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Digital retrofit: A first step toward the adoption of Industry 4.0 to the manufacturing systems of small and medium-sized enterprises
Abstract
In recent years, Industry 4.0 has gained relevance in the manufacturing sector. On one hand, it is expected that this new paradigm will affect the entire value chain and increase the capabilities of the manufacturing system as a whole, in terms of interoperability and communication throughout factories and beyond. On the other hand, considering that small and medium-sized enterprises represent one of the main forces in economic development and employment generation, focus is shifting toward said manufacturing paradigm in order to ensure competitiveness in the market in the nearby future. However, economic factors could stand in the way of this migration. Thus, digital retrofit is seen as a possibility for the integration of Industry 4.0, paving the way for unappealing technologies to large investment opportunities. In this article, a thorough literary review is performed regarding the formal implementation of Industry 4.0 applications. The result is the Asset Administration Shell model. Afterward, a methodology is proposed for the design and implementation of the Asset Administration Shell, leading to a digital retrofit approach for manufacturing resources. Finally, the methodology is applied in a turning station, thereby validating an increase in the communication and interoperability of the station, which can be used to add overall value to the manufacturing system.
... The integration of new devices and technology into the traditional processes in the digitization journey can offer great opportunities for companies to re-design business and expand service activities, which facilitate data-driven business strategy making [14]. The need for retrofitting solutions emerges in Small and Medium-sized Enterprises (SMEs) [15], which is the most vulnerable object of being left behind in the I4.0 development [16]. The IoT upgrade for better utilization of existing infrastructure with legacy equipment and legacy software is named brownfield development [17], also known as retrofitting [18]. ...
... The establishment of vertical and horizontal integration of the entire production is needed [65], to allow the autonomous operation of the equipment without significant modification [66]. This goal should be achieved by a long-term strategy as the company moves forward in its digitization journey [16]. The following subsection explored the brownfield development across industries, with the technologies utilized in the activities mentioned above. ...
... In general, the connectivity enhancement for a legacy system is implemented according to an architecture that the authors usually suggested in their projects [16], [57], [112], [115], [123]. These architectures are the prerequisite output that needs to be designed in the very beginning stage of the retrofitting project. ...
The ongoing Industry 4.0 is characterized by the connectivity between components in the manufacturing system. For modern machines, the Internet of Things is a built-in function. In contrast, there are legacy machines in deployment functioning without digital communication. The need to connect them became popular to improve overall production efficiency. As building a new smart factory as a greenfield investment is a capital-intensive choice, retrofitting the existing infrastructure with IoT capability is more reasonable than replacing them. However, this so-called brownfield development, or retrofitting, requires specific prerequisites, e.g., digitization status assessment, technical and connectivity development, management requirement, and operational need, representing a significant disadvantage: lack of scalability. In the meantime, Industry 5.0 is under human-centric priority, which poses new challenges to the retrofitted system. Aware of the challenge, this paper provides a systematic overview of brownfield development regarding technical difficulties, supporting technologies, and possible applications for the legacy system. The research scope focuses on available Industry 4.0 advancements but considers preparing for the forthcoming Industry 5.0. The proposed retrofitting project approach can be a guideline for manufacturers to transform their factories into intelligent spaces with minimal cost and effort but still gain the most applicable solution for management needs. The future direction for other research in brownfield development for Industry 5.0 is also discussed.
... While replacing machinery can yield positive short-term effects on the production plant's digitization level, the replacement of functional machines also requires high investments and contradicts the idea of sustainable production (Khan et al., 2018;Li et al., 2021). In contrast, smart retrofittingwhich has been outlined as a shift from traditional manufacturing to smart manufacturing with the least financial effort, limited risks as well as minimal time expenditure (García et al., 2020;Guerreiro et al., 2018) can be seen as a more sustainable and favourable way of transforming the current state of legacy equipment. ...
... In addition to technical challenges, economic factors can also stand in the way of migrating towards Industry 4.0 (García et al., 2020). In this regard, digital retrofitting is described as an economically viable strategy, especially for SMEs. ...
... The authors point out that the challenges are bigger for that group of companies since they can only draw on a limited resource base (Bunterngchit et al., 2019;Contreras Pérez et al., 2018;Nsiah et al., 2018). This finding supports García et al. (2020), Mittal et al. (2018b) and Mittal et al. (2018a) contributions which indicate inferior starting conditions of SMEs compared to large companies when it comes to smart manufacturing endeavors. A detailed discussion of limited resources, especially at the level of skills and human resources of an organization, or regarding the advantages of a longer use of the installed base is not yet to be found. ...
The transition towards smart manufacturing is challenging for companies with a considerable installed base of legacy machines and equipment. In this regard, smart retrofitting has been introduced as a sustainable approach of transforming the current state of legacy equipment into smart and connected assets. By equipping the existing installed base with hardware and software as well as networking capability, smart retrofitting allows for new data-driven processes and business models. While we see a growing number of articles that focus on retrofitting in the context of smart manufacturing, there is still a lack of a uniform definition of smart retrofitting as well as a holistic understanding of its drivers, challenges, and benefits. Therefore, we conducted a systematic review of the retrofitting literature available through the online databases Scopus and Web of Science. We identified 23 particularly relevant academic articles for a detailed full-text analysis. In this full-text analysis, we searched for definitions of smart retrofitting and synthesized them into a novel definition. Furthermore, we inductively derived contextual drivers and challenges of smart retrofitting as well as retrofit benefits. This literature review provides an overview of the current body of knowledge on smart retrofitting. For academics, our findings show that current research on smart retrofitting focuses on new technologies and legacy systems but largely disregards transformational aspects regarding business models and servitization as well as sustainability aspects, although these deserve more attention in times of global climate challenges. For practitioners, we provide an overview of the drivers, challenges, and benefits that can guide their smart retrofitting initiatives.
