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![Fig. 1. Production Volume vs Variety, derived from [19].](profile/Jeff-Morgan-6/publication/350690571/figure/fig1/AS:1009976441069568@1617808556041/Production-Volume-vs-Variety-derived-from-19_Q320.jpg)
This paper provides a fundamental research review of Reconfigurable Manufacturing Systems (RMS), which uniquely explores the state-of-the-art in distributed and decentralized machine control and machine intelligence. The aim of this review is to draw objective answers to two proposed research questions, relating to: (1) reconfigurable design and in...
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... and small batch sizes [27]. This is a significant disadvantage to SMEs, as the capability to dynamically adjust, grow, and ultimately evolve product portfolios in-line with market demand is limited, or split between: 1. manual low volume / high variety batches, and 2. automated high volume / low variety medium to large batches; as depicted in Fig. ...
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... systems (FMSs) can produce a variety of products, with changeable volume and mix, on the same system. However, FMS typically utilize general purpose technology, which have a range of operational flexibility, but at lower throughput speeds due to sequential operations. This is a tradeoff between speed and flexibility, as depicted previously in Fig. 1. A typical example of an FMS is a CNC machine tool, which is capable of multi-axis dynamic motion, and custom part production. Throughout the literature key focus is given to the correct classification between RMSs and FMSs. For example, CNC machine tools are considered FMSs, but can exhibit reconfigurable behaviors with custom ...
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... reconfigurable machine examples are provided with reference to: Reconfigurable Machine Tools (RMT), Reconfigurable Assembly Machines (RAM), and Reconfigurable Inspection Machines (RIM). An example of an RMT can be seen in Fig. 4-1. This example identifies the multilayered modular capability of a reconfigurable machine, including both flexibility ΔF "to bend as part of the body" [44], and reconfigurable ΔR "to change the shape or formation" [44]. The composition of this example adheres to the RMS definition and characteristics, for hardware, software, and control ...
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... Both systems can be observed as potentially competing models. However the layered control aspect of CPS can consider the DT a high level of control intelligence, the 'cyber → cognition→ configuration' layers as defined in the 5C implementation model for CPSs [7] [30]. To provide context to this comparative, Lu et al. created visual as seen in Fig. 10 ...
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... supports vertical and horizontal integration and CPS intelligence; can be further encapsulated by three computational layers, namely: the Edge, the Fog, the Cloud. Philosophically this represents the 4th industrial (r)evolution of the centralized 'biological mind', transcending to the distributed cyber physical 'digital mind', as depicted in Fig. ...
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... several "Smart Machine" architectures and models. These models act as convergence points in literature for applied research and technology development. Observably, there are similarities in their abstraction of the machine control, intelligence, virtualization, modularity, universal service integration, and collaborative communication; as seen in Fig. 12. Objectively, these models can form part of, or integrate with, the CPS 5C architecture and its maturity model, as represented in Fig. 9. CPS is hypothesized to be a holistic model for advancing intelligent control, as stated in Section 3.3.3. To assist with further imagination of these models, the depiction of the "digital mind" Fig. ...
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... seen in Fig. 12. Objectively, these models can form part of, or integrate with, the CPS 5C architecture and its maturity model, as represented in Fig. 9. CPS is hypothesized to be a holistic model for advancing intelligent control, as stated in Section 3.3.3. To assist with further imagination of these models, the depiction of the "digital mind" Fig. 10, and "digital avatar" Fig. 11, should be ...
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... these models can form part of, or integrate with, the CPS 5C architecture and its maturity model, as represented in Fig. 9. CPS is hypothesized to be a holistic model for advancing intelligent control, as stated in Section 3.3.3. To assist with further imagination of these models, the depiction of the "digital mind" Fig. 10, and "digital avatar" Fig. 11, should be ...
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... following leading smart machine models are discussed and compared for context within this present review. Fig. 12-1, depicts the Asset Administration Shell [92]. In this model, a 'thing' is a globally uniquely identifiable object, e.g. a machine or a station with a communication capability. Once the 'thing' is connected to, or conforms to, the administration shell requirements, it becomes a standard Industry 4.0 component within a wider information ...
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... explored "Plug and Work" capabilities for modular services within automation hierarchies, and industrial standards including OPC-UA [30]. The benefits of which, are being estimated at 20 % reduction in machine startup time/cost and a 70 % reduction in vertical integration time/cost [174]. For reference, the OPC-UA standard is open access. Fig. 12-2, depicts an Agent/Holonic model for machine control [113,114]. This model incorporates high and low, or bi-level, communication. The lower level real-time machine controls, or 'decision making', is standardized in PLC function block coding. The higher level decision making capabilities of the machine, are abstracted and elevated into ...
