Production System Architecture Research Articles

An ecotype-specific effect of osmopriming and melatonin during salt stress in Arabidopsis thaliana

BackgroundNatural populations of Arabidopsis thaliana exhibit phenotypic variations in specific environments and growth conditions. However, this variation has not been explored after seed osmopriming treatments. The natural variation in biomass production and root system architecture (RSA) was investigated across the Arabidopsis thaliana core collection in response to the pre-sawing seed treatments by osmopriming, with and without melatonin (Mel). The goal was to identify and characterize physiologically contrasting ecotypes.ResultsVariability in RSA parameters in response to PEG-6000 seed osmopriming with and without Mel was observed across Arabidopsis thaliana ecotypes with especially positive impact of Mel addition under both control and 100 mM NaCl stress conditions. Two ecotypes, Can-0 and Kn-0, exhibited contrasted root phenotypes: seed osmopriming with and without Mel reduced the root growth of Can-0 plants while enhancing it in Kn-0 ones under both control and salt stress conditions. To understand the stress responses in these two ecotypes, main stress markers as well as physiological analyses were assessed in shoots and roots. Although the effect of Mel addition was evident in both ecotypes, its protective effect was more pronounced in Kn-0. Antioxidant enzymes were induced by osmopriming with Mel in both ecotypes, but Kn-0 was characterized by a higher responsiveness, especially in the activities of peroxidases in roots. Kn-0 plants experienced lower oxidative stress, and salt-induced ROS accumulation was reduced by osmopriming with Mel. In contrast, Can-0 exhibited lower enzyme activities but the accumulation of proline in its organs was particularly high. In both ecotypes, a greater response of antioxidant enzymes and proline accumulation was observed compared to mechanisms involving the reduction of Na+ content and prevention of K+ efflux.ConclusionsIn contrast to Can-0, Kn-0 plants grown from seeds osmoprimed with and without Mel displayed a lower root sensitivity to NaCl-induced oxidative stress. The opposite root growth patterns, enhanced by osmopriming treatments might result from different protective mechanisms employed by these two ecotypes which in turn result from adaptive strategies proper to specific habitats from which Can-0 and Kn-0 originate. The isolation of contrasting phenotypes paves the way for the identification of genetic factors affecting osmopriming efficiency.

Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production systems. It investigates the optimal configuration methods for the architectural model of new energy hydrogen production systems in Xining City, Qinghai Province, as well as the internal storage battery, ALK hydrogen production equipment, and PEM hydrogen production equipment, aiming at various scenarios of power sources such as wind, solar, wind–solar complementary, and wind–solar–storage complementary, as well as intermittent hydrogen production scenarios such as hydrogen stations, hydrogen metallurgy, and continuous hydrogen production scenarios such as hydrogen methanol production. The results indicate that the fluctuation of hydrogen load scenarios has a significant impact on the installed capacity and initial investment of the system. Compared with the single-channel photovoltaic hydrogen production scheme, the dual-channel hydrogen production scheme still reduces equipment capacity by 6.04% and initial investment by 6.16% in the chemical hydrogen scenario with the least load fluctuation.

Model-based approach to formalizing a production system architecture

An approach to developing a set of related system models that forms the boundaries of the subject area, a hierarchical decomposition of the target system itself and its formalized description, called Model-based System Engineering, is considered. This approach is one of the basic systems engineering tools in the system modeling practice of complex systems. The results associated with the application of a model-based approach in complex supply chain planning systems in the electronics industry are presented. A comparative analysis of modeling languages for systems (SysML), web ontology (OWL) and the visual language of the BPMN method was carried out from the perspective of tools and a model-oriented approach. Recommendations are given on the use of tools for developing visual system models when modeling the order management process within the framework of supply chain control in the electronic equipment production. Keywords visual modeling, model-based systems engineering, MBSE, business process modeling, BPMN, SysML, ontology development, OWL

Production system ontology for continuous Capability-based Engineering

The mechanical engineering industry, and in particular the special machinery industry, is characterized by a high degree of complexity in the design of customized systems. This is due to the wide variation in system design, the desire for reconfigurable manufacturing systems, and cost and time pressures. For efficient and flexible planning in the early phases of the product life cycle, the relationships between Product, Processes and Resources (PPR paradigm) should be determined in order to derive the production system architecture from them. Based on the common modelling of the relationships, it is also desirable to extend the relationships between Processes and Resources. Capability-based engineering, i.e. linking Process and Resource through Capabilities, Skills (and Services) in the context of CSS modelling, further emphasizes these relationships. This paper discusses the state of the art in PPR and CSS research. In addition, we present an extension that, on the one hand, focuses more on the Product for subsequent planning steps and, on the other hand, discusses the description of capabilities in more specific terms. The paper’s objective is to simplify the Processes of production system planning, based on a model-based manufacturing system description, and accessibility to Resource catalogs for capability matching.