... The academic community disputes that the widespread adoption of technologies associated with Industry 4.0 is contingent on there being available low-cost and powerful IoT [6,7,20]. These devices are anticipated to assist mitigate the financial risk associated with testing out new digital technologies that could implement in digital retrofitting the SMEs in order to cultivate the digital twin paradigms by deploying intelligent activities, accomplishing the advanced level of the smart cyber-physical system, and optimising the efficiency of human-machine interactions in which machines assist and enhance workers rather than eliminate them; this will revitalise industries and pave the way for the industries' resilience and flourishing with Industry I5.0, which is dedicated to tailoring human-centric industry and sustainability and making Industry I 5.0 a reality [11][12][13][14][15]. ...
... In Industry 4.0, the advancement of pillar technologies such as big data analytics, digital twins, the Internet of Things (IoT), and cloud computing, in conjunction with Artificial Intelligence (AI), are used to facilitate the realisation of Smart Cyber-Physical Systems (CPS), which serve as a real-time interface between the virtual and physical worlds and provide on-demand services with high reliability, scalability, and availability in a distributed environment [1,35]. Industry 4.0 aspires to reinvent product manufacturing in order to create smart factories of the future that are more creative, cost-effective, and responsive to customer needs [6,7,35]. As a result, providing distinctive features at scale is one of the most desired competencies in Industry 4.0. ...
Industry 4.0 is evolving through technological advancements, leveraging information technology to enhance industry with digitalisation and intelligent activities. Whereas Industry 5.0 is the Age of Augmentation, striving to concentrate on human-centricity, sustainability, and resilience of the intelligent factories and synergetic industry. The crucial enhancer for the improvements accomplished by digital transformation is the notion of ‘digital triplet D3’, which is an augmentation of the digital twin with artificial intelligence, human ingenuity, and experience. digital triplet D3 encompasses intelligent activities based on human awareness and the convergence among cyberspace, physical space, and humans, in which Implementing useful reference hierarchy is a crucial part of instigating Industry 5.0 into a reality. This paper depicts a digital triplet which discloses the potency of retrofitting a conventional drilling machine. This hierarchy included the perceptive level for complex decision-making by deploying machine learning based on human ingenuity and creativity, the concatenated level for controlling the physical system’s behaviour predictions and emulation, the observing level is the iterative observation of the actual behaviour of the physical system using real-time data, and the duplicating level visualises and emulates virtual features through physical tasks. The accomplishment demonstrated the viability of the hierarchy in imitating the real-time functionality of the physical system in cyberspace, an immaculate performance of this paradigm. The digital triplet’s complexity was diminished through the interaction among facile digital twins, intelligent activities, and human awareness. The performance parameters of the digital triplet D3 paradigm for retrofitting were eventually confirmed through appraising, anomaly analysis, and real-time monitoring.
... Retrofitting can be defined as the process of introducing changes to traditional machinery to make it more efficient, while simultaneously minimising financial and time costs and risks [12,13]. Industrial infrastructure can be automated and can have its lifespan extended by retrofitting machinery with hardware, software, and networking capabilities to gather data for processing and analysis [5,14]. ...
... Moreover, reference architectures do not concentrate on the retrofitting of systems and their specific requirements. García et al. [12] implement an Asset Administration Shell [102] to retrofit manufacturing resources within the RAMI architecture. AAS is a standard used to describe assets electronically [103]. ...
Industry 4.0 technologies and digitalised processes are essential for implementing smart manufacturing within vertically and horizontally integrated production environments. These technologies offer new ways to generate revenue from data-driven services and enable predictive maintenance based on real-time data analytics. They also provide autonomous manufacturing scheduling and resource allocation facilitated by cloud computing technologies and the industrial Internet of Things (IoT). Although the fourth industrial revolution has been underway for more than a decade, the manufacturing sector is still grappling with the process of upgrading manufacturing systems and processes to Industry 4.0-conforming technologies and standards. Small and medium enterprises (SMEs) in particular, cannot always afford to replace their legacy systems with state-of-the-art machines but must look for financially viable alternatives. One such alternative is retrofitting, whereby old manufacturing systems are upgraded with sensors and IoT components to integrate them into a digital workflows across an enterprise. Unfortunately, to date, the scope and systematic process of legacy system retrofitting, and integration are not well understood and currently represent a large gap in the literature. In this article, the authors present an in-depth systematic review of case studies and available literature on legacy system retrofitting. A total of 32 papers met the selection criteria and were particularly relevant to the topic. Three digital retrofitting approaches are identified and compared. The results include insights common technologies used in retrofitting, hardware and software components typically required, and suitable communication protocols for establishing interoperability across the enterprise. These form an initial basis for a theoretical decision-making framework and associated retrofitting guide tool to be developed.