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... standardization of the Agent model is recognized in OPC-UA: Programs [177], Dorofeev & Zoitl [178] explored the application of which in combination with standardized PackML state-machines. For reference, a universal robot control architecture that is similar to the Agent model, is recognized in the open source Robot Operating System (ROS) [179]. Fig. 12-3 depicts a "basic" Software-defined Cloud manufacturing architecture, with two planes: 1. hardware and 2. software, and three layers: 1. hardware, 2. control, and 3. virtual [180]. Uniquely, this model explores the use of "virtualization", e.g. software-defined control environments/platforms, in which the 'control layer' is an ...
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... challenges in complex networking, avoid vendor "lock-in" problems, and reduce restrictions in change and innovation. The key to SDN is standardization, most notably through the OpenFlow protocol [184]. For reference, a resource which promotes SDN standardization is the Open Networking Foundation (ONF), which offers SDN open source software. Fig. 12-5, depicts a Digital Twin (DT) Five-Dimension (5D) model [8,185]: D1. Physical Entities -consisting of a device or product, physical system, activities process, and even an whole organization; D2. Virtual Models -faithful replicas of physical entities, which reproduce the physical geometries, properties, behaviors, and rules; D3. ...
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... Machine Tool', which is true to the DT origin, with coupled simulation components [189]. Leng et al. are exploring the synchronization between Digital Twin cyber physical components, with bi-level IIoT communication [124], and blockchain [171]. For reference, an emerging 'IT centric' open source platform for DT's is called Eclipse Ditto [190]. Fig. 12-4, depicts a Anthropocentric CPS (A-CPS) model [191], which recognizes humans as a key component in intelligent manufacturing systems. A similar model, is the Human-CPS (H-CPS) model [192], as seen in Fig. 12-6. Both models depict a "Human--in-the-Loop", yet each explore paradigm from different perspectives. Pirvu et al. [191] defined ...
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... [124], and blockchain [171]. For reference, an emerging 'IT centric' open source platform for DT's is called Eclipse Ditto [190]. Fig. 12-4, depicts a Anthropocentric CPS (A-CPS) model [191], which recognizes humans as a key component in intelligent manufacturing systems. A similar model, is the Human-CPS (H-CPS) model [192], as seen in Fig. 12-6. Both models depict a "Human--in-the-Loop", yet each explore paradigm from different perspectives. Pirvu et al. [191] defined the A-CPS architecture through state-of-the-art HMI methods and technologies, such as Virtual Reality (VR) and Augmented Reality (AR). Ji et al. [192] explored the H-CPS model through advancing cyber ...
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... switches, etc. A reconfigurable hardware/software composition would ensure new maintenance tasks could be added throughout a machine's lifecycle. The identification and exposure of key problem-solving reconfigurable machine activities aims to 'close-the-loop' in the CPS architecture, in safely Verified and Validated (V&V) ways, as depicted in Fig. 13. However, the severity of the failure is a key factor, as a machine could implement simple preventative maintenance tasks to avoid a sever failure. If a severe failure was to occur, it might not have the autonomous capability or high level intelligence to identify or solve the problem. Therefore, if the failure is outside the ...
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... The Orchestrator is responsible for the system as a whole, 'the collective'. The Machine is responsible for its task, 'the individual'. Universally, Orchestrators represent collectives and act as central contact points in decentralized cyber-physical environments, for configuration, co-ordination, and/or adaptive behavior, as depicted in Fig. ...
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... an effort to depict the conceptual convergence of the three reviewed research sections, Fig. 15 is presented. This figure identifies a fundamental RMS foundation, consisting of: -Coupling and decoupling: Hardware, Software, Information, Communication -Changeable structures, Simultaneous operations, and Open control architectures. -Characteristics: Modularity, Integrability, Customization, Convertibility, Scalability, ...
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... Technology (IT) for machine Intelligence; as distributed and decentralized environments, which are horizontally and vertically integrated. The convergence of these domains is represented with a intermedium virtual layer, which is enabled by industrial communication and data standardization, virtual models, and collective orchestrators. As such, Fig. 15 depicts closing the loop in distributed and decentralized control and intelligence systems, which has the potential to enable extraordinary Smart Reconfigurable (SR*) capabilities in next generation Industry 4.0 SR* manufacturing machines and ...
