-physical systems that include capabilities for attaching to the Internet of Things, and capabilities for complex event processing and Big Data algorithmic analytics.     

The role of wearable devices in meeting the needs of cloud manufacturing: A case study

Cloud manufacturing is a service-oriented, customer-centric and demand-driven process with well-established industrial automation. Even though, it does not necessarily mean the absence of human beings. Due to products and their corresponding manufacturing processes becoming increasingly complex, operators' daily working lives are also becoming more difficult. Enhanced human–machine interaction is one of the core areas for the success of the next generation of manufacturing. However, the current research only focuses on the automation and flexibility features of cloud manufacturing, the interaction between human and machine and the value co-creation among operators is missing. Therefore, a new method is needed for operators to support their work, with the objective of reducing the time and cost of machine control and maintenance. This paper describes a practical demonstration that uses the technologies of the Internet of things (IoT), wearable technologies, augmented reality, and cloud storage to support operators' activities and communication in discrete factories. This case study exhibits the capabilities and user experience of smart glasses in a cloud manufacturing environment, and shows that smart glasses help users stay productive and engaged.     

Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review

Human–robot collaboration is a main technology of Industry 4.0 and is currently changing the shop floor of manufacturing companies. Collaborative robots are innovative industrial technologies introduced to help operators to perform manual activities in so called cyber-physical production systems and combine human inimitable abilities with smart machines strengths. Occupational health and safety criteria are of crucial importance in the implementation of collaborative robotics. Therefore, it is necessary to assess the state of the art for the design of safe and ergonomic collaborative robotic workcells. Emerging research fields beyond the state of the art are also of special interest. To achieve this goal this paper uses a systematic literature review methodology to review recent technical scientific bibliography and to identify current and future research fields. Main research themes addressed in the recent scientific literature regarding safety and ergonomics (or human factors) for industrial collaborative robotics were identified and categorized. The emerging research challenges and research fields were identified and analyzed based on the development of publications over time (annual growth).     

Advanced mobile and wearable systems

The recent spectacular progress in the microelectronic, information, communication, material and sensor technologies created a big stimulus towards development of smart communicating cyber-physical systems (CPS) and Internet of Things (IoT). CPS and IoT are undergoing an explosive growth to a large degree related to advanced mobile systems like smart automotive and avionic systems, mobile robots and wearable devices. The huge and rapidly developing markets of sophisticated mobile cyber-physical systems represent great opportunities, but these opportunities come with a price of unusual system complexity, as well as, stringent and difficult to satisfy requirements of many modern applications. Specifically, smart cars and various wearable systems to a growing degree involve big instant data from multiple complex sensors or other systems, and are required to provide continuous autonomous service in a long time. In consequence, they demand a guaranteed (ultra-)high performance and/or (ultra-)low energy consumption, while requiring a high reliability, safety and security. To adequately address these demands, sophisticated embedded computing and embedded design technologies are needed. After an introduction to modern mobile systems, this paper discusses the huge heterogeneous area of these systems, and considers serious issues and challenges in their design. Subsequently, it discusses the embedded computing and design technologies needed to adequately address the issues and overcome the challenges in order to satisfy the stringent requirements of the modern mobile systems.     

On a containerized approach for the dynamic planning and control of a cyber - physical production system

The increased complexity of modern production systems requires sophisticated system control approaches to maintain high levels of flexibility. Furthermore, the request for customized production with the introduction of heterogeneous production resources, increases the diversity of manufacturing systems making their reconfiguration complex and time consuming.In this paper, an end-to-end approach for reconfigurable cyber-physical production systems is discussed, enabled by container technologies. The presented approach enhances flexibility in a cyber-physical production system (CPPS) through the dynamic reconfiguration of the automation system and the production schedule, based on occurring events.High-level management of manufacturing operations is performed on a centralized node while the data processing and execution control is handled at the network edge. Runtime events are generated at the edge and in smart connected devices via means of a variant of IEC61499 function blocks. Software containers manage the deployment and low-level orchestration of FBs at the edge devices. All aspects of the proposed solution have been implemented on a software framework and applied in a small scale CPPS coming from the automotive industry.     

FASA: A software architecture and runtime framework for flexible distributed automation systems

Modern automation systems have to cope with large amounts of sensor data to be processed, stricter security requirements, heterogeneous hardware, and an increasing need for flexibility. The challenges for tomorrow’s automation systems need software architectures of today’s real-time controllers to evolve.This article presents FASA, a modern software architecture for next-generation automation systems. FASA provides concepts for scalable, flexible, and platform-independent real-time execution frameworks, which also provide advanced features such as software-based fault tolerance and high degrees of isolation and security. We show that FASA caters for robust execution of time-critical applications even in parallel execution environments such as multi-core processors.We present a reference implementation of FASA that controls a magnetic levitation device. This device is sensitive to any disturbance in its real-time control and thus, provides a suitable validation scenario. Our results show that FASA can sustain its advanced features even in high-speed control scenarios at 1 kHz.     

Motion estimation and CABAC VLSI co-processors for real-time high-quality H.264/AVC video coding

Real-time and high-quality video coding is gaining a wide interest in the research and industrial community for different applications. H.264/AVC, a recent standard for high performance video coding, can be successfully exploited in several scenarios including digital video broadcasting, high-definition TV and DVD-based systems, which require to sustain up to tens of Mbits/s. To that purpose this paper proposes optimized architectures for H.264/AVC most critical tasks, Motion estimation and context adaptive binary arithmetic coding. Post synthesis results on sub-micron CMOS standard-cells technologies show that the proposed architectures can actually process in real-time 720 × 480 video sequences at 30 frames/s and grant more than 50 Mbits/s. The achieved circuit complexity and power consumption budgets are suitable for their integration in complex VLSI multimedia systems based either on AHB bus centric on-chip communication system or on novel Network-on-Chip (NoC) infrastructures for MPSoC (Multi-Processor System on Chip).     

Assessing creative problem-solving with automated text grading

The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational–statistical machine learning methods to grade students’ natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit students’ constructed responses are beneficial. But the high cost required in manually grading constructed responses could become an obstacle in applying open-ended questions. In this study, automated grading schemes have been developed and evaluated in the context of secondary Earth science education. Empirical evaluations revealed that the automated grading schemes may reliably identify domain concepts embedded in students’ natural language responses with satisfactory inter-coder agreement against human coding in two sub-tasks of the test (Cohen’s Kappa = .65–.72). And when a single holistic score was computed for each student, machine-generated scores achieved high inter-rater reliability against human grading (Pearson’s r = .92). The reliable performance in automatic concept identification and numeric grading demonstrates the potential of using automated grading to support the use of open-ended questions in science assessments and enable new technologies for science learning.