Robustness Evaluation Process for Scheduling under Uncertainties

Scheduling production is an important decision issue in the manufacturing domain. With the advent of the era of Industry 4.0, the basic generation of schedules becomes no longer sufficient to face the new constraints of flexibility and agility that characterize the new architecture of production systems. In this context, schedules must take into account an increasingly disrupted environment while maintaining a good performance level. This paper contributes to the identified field of smart manufacturing scheduling by proposing a complete process for assessing the robustness of schedule solutions: i.e., its ability to resist to uncertainties. This process focuses on helping the decision maker in choosing the best scheduling strategy to be implemented. It aims at considering the impact of uncertainties on the robustness performance of predictive schedules. Moreover, it is assumed that data upcoming from connected workshops are available, such that uncertainties can be identified and modelled by stochastic variables This process is supported by stochastic timed automata for modelling these uncertainties. The proposed approach is thus based on Stochastic Discrete Event Systems models and model checking techniques defining a highly reusable and modular process. The solution process is illustrated on an academic example and its performance (generecity and scalability) are deeply evaluated using statistical analysis. The proposed application of the evaluation process is based on the technological opportunities offered by the Industry 4.0.

Architecture and knowledge modelling for self-organized reconfiguration management of cyber-physical production systems

ABSTRACT The demand for reconfigurations of production systems is increasing, driven by shorter innovation and product life cycles and economic volatility. Another trend in the domain of industrial automation is the emergence of cyber-physical production systems, which offer promising potentials, for example, self-organization capabilities. A suitable cyber-physical production system architecture that incorporates knowledge modelling and management concerns, plus a reconfiguration management methodology, is crucial for realizing self-organized reconfiguration management. In this paper, first reference architectures, architectural patterns, and basic principles, as well as knowledge modelling and management approaches, are discussed in general. Afterwards, these are examined concerning the reconfiguration management use case focusing on UML/XML-based and ontology-based approaches. A novel approach comprising a multi-agent system and the MAPE-K concept for reconfiguration management is presented. In addition, the approach contains a service-oriented architecture for a deterministic plant control within a layered architecture. The knowledge modelling is realized through a UML information model, which can be integrated into the system utilizing XML files. Furthermore, the provided tool support is described. It enables a user to describe system components in an effort-reduced manner and conform to the schema defined by the information model and its restrictions via a GUI.

Codesign of Architecture, Control, and Scheduling of Modular Cyber-Physical Production Systems for Design Space Exploration

Design space exploration (DSE) of cyber-physical production systems (CPPS) is a search problem in the space of potential compositional configurations. Current design methodologies follow the separated design paradigm in which the architecture, control, and scheduling are separately designed. Optimization of each part considers only the corresponding goals of interest and overlooks other aspects by adopting gross assumptions, which makes it difficult to determine the global optimal solution for a given system. To address this problem, in this article, we propose a codesign method that considers the design spaces of architecture, control, and scheduling as monolithic, mixed discrete-continuous spaces. We formulate DSE as an optimization problem and propose a generic iterative algorithm schema involving simulation in the loop to solve the abovementioned new problem. To illustrate the effectiveness of the proposed method, a real single-stage reducer assembly production system is considered. The design results demonstrate that our method provides better solutions than does the separated design method.

Elucidating connections between the strigolactone biosynthesis pathway, flavonoid production and root system architecture in Arabidopsis thaliana.

Strigolactones (SLs) are the most recently discovered phytohormones, and their roles in root architecture and metabolism are not fully understood. Here, we investigated four MORE AXILLARY GROWTH (MAX) SL mutants in Arabidopsis thaliana, max3‐9, max4‐1, max1‐1 and max2‐1, as well as the SL receptor mutant d14‐1 and karrikin receptor mutant kai2‐2. By characterising max2‐1 and max4‐1, we found that variation in SL biosynthesis modified multiple metabolic pathways in root tissue, including that of xyloglucan, triterpenoids, fatty acids and flavonoids. The transcription of key flavonoid biosynthetic genes, including TRANSPARENT TESTA4 (TT4) and TRANSPARENT TESTA5 (TT5) was downregulated in max2 roots and seedlings, indicating that the proposed MAX2 regulation of flavonoid biosynthesis has a widespread effect. We found an enrichment of BRI1‐EMS‐SUPPRESSOR 1 (BES1) targets amongst genes specifically altered in the max2 mutant, reflecting that the regulation of flavonoid biosynthesis likely occurs through the MAX2 degradation of BES1, a key brassinosteroid‐related transcription factor. Finally, flavonoid accumulation decreased in max2‐1 roots, supporting a role for MAX2 in regulating both SL and flavonoid biosynthesis.

Context-aware scheduling and control architecture for cyber-physical production systems

Cyber-physical production systems provide a flexible and open mechanism for manufacturing process scheduling and control, and they also offer an opportunity to further improve the performance of systems by the joint optimization of scheduling and control (JSC). With given optimization objectives, the solution of JSC not only provides the schedule plan but also provides the optimal control parameters. However, due to the dynamic nature of the production system, it is not possible to consider all potential situations to make an ideal solution for the JSC at the beginning. Therefore, this paper formulates the problem of dynamic JSC and proposes an architecture for context-aware production scheduling and control systems, which utilizes ontology and reasoning technologies from knowledge engineering to enhance the adaptabilities of production systems. To illustrate the feasibility of the proposed architecture, we take an international competition platform as a case study and compare the performance with the champion team’s system. The result shows that our system performs better than does the system of the champion team, and it also proves the feasibility of the proposed architecture.

Intelligent Digital Twin in Health Sector: Realization of a Software-Service for Requirements- and Model-based-Systems-Engineering

The concept of the intelligent