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... and vertically integrate technology and create effective manufacturing equipment. As such, RMS leverage this foundation of dynamic OT composition, yet seek to further advance its capability and capacity in relation to: speed (rapid adjustment), cost reduction (labor, burden, material), and provider (external, internal, automated); as depicted in Fig. 16. Furthermore, reconfigurable designs seek to predefine and standardize the method of change, becoming proactive in a less critical timeframes (design phase), rather than being reactive in a highly critical timeframe (production phase). Key enabling and distinguishing reconfigurable aspects includes: the reconfigurable automation of ...
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... (DC) and Decentralized Control (DZC) machines/systems; and the incorporation of reconfigurable intelligent behaviors for individual and collectives of machines/systems. A state-of-the-art technology stack that supports and enables modern RMS, is represented in the parallel, horizontal/vertically integrated domains of IT and OT, as depicted in Fig. 15. Both domains exhibit distributed and decentralized computing control, and intelligence paradigms, to enable increased scalability, technical agility and failure resilience. Presently, the convergence of these domains is being further enabled through the alignment of end-to-end machine control, data, and communication standards, as ...
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... Impact -An ideal future-state of SR* machines would be full-scale reconfigurable autonomy. However, this could be highly impractical and highly costly, and therefore a balance is needed, taking into account the change/reconfigurable cost, speed, and frequency, as depicted in Fig. 16. As such, there is an opportunity to provide more 'business impact cost analysis' studies, to examine the effects of faults in the centralized production systems, in contrast to RMS. This would be an incentive to incorporate higher levels of reconfigurable DZC designs, to increase reliability through redundancy, and adaptive control ...
Citations
... Manufacturing halls are the cornerstone of industrial operations, where speed, efficiency, and safety of material and component transportation play crucial roles in achieving maximum productivity [26][27][28]. In this context, increasing attention is being paid to the development and implementation of innovative transportation means that can enhance efficiency and flexibility within manufacturing halls. ...
This article presents the design of a smart three-wheeled unit for the manufacturing industry with the aim of optimizing and automating internal logistical processes. It presents an innovative solution that combines the advantages of mobility, intelligent transportation technology, and smart devices to ensure the efficient movement of materials and raw materials in manufacturing facilities. The article describes the design, production, and testing of the tricycle in a real manufacturing environment of the production system and the testing of the proposed smart devices. It evaluates the advantages of the electric smart tricycle, including increased efficiency, reduced costs, and more flexible production processes. The results of this study suggest that the intelligent three-wheeled unit represents a promising technological innovation with the potential to increase competitiveness and productivity in manufacturing enterprises.
... Hawryluk et al. presents the concept of a multifunctional automatized forging station with a supervisory system of the process and the production management, which is mainly applicated at forging shops equipped with older-generation devices and forging units [3]. Morgan et al. found that smarter machines are sought for to dynamically and rapidly meet the requirements of today, tomorrow, and across their products lifecycle [4]. At present, the manufacturing industry has gradually developed towards intelligence, while the intelligence of the forging industry is still in the initial stage. ...
... The integrated management platform consists of an industrial computer and a programmable logic controller (PLC). Table 1 The forging procedure and the tolerance of the process parameters of a type of shaft forging 1 Return height: the return height of the hamming block of the quick forging machine after forging 2 Numbers of blow: the number of blows within a pass of quick forging machine 3 Manipulator return height: the vertical movement distance of the manipulator after one pass forging 4 Offset amount: the vertical movement height of the manipulator/robot after each blow 5 Detection step: 1 represents forging inspection after this step, 0 represents no forgings inspection process Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
The engine main shaft forgings have high requirements for product consistency and reliability, which are difficult to be guaranteed by traditional manual free forging. On the basis of the already-in-place machinery, technical optimization was done in order to realize intelligent forging of the engine main shaft forgings. A rail trailer, holding robot, inspection robot, and expert system were installed as well as other hardware and software. Additionally, the procedure and specifications for robot intelligent free forging were revised. Based on the artificial neural network (ANN) model, an optimization model and a prediction model were created, and the process parameters can be controlled during forging. The verification result shows that the intelligent free forging production line can achieve real-time control of the shaft forging process, and obtain the forgings whose shape, size, microstructure, performance and consistency meet the requirements. With the help of this production line, free forging can be produced more quickly and efficiently, which is crucial for realizing the automation, digitization, and intelligence of shaft forging free forging.
... Continuing with this industry, in [7] illustrate the development of smart reconfigurable machines in terms of adaptability and reconfiguration. By automatically adapting their operations to changing production requirements, these machines represent a significant advancement and are essential to maintaining high-quality standards in a dynamic and constantly changing environment. ...