... Gualtieri et al. [32] propose a qualitative and economic evaluation model for the transformation from the manual working area to the human-robot collaborative area. Garcia et al. [33] present a case study that aims to increase the level of communication and interoperability in a flexible manufacturing system. Some retrofitting works use a Reference Architectural Model Industrie 4.0 (RAMI 4.0) based approach [24,33,34]. ...
... Garcia et al. [33] present a case study that aims to increase the level of communication and interoperability in a flexible manufacturing system. Some retrofitting works use a Reference Architectural Model Industrie 4.0 (RAMI 4.0) based approach [24,33,34]. RAMI 4.0 is a kind of framework for the implementation and development of industry 4.0 applications. ...
The transformation from traditional industry to Industry 4.0 can bring many benefits in various spheres, from efficiency to safety. However, this transition involves adopting technologically advanced machinery with a high level of digitization and communication. The costs and time to replace obsolete machines could be unsustainable for many companies while retrofitting the old machinery. To make them ready to the Industry 4.0 context, they may represent an alternative to the replacement. Even if there are many studies related to retrofitting applied to machinery, there are very few studies related to the literature process industry sector. In this work, we propose a case study of a two-phase mixing plant that needed to be enhanced in the safety and maintainability conditions with reasonable times and costs. In this regard, the Digital Twin techniques and Deep Learning algorithms will be tested to predict and detect future faults, not only already visible and existing malfunctions. This approach strength is that, with limited investments and reasonable times, it allows the transformation of an old plant into a smart plant capable of communicating quickly with operators to increase its safety and maintainability.
... The robots with teleoperation are extremely useful in remote monitoring and control applications. These systems are used in surveillance 5 , telemedicine 6 , and other applications 7 , 8 . Telerobotic concepts are applied in the teleoperation of soft robotics (soft material robots) to expand the range of applications 9 . ...
... The molds are heat-treated to cure the material inside the oven. [8]. The two parts (top and bottom layers of the actuator) are combined using the same approach. ...
Soft actuators are used in bilateral systems as slave-side actuators. To broaden the applications and usage of these soft actuators in teleoperated applications required high accuracy and control. This article focuses on the improvement and control of the soft actuators operated through the Internet using a glove-based gesture control system. A hand-worn sensor glove (a glove with flex sensors and accelerometers) is used to detect hand gestures (specifically bending angle and force generated in a finger while gripping a spherical object) of an operator. The operator’s finger movements are mimicked in a remote location, through the Internet, in a soft actuator. The bending angles of the human finger have converted into the pressure variations inside the actuator using a linear calibration technique. A vertically mounted actuator and a spherical object are used to demonstrate the gripping action. The main problems that occurred during the controlling of this setup are noise and delays. Electronic noise (line noise and circuit noise), mechanical noise (vibration, nonuniformities in actuator fabrication, wear, and tear), elastic effect (energy absorbed during the state transformation), communication delays (delays occurred due to geography, telecommunication infrastructure, and round trip transmission), and other noises (other environmental effects) degraded the performance. This article considered a Brownian motion model, an additive Gaussian noise model, and a Kalman filter to solve these problems. The experimentations are performed in three different locations (to demonstrate the teleoperation) and the recorded improvement in the performance is approximately 17%.
... First, the aim of smart retrofitting is to apply Industry 4.0 technology to manufacturing equipment and processes with minimal cost and time. Modifications to conventional manufacturing equipment, such as machine tools, industrial robots, and process plants, have been reported continuously, and studies pertaining to them have confirmed the usefulness of OSS for development (Di Carlo et al., 2021;García et al., 2020;Sezer et al., 2018). Second, an appropriate smart factory has been proposed for applying smart manufacturing technologies with user-friendly and affordable functions to SMEs. ...
Manufacturing innovation promoted by the Fourth Industrial Revolution presents various technologies and policies for implementing future smart factories based on advances in information and communication technology (ICT). However, despite recent advances in smart manufacturing technologies, several difficulties in migrating conventional manufacturing to smart factories remain, particularly in small and medium-sized enterprises (SMEs). Among the recently emerging technologies, ICT-related technologies have been developed and utilized as open-source software (OSS) to accelerate their development through collective intelligence and community growth. In this study, to facilitate the identification of appropriate smart manufacturing solutions for personnel in SMEs with insufficient prior experience and knowledge using OSS, several reference architectures (RAs) are investigated to define a small RA that can be referenced to configure the mandatory functions during development as technical requirements. Subsequently, user-oriented requirements are summarized to determine the enabling OSS by considering the conditions of SMEs for the functional components in developing appropriate smart manufacturing technologies to form Internet of Things edge computing, and a recommendation for enabling OSS that guides the development of SMEs is proposed. In the evaluation, a small edge and gateway demonstration is performed using two single-board computers equipped with Raspberry Pi, where some of the recommended enabling OSS of Message Queuing Telemetry Transport via Wi-Fi, MySQL, Node-RED, and Python are used. Finally, the feasibility of the proposed approach is confirmed by evaluating the demonstration.
... It can extend the life cycle of machinery and equipment in a way that is feasible, time-saving, and requires comparatively low investments". Thus, through smart retrofitting, one can pursue the goals of Industry 4.0 while not being equipped with state-of-the-art machinery, keeping adaptation costs low [19] while extending the life cycle of already acquired machinery [20]. Both aspects benefit, especially, small and medium-sized companies. ...
Industry nowadays must deal with the so called “fourth industrial revolution”, i.e. Industry 4.0. This revolution is based on the introduction of new paradigms in the manufacturing industry such as flexibility, efficiency, safety, digitization, big data analysis and interconnection. However, human factors’ integration is usually not considered, although included as one of the paradigms. Some of these human factors’ most overlooked aspects are the customization of the worker’s user experience and on-board safety. Moreover, the issue of integrating state of the art technologies on legacy machines is also of utmost importance, as it can make a considerable difference on the economic and environmental aspects of their management, by extending the machine’s life cycle. In response to this issue, the Retrofitting paradigm, the addition of new technologies to legacy machines, has been considered. In this paper we propose a novel modular system architecture for secure authentication and worker’s log-in/log-out traceability based on face recognition and on state-of-the-art Deep Learning and Computer Vision techniques, as Convolutional Neural Networks. Starting from the proposed architecture, we developed and tested a device designed to retrofit legacy machines with such capabilities, keeping particular attention to the interface usability in the design phase, little considered in retrofitting applications along with other Human Factors, despite being one of the pillars of Industry 4.0. This research work’s results showed a dramatic improvement regarding machines on-board access safety.
... From an economic aspect, it avoids the incurring of large investments associated with the redesign or purchase of new equipment. In addition, the retrofit technique can assess the complete (or partial) migration to new technologies without compromising the integrity of the traditional methods employed by the company (García et al. 2020). Therefore, the need for retrofitting is also considered in the proposed framework as part of IIoT. ...
In this chapter, we explore after 10 years of Industry 4.0 the status of the application in manufacturing companies and especially in small- and medium-sized enterprises (SMEs). Based on literature and previously conducted research we present guidelines and a modular framework for implementing smart manufacturing in SMEs. In addition, a stage model is illustrated to support SMEs in breaking down the framework from a design level to an implementation and operational level. Finally, an outlook is given on the future challenges that SMEs will face in the coming years when they want to reach the next level of Industry 4.0 in their own company.
... Recently, a four-layer architecture is developed in [19] to provide more flexibility and interoperability of the system. Recently, Garcia et al. [20] proposed a methodology for the adoption of the Industry 4.0 paradigm in SMEs through digital retrofitting by systematically designing and implementing the proper IT infrastructure, using technological tools for easy access. However, the proposed methodology requires monotonous and repetitive tasks from the developer such as the one-way binding of the databases with their respective variables in the OPC UA information model [21]. ...
In the context of Industry 4.0, Cyber-Physical Production Systems (CPPS) and digital twins are key technologies for the management of huge amount of data generated by Industrial Internet of things (IIoT) devices. However, the interoperability and flexibility of different components is still an important challenge so as to integrate them in the process and fit all industrial specific needs. Thus, the main contribution of this paper is to propose a database architecture and a data model associated allowing multiple agents to work collaboratively and synchronously to perform high-level tasks. Therefore, it fulfils requirements and needs of Industry 4.0: interoperability, scalability, flexibility and resilience. The proposed architecture and model are implemented on a cyber-physical production system (CPPS) which is used in order to show and discuss several use cases examples.
Sustainable production systems are an important factor that contributes to the desired reduction of greenhouse gas emissions and resource efficiency for the industrial sector. Especially the digitalization of legacy factory equipment using a digital retrofit constitutes a major building block for this goal.
In this paper the authors will explain the contribution of digital retrofit towards different use cases and objectives regarding resource efficiency. We will discuss the challenges for the practical implementation of a digital retrofit project. As a solution we will present a new implementation model approach based on standard KPIs and basic data models to tackle these obstacles and to guarantee a successful realization of digital retrofit in practice.
The emergence of highly instrumented manufacturing systems has enabled the paradigm of smart manufacturing that provides high levels of prognostics functionality. Of particular interest is to precisely determine geometric conformance or non-conformance of workpieces during manufacturing. This paper presents a new dimensional product health monitoring system that learns from in-process sensor data and updates the prediction of the product quality as the product is manufactured. The system uses data from multiple manufacturing stages, unlike from a single stage at a time, to predict the dimensional quality of the finished product that is updated with subsequent measurements such as On-Machine Measurements (OMMs), in on-line incremental learning fashion. It is based on self-supervised neural networks for dimensionality reduction, Gaussian Process Regression (GPR) models for probabilistic prediction about the end product condition and the associated uncertainty, and Bayesian information fusion for updating the conditional probability distribution of the end product quality in the light of new information. The monitoring approach was tested on the prediction of diameter deviations with validation results showing its ability to achieve an average accuracy better than 5 μm in terms of the Root Mean Squared Error (RMSE). Having obtained a Probability Density Function (PDF) for the measurand of interest, the conformance and non-conformance probabilities given the tolerance specifications are computed to support the principle of inspection by exception. This ability to construct a conformance probability-based product quality monitoring system using probabilistic machine learning methods constitute a step change to manufacturing prognostics.