The digital transformation in Industry 4.0 has revolutionized quality control paradigms, integrating advanced technologies such as augmented reality, deep learning, and computer vision systems into a new era called "Quality 4.0". This study systematically reviews how these technologies are shaping new practices in continuous quality supervision and improvement, adapting to increasingly automated and connected production environments. Through a comprehensive analysis of the literature, practical applications in various industrial sectors, from manufacturing and agriculture to the production of sweets and snacks, are highlighted. Additionally, the conceptual and detailed design of a quality control system using artificial intelligence tools is presented, focusing on the inspection of dimensions, color, weight, and labeling of MDF boxes. The proposed system demonstrates an effective integration of sensors, high-definition cameras, and deep learning algorithms, validated through tests with boxes of different sizes. The results suggest that the adoption of these technologies not only improves the accuracy and efficiency of inspections but is also essential to maintaining high-quality standards in a dynamic and competitive industrial environment.
... In light of the aforementioned context, industrial systems must possess the capacity for reconfiguration and flexibility in order to adapt swiftly to market changes [14][15][16]. In this context, highly experienced operators are of great importance, as they possess the necessary skills to perform tasks such as programming, maintenance and diagnostics [17][18][19][20]. ...
In the context of Industry 4.0, industrial environments are at a crossroads, facing the challenge of greater flexibility and significant technical skills gaps. In this situs, Augmented Reality (AR) emerges as a transformative tool, enhancing the synergy between technical staff and emerging technologies. This article focuses on exploring the integration of AR in Industry 4.0, with a particular emphasis on its role in improving technical assistance and training. The research addresses the ways in which AR not only facilitates more efficient processes but also acts as an essential bridge for training and skills development in constantly changing technological environments. It investigates the significant impact of AR on both optimising work processes and training workers to meet the emerging challenges of Industry 4.0. Through a qualitative analysis, the studies are categorised according to their application domains, grouping them into specific thematic areas. Subsequently, a meta-analysis is conducted to determine the actual impact of AR in the sector. The findings reveal a positive and significant correlation between the implementation of AR and its effectiveness in assistance and training in the framework of Industry 4.0. Finally, the article delves into an analysis of current limitations and challenges, providing insights into possible developments and trends in the use of AR for assistance and training in Industry 4.0.
... In the field of communication, compared with other technologies, 5G has KPIs such as ultra-high bandwidth (theoretical peak speed can reach 20G bps), lower latency (in milliseconds), massive connections, and higher reliability. These characteristics enable 5G to play a crucial role in the intelligent transformation of industrial systems, providing stable and efficient network support for applications such as the Internet of Things (henceforth IoT), machine learning, and artificial intelligence [5][6][7][8][9][10][11]. The difference between industrial IoT (henceforth IIoT) and non-industrial IoT mainly lies in bandwidth, latency, and reliability. ...
Under the auspice of further developing 5G mobile communication technology and integrating it with the latest advancements in the field of Industrial Internet-of-Things, this study conducts in-depth research and detailed analysis on the combination of 5G with industrial systems based on composite structures, communication network architectures, wireless application scenarios, and communication protocols. The status quo, development trend, and necessity of 5G mobile communication technology are explored and its potential in industrial applications is analyzed. Based on the current practical development level of 5G technology, by considering different requirements for bandwidth, real-time performance, and reliability in communication networks of industrial systems, this study proposes three feasible paths for the integration between 5G and industrial systems, including the method to use 5G in place of field buses. Finally, by introducing real-world cases, this study has successfully demonstrated the integration of 5G and industrial systems by extending 5G terminals as field bus gateways. This study provides valuable references for research and practice in related fields.
... The focus on the mean and the variance is based on the following two assumptions: (i) the single manufacturing process has small variability [10], and (ii) manufacturing lines are usually designed to work with a little product variety and utilization close to 100% [11]. However, these assumptions become weaker in the I4.0 and I5.0 paradigms because these systems need to be more flexible and more easily reconfigurable to meet demand fluctuations [12], thus requiring larger installed capacity, long transients, and frequent setups (i.e. utilization can no longer be close to 100%) [13]. ...
... Moreover, advancements in connectivity technologies such as 5G networks and edge computing promise to revolutionize the way data is transmitted, processed, and utilized within manufacturing environments, unlocking new levels of agility and efficiency. Automation lies at the heart of Industry 4.0, offering the promise of increased efficiency, precision, and flexibility in manufacturing processes [3]. Through the deployment of intelligent robotics, AI-driven algorithms, and autonomous systems, organizations can automate routine tasks, streamline production workflows, and adapt swiftly to changing market dynamics. ...