Aiming at the low efficiency and intelligence degree in the parameter setting of computer-aided manufacturing (CAM) numerical control (NC) programming caused by manual work, an intelligent decision-making method of CAM NC programming parameters based on model-based definition (MBD) is proposed. The hierarchical process model uses MBD technology to express the process flow and manage the process information. The tightness between process and NC machining is enhanced by sorting out NC machining steps and extracting their process information automatically. Then a back propagation (BP) neural network is used to mine the experience of NC programming in historical cases. Sparrow search algorithm (SSA) and other methods are used to optimize BP neural network’s initial weight and threshold, learning rate, and feedback process. A CAM parameter decision model based on BP neural network is constructed. Taking the connecting rod of a certain type of diesel engine as the experimental object, the method increases programming efficiency by 47%, with an accuracy rate of 80%. It is proved that the intelligent decision-making method proposed in this paper has practicability and feasibility.
Industry 4.0 technologies enable manufacturing companies to efficiently utilise their assets and reach a competitive advantage. These technologies provide new methods of generating revenue from data-driven services and facilitate predictive maintenance through the use of real-time data analytics. Even after more than a decade from the start of the fourth industrial revolution, manufacturing industry still struggles to upgrade to Industry 4.0-compliant technologies and standards. Currently, the barriers and enablers facing manufacturing companies to adopt new technology are not well understood, and there is a significant gap in the available literature. The authors of this paper review the challenges, opportunities, and applications facing manufacturers from an academic and industrial perspective. In order to identify these factors, a literature review and a survey were chosen. Based on the results of this study, the main challenges identified are financial constraints, a lack of knowledge, and the complexity of new technologies. The research also identified opportunities for manufacturing firms, such as improving efficiency, reducing costs, and improving quality. Potential applications for manufacturers include sharing machine status, predictive maintenance and controlling machines remotely. In the current work, these factors have been ranked from an industrial perspective.
Within Industry 4.0 the communication between the physical and the cyber part of manufacturing system faces an ever-growing rise in complexity. The Asset Administration Shell (AAS) is an information framework, within Industry 4.0, that describes the technological features of an asset. It was created to present data and information in a structured and semantically defined format, allowing for interoperability. The work addresses the industrial implementation of AAS, where a systematic literature review has been carried out to investigate the features of the implemented AAS metamodel, and the tools used for the realization of the models. A study of the convergence present in literature between the AAS and Digital Twin (DT) has also been carried out. This paper presents a reference of AAS tools and information for industry practitioners, as well as suggestions for research gaps in the standardization of AAS information modelling.
Machine utilization and production efficiency of manufacturing systems can be effectively improved through reasonable production scheduling. Traditionally, production scheduling and maintenance planning are considered as two independent issues, but it may lead to a suboptimal solution that is unable to maximize the productivity of the manufacturing system. Therefore, a mission reliability-oriented integrated scheduling model that considers production planning and maintenance activities is proposed. Firstly, the mission reliability that takes into account product type and equipment performance is defined to characterize production rhythm. Secondly, the maintenance strategy based on machine degradation cumulative failure and stochastic failure is proposed to guarantee the mission reliability of the machine effectively. Thirdly, an integrated scheduling model is established with the goal of minimizing total operational time, and Genetic algorithm is tailored to find the best production scheduling plan. Finally, a case study and comparative study of the cylinder head manufacturing system are presented to demonstrate the effectiveness of the proposed method. Results show that the proposed method is more suitable for production practice than the previous production scheduling strategy.
This paper describes how a Smart Learning Factory enables manufacturing SMEs of capturing the benefits of highly complex tools
and enablers such as virtual simulation and the Digital Twin. The collaborative factory design approach is enabled by embedding
the use of discrete event simulation connected with physical objects placed on a Digital Twin Module (DTM). The users can
manipulate the physical objects as physical counterparts to the machines and equipment in the virtual space and visualize the
designed factory and make further analysis. The method combines the use of dynamic discrete event simulation seamlessly
connected with physical objects placed on the DTM to enable collaborative design. The bridging between simulation and physical
objects is done by using a digital integration platform. Using physical artifacts as counterparts of the virtual objects in the simulation,
participants confidently interact with the simulation regardless level of skills and competencies. The Smart Learning Factory is
helping SMEs to get inspired using physical and virtual simulations for factory design and re-design, and develop solutions in a
collaborative environment. This is in-line with the theory of lean automation, that suggests making simple and cheap automation
and automating the correct value-adding processes. A demonstration case of designing a production setup in cooperation with a
SME is developed and documented.
This review is on the various techniques and methodologies applied to sensor based monitoring of the quality and control of defects in an advanced joining process named friction stir welding (FSW). This study is aimed to come up with Industry 4.0 automation of the same, which will help in exchanging data efficiently for a sustainable growth in the industrial sector. The implementation of sensors into the existing FSW machine chan-nelizes the information about the process in real time, and subsequently helps in controlling the process anomaly, and thereby ensures product quality and precision. Important aspects of FSW viz. its process parameters , weld zones, and defects have been discussed from the monitoring and control point of view. In this context, the applicability of various sensors such as force, torque, current, power, temperature, vibration, acoustic emission, and imaging to acquire information about the process have been discussed. In addition to the sensing systems, processing techniques have also been discussed. A case study has also been presented to introduce the readers with the behavior of some of the signals, and semantic utilization of the processing techniques in FSW. Towards the end, a roadmap for implementing the idea of Industry 4.0 in FSW has been proposed.