The advent of Industry 4.0 has catalyzed a transformative shift in advanced manufacturing, driven by the integration of cutting-edge technologies in connectivity, automation, and data exchange. This review delves into the pivotal role played by these technological pillars in reshaping the modern industrial landscape. In the realm of connectivity, emphasis is placed on the evolution towards ultra-fast and reliable communication networks, exemplified by the emergence of 5G technology and the proliferation of edge computing solutions. Automation, another cornerstone of Industry 4.0, is explored through the lens of artificial intelligence (AI) and robotics, showcasing how intelligent automation systems are revolutionizing production processes and enhancing operational efficiency. Moreover, the review investigates the critical importance of data exchange in facilitating seamless interoperability and information flow across interconnected manufacturing ecosystems. Key advancements in data exchange technologies, including blockchain and federated learning, are examined for their potential to ensure data integrity, privacy, and security. By synthesizing insights from these three domains, this review provides a comprehensive overview of the technological landscape driving the advancement of Industry 4.0 in advanced manufacturing. It underscores the transformative potential of connectivity, automation, and data exchange in fostering innovation, agility, and sustainability across industrial sectors, while also highlighting the challenges and opportunities that lie ahead in harnessing these technologies to their fullest extent.
... Cyber-Physical Systems (CPS) have gained significant attention in various industries, revolutionizing the way machines and systems operate. In the agricultural sector, CPS-based smart machines have shown immense potential in improving efficiency, productivity, and quality across different processes [2]. One such application is the integration of CPS technology into small-scale rice milling machines. ...
... Autonomous and Adaptive Operations [2]. CPS-based smart machines have the capability to operate autonomously and adapt to changing conditions. ...
... They are increasingly used in mission-critical scenarios with strict non-functional requirements (e.g., response-time latency and availability) [4,5,6,7,8,9]. Those scenarios adopt fog/edge-cloud and servicebased paradigms to support fast and automated reconfiguration [10,11,12,13,14]. Kubernetes (henceforth,K8s), is the de facto standard among container orchestration systems to provide automated management of services. ...
In this paper, we i) analyze and classify real-world failures of Kubernetes (the most popular container orchestration system), ii) develop a framework to perform a fault/error injection campaign targeting the data store preserving the cluster state, and iii) compare results of our fault/error injection experiments with real-world failures, showing that our fault/error injections can recreate many real-world failure patterns. The paper aims to address the lack of studies on systematic analyses of Kubernetes failures to date. Our results show that even a single fault/error (e.g., a bit-flip) in the data stored can propagate, causing cluster-wide failures (3% of injections), service networking issues (4%), and service under/overprovisioning (24%). Errors in the fields tracking dependencies between object caused 51% of such cluster-wide failures. We argue that controlled fault/error injection-based testing should be employed to proactively assess Kubernetes' resiliency and guide the design of failure mitigation strategies.
... Research on the integration of design and manufacturing with computer-based has been carried out [24], [25], [26]. Research of integration in batik production equipment by Ridho [27], Dwinugroho et al. [28], Akhmad et al. [29], Muthi'ah [30], Sudiarso and Kusumawardani [31], increase the ability to produce batik using a CNC batik. ...
The process of determining costs and prices in Make-to-Order (MTO) companies is generally complex because they make products according to special orders. Speed, accuracy, and consistency are necessary for a successful pricing estimation. Custom batik price, along with anticipated production time, is still done manually at the moment. Integration of design and manufacturing with computer-based was needed to produce custom batik products. This paper aims to develop a pricing decision system model for CAD custom batik motifs and written batik CNC machines used in the production processes. Customers can adapt their budgets by changing the parameters to suit their requirements and capacities using various price options. This model helps the customers to make buying decisions. The data and information were gathered through interviews, observations, and a literature review. The pricing decision system has been determined using the job order costing method, where prices are collected for each order separately according to the demand's identity or the order's cost. The system was developed through the prototyping process. This research has created a framework for determining the price for custom design batik based on 4 (four) parameters: the motif, the number of colors used, the type of color used, and the type (size) of the fabric used. Also, incorporating more comprehensive parameters has improved the system (motif size, number of colors, color selection, coloring techniques, and fabric sizes). The outcome of the calculation simulation demonstrates that the constructed model successfully calculated the unit price of products with accuracy. It shows that the system can be used in practical situations.