The fourth industrial revolution or industry 4.0 has become a trend topic nowadays, this standard-based strategy integrates Smart Factories, Cyber-physical systems, Internet of Things, and Internet of Service with the aim of extended the capacities of the manufacturing systems. Although several authors have presented the advantages of this approach, few papers refer to an architecture that allows the correct implementation of industry 4.0 applications using the guidelines of the reference architecture model (RAMI 4.0). In this way, this article exposes the essential characteristics that allow a manufacturing system to be retrofitting as a correct industry 4.0 application. Specifically, an intelligent manufacturing system under a holonic approach was developed and implemented using standards like FDI, AutomationML and OPC UA according to the RAMI 4.0
The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of ‘‘semantic similarity’’ to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a set of five defining characteristics, 11 technologies and three enabling factors that are considered relevant for the smart manufacturing scope. This article then evaluates the derived structure by matching the characteristics and technology clusters of smart manufacturing with the design principles of Industry 4.0 and cyber-physical systems. The authors aim to provide a solid basis to start a broad and interdisciplinary discussion within the research and industrial community about the defining characteristics, technologies and enabling factors of smart manufacturing.
An application of Industry 4.0 methods to the production of packaging films is presented. The production planning issues that are addressed often include contrasting objectives and strategies between customer's service and production optimization. We present an Advanced Planning and Scheduling (APS) tool that allows the decision maker to automatically generate and chose among a wide range of differently optimized scenarios. The vast amount of different results is analyzed and presented to the decision maker, using advanced data analytics techniques in order to put him/her in the condition to rapidly take an aware and solidly supported decision.
The pervasive diffusion of Information and Communication technologies (ICT) and automation technologies are the prerequisite for the preconized fourth industrial revolution: the Industry 4.0 (I4.0). Despite the economical efforts of several governments all over the world, still there are few companies, especially small and medium enterprises (SMEs), that adopt or intend to adopt in the near future I4.0 solutions. This work focus on key issues for implementing the I4.0 solutions in SMEs by using a specific case example as a test bench of an Italian small manufacturing company. Requirements and constraints derived from the field experience are generalised to provide a clear view of the profound potentialities and difficulties of the first industrial revolution announced instead of being historically recognised. A preliminary classification is then provided in view to start conceiving a library of Industry 4.0 formal patterns to identify the maturity of a SME for deploying Industry 4.0 concepts and technologies.
In the past five years, several industrial initiatives such as “Industry 4.0”, “Industrial Internet of Things”, “Factories of the Future” and “Made in China 2025”, have been announced by different governments and industrial leaders. These initiatives lead to an urgent need to advance current manufacturing systems into a high level of intelligence and autonomy. As the main component of any manufacturing system, machine tools have evolved from manually operated machines into the current computer numerically controlled (CNC) machine tools. It is predicted that current CNC machine tools are not intelligent and autonomous enough to support the smart manufacturing systems envisioned by the aforementioned initiatives. Inspired by recent advances in ICT such as Cyber-Physical Systems (CPS) and Internet of Things (IoT), this paper proposes a new generation of machine tools, i.e. Machine Tool 4.0, as a future development trend of machine tools. Machine Tool 4.0, otherwise known as Cyber-Physical Machine Tool (CPMT), is the integration of machine tool, machining processes, computation and networking, where embedded computers and networks can monitor and control the machining processes, with feedback loops in which machining processes can affect computations and vice versa. The main components and functions of a CPMT are presented. The key research issues related to the development of CPMT are identified and discussed. A three-layer CPMT-centered Cyber Physical Production System (CPPS) is proposed to illustrate both the vertical integration of various smart systems at different hierarchical levels and the horizontal integration of field-level manufacturing facilities and resources.
Today, small and medium-sized enterprises (SMEs) are the bedrock of most economies. Additionally, they have the potential to become the foundation of sustainable economic growth if given the right tools and support. In order to stay competitive, SMEs have to adapt permanently both in products and production. Approaches related to the vision of Industrie 4.0 (I4.0) and Smart Factories can help SMEs to address these challenges. The use cases, implementation examples and recommendations of the “Plattform Industrie 4.0” provide a basis for these approaches. However, the success of these approaches will depend on whether or not SMEs will adopt and implement them. Nevertheless, the barriers for adopting I4.0 approaches and technologies are still high. Specialized research institutions in Germany – so-called I4.0 test environments – have a broad infrastructure for developing and testing I4.0-relevant technologies, such as production machines and equipment or ICT testing facilities.
Industry 4.0 is initiated by the German government as a high level technology strategy in order to keep Germany in a global leading position in manufacturing, also intends to solve energy and aging issues in parallel. Germany has proposed the “Reference Architecture Model Industries 4.0” which is based on the regulations published by the International Electrotechnical Commission. The manifestation of Industry 4.0 can be achieved by the realization of digital factory. And it's emphasized on total integration with Cyber Physical System as its core technology, via Internet of Things to realize the operational environment of human machine interaction, and the utilization of Big Data for decision making. The research motivation of this thesis is to utilize Totally Integrated Automation Portal as a digital factory software development platform, to achieve the fundamental educational requirements of digital factory, and to understand Industry 4.0 concept. Methodology of this research is based on a set of experimental and training mechanism, the sequence of machine movements is controlled by a Programmable Logic Controller, and this PLC has to be complied with IEC61131 International Standard. Regarding communication technology also to be complied with IEC61158 and IEC62541 e.g. PROFINET and OPC UA protocol. And finally realizing the operational environment of human machine interaction via smart phone according to the outcome of this research, it has been shown that total integration concept can be realized by single production machine. By standardization of factory environment and increasing the level of operational environment of human machine interaction, productivity and quality of production could be significantly increased. PLC could be used to analyze intelligent platform and comply with the Industry 4.0 standard at the same time.
As subject for illustration the so called Industrie 4.0 Demonstrator was chosen. This is a representative example of a production application. The Industrie 4.0 Demonstrator illustrates some core aspects of Industrie 4.0 (I4.0) by concretization of various application scenarios. At first glance, this task appeared quite simple. The concrete modeling however has quickly shown a high degree of complexity and associated challenges which a user in the context of I4.0 is confronted with.
The target audience of this document are users who want to better understand the content and application of the Reference Architecture Model Industrie 4.0 (RAMI 4.0), specifically the concept of I4.0 Components and their administration shells, from an application perspective.
Some selected concepts of RAMI 4.0 are described and concrete I4.0 Components of the I4.0 Demonstrator are proposed and modeled. These descriptions do not claim to be complete with respect to the concepts of RAMI 4.0 or I4.0 Components.
It should be emphasized that the focus is on concepts. The realization of the I4.0 Demonstrator itself was done without any “guiding principle” of RAMI 4.0 or I4.0 Components. It was implemented based on currently commercially available products. The I4.0 Demonstrator was largely driven by the intended customer benefits – which are addressed by the appropriate application scenarios – and less by new technical concepts and solutions.
The I4.0 Demonstrator is based on commercially avail-able products. I4.0 Components are proposed by enriching the existing implementation. This was based on application scenarios, for which a qualitative benefit for the involved stakeholders can be identified.
Therefore, the implementation of I4.0 does not require complete new products, but – as has been illustrated – the management of information related to the assets will be organized in a more structured way. Of course, it is assumed that in the context of I4.0 new products will emerge in the market. But which specific products this really will be, will ultimately be decided by their market success.
Also, it seems not effective to transfer all the information, which today is already available in form of data sheets, (informal) engineering data, runtime data, etc., brute-force into a new formal structure. Organizing data in a more structured way results in an expense, partially in the development and production of products, but also in engineering of solutions. This will only be accepted if there this generates a benefit in total with respect to all stakeholders concerned.
In the engineering and manufacturing domain, there is currently an atmosphere of departure to a new era of digitized production. In different regions, initiatives in these directions are known under different names, such as industrie du futur in France, industrial internet in the US or Industrie 4.0 in Germany. While the vision of digitizing production and manufacturing gained much traction lately, it is still relatively unclear how this vision can actually be implemented with concrete standards and technologies. Within the German Industry 4.0 initiative, the concept of an Administrative Shell was devised to respond to these requirements. The Administrative Shell is planned to provide a digital representation of all information being available about and from an object which can be a hardware system or a software platform. In this paper, we present an approach to develop such a digital representation based on semantic knowledge representation formalisms such as RDF, RDF Schema and OWL. We present our concept of a Semantic I4.0 Component which addresses the communication and comprehension challenges in Industry 4.0 scenarios using semantic technologies. Our approach is illustrated with a concrete example showing its benefits in a real-world use case.
The vision of the 4th industrial revolution describes the realization of the Internet of Things within the context of the factory to realize a significantly higher flexibility and adaptability of production systems. Driven by politics and research meanwhile most of the automation technology providers in Germany have recognized the potentials of Industry 4.0 and provide first solutions. However, presented solutions so far represent vendor-specific or isolated production system. In order to make Industry 4.0 a success, these proprietary approaches must be replaced by open and standardized solutions. For this reason, the SmartFactoryKL has realized a very first multi-vendor and highly modular production system as a sample reference for Industry 4.0. This contribution gives an overview of the current status of the SmartFactoryKL initiative to build a highly modular, multi-vendor production line based on common concepts and standardization activities. The findings and experiences of this multi-vendor project are documented as an outline for further research on highly modular production lines.
Today the term Industry 4.0 (I4.0) is omnipresent and many machine builders want to benefit from the advantages by integrating these new technologies e.g. OPC UA into their products. However, due to economic reasons, it is not feasible to exchange existing industrial production lines with the new I4.0-enabled technologies at once. Therefore, this paper will propose a retrofitting approach for the gradual migration to I4.0 by providing a defined schedule with specified deliverables consisting of four consecutive phases. In addition, a new business model called Retrofitting as a Service (RaaS) for future systems will be introduced to show a possible usage of retrofitting with a specific use case.
Industry 4.0 (I4.0) is accompanied by a variety of technologies which offer great potential for optimizing the manufacturing of electric motors. However, the application of I4.0 technologies in this sector has hardly been examined yet. For determining I4.0 potentials in the electric motor production, a structured approach is required since the variety of sub-processes and production technologies results in a vast number of possible combinations. Therefore, this paper first compares different generic approaches for identifying, selecting and implementing I4.0 use cases. Building on this, a methodical approach is derived in order to tap the numerous I4.0 potentials within the electric motor production. On the one hand, use cases can be derived from current technological opportunities resulting from application examples and best practices in research and industry. On the other hand, concrete problems in critical manufacturing processes can give rise to the application of novel I4.0 solutions. By presenting a comprehensive overview of promising application scenarios, this paper mainly facilitates the identification of I4.0 potentials in the electric motor production from an opportunity-driven perspective. In addition to approaches directly addressing the electric motor production, concepts can be derived from related processes in other application domains. Examples are provided by outlining a selection of the presumably most relevant use cases. The results indicate that especially data-driven approaches, i.e. data analytics and machine learning, offer great potential in electric motor production. As an outlook, I4.0 potentials can also be disclosed from a problem-pull perspective, supplementing the opportunity-push approach of this paper.
Industrial Internet platforms have the ability to access, manage and control product-related data, information and knowledge across all the lifecycle phases (beginning of life, middle of life and end of life). Traditional product lifecycle management/product data management software have many limitations when it comes to solving product lifecycle management challenges, like interoperability for instance. Industrial Internet platforms can provide real-time management of data and information along all the phases of a product’s lifecycle. Platform openness in combination with the above-mentioned industrial internet platform characteristics helps solve the product lifecycle management challenges. This article describes the product lifecycle management challenges in detail from the existing literature and presents solutions using industrial internet platform openness and related dimensions as well as sub-dimensions. A wide pool of platforms is narrowed down to specific platforms that can solve the documented product lifecycle management challenges and allow the manufacturing companies to collaborate as well as enhance their business. We also present in detail managerial implications toward long-term and sustainable selection of industrial internet platform.
In the manufacturing domain, interoperability represents a characteristic of a manufacturing system in which its components are capable of exchanging information with one another, using the information that has been exchanged. Even though the discussion about interoperability issues can trace back to the 1970s, system interoperability is still the “elephant in the room”. Additionally, in the past few years, research topics related to the fourth industrial revolution, also known as Industry 4.0, have been gradually accepted and promoted by governments and organizations all around the world. A research question then arises: what is the role of interoperability in the fourth industrial revolution era? The aim of this paper is to provide a scientific and evidence-based answer to this question. From an academic perspective, a systematic literature review was carried out to discover the main concepts related to interoperability in an Industry 4.0 context. From an industrial perspective, a questionnaire survey was developed to guide the application of a multi-criteria decision analysis method, the Analytic Hierarchy Process, used to analyze and make explicit the relationships of the previously discovered concepts. Results of this study can be used as a basis for future interoperability research in this new industrial revolution wave.
Just-in-time manufacturing is a main manufacturing strategy used to enhance manufacturers’ competitiveness through inventory and lead time reduction. Implementing just-in-time manufacturing has a number of challenges, for example, effective, frequent and real-time information sharing and communication between different functional departments, responsive action for adjusting the production plan against the continually changing manufacturing situation. Internet of Things technology has the potential to be used for capturing desired data and information from production environment in real time, and the collected data and information can be used for adjusting production schedules corresponding to the changing production environment. This article presents an Internet of Things based framework to support responsive production planning and scheduling in just-in-time manufacturing. The challenges of implementing just-in-time manufacturing are identified first and then an Internet of Things based solution is proposed to address these challenges. A framework to realise the proposed Internet of Things solution is developed and its implementation plan is suggested based on a case study on automotive harness parts manufacturing. This research contributes knowledge to the field of just-in-time manufacturing by incorporating the Internet-of-Things technology to improve the connectivity of production chains and responsive production scheduling capability.
CyberManufacturing System is an advanced vision for future manufacturing where physical components are fully integrated and seamlessly networked with computational processes, forming an on-demand, intelligent, and communicative manufacturing resource and capability repository with optimal and sustainable manufacturing solutions. The CyberManufacturing System utilizes recent developments in Internet of things, cloud computing, fog computing, service-oriented technologies, among others. Manufacturing resources and capabilities can be encapsulated, registered, and connected to each other directly or through the Internet, thus enabling intelligent behaviors of manufacturing components and systems such as self-awareness, self-prediction, self-optimization, and self-configuration. This research presents an introduction to the CyberManufacturing System, establishing the architecture and functions of the CyberManufacturing System, designing the pivotal control strategy, and investigating the performance analysis of the CyberManufacturing System using modeling and simulation techniques. In total, five component-level examples and one system-level case study have been developed and used for illustration and validation of the CyberManufacturing System operations. The results show that the CyberManufacturing System is superior to other types of manufacturing systems in terms of functionality and cooperative performance.
In this chapter, the base Information Model of OPC UA is introduced. This model provides the foundation for OPC UA information modeling and is always used as foundation to define additional Information Models. We will also look at the extensions of this model defined by the OPC UA specification. Those extensions are used to define a standard way to represent capabilities and diagnostic information of an OPC UA server in its Address Space and how specific information for current data, historical data, state machines, programs, alarms, and conditions are modeled. Depending on your application you should use those extensions (for example, in the case of Data Access) or you must use them (for example, in the case of Historical Data where it is required to provide certain information). Finally, we take a look at what standard Information Models are currently in development by other organizations based on OPC UA. Maybe there are already activities in your domain that you can use or should join.
Before looking at those standard Information Models, we start this chapter by considering how to handle standard Information Models. What is defined by an OPC-UA-based Information Model, how you can actually define such a model, and what mechanisms are built into OPC UA allowing servers and clients to work simultaneously with different Information Models?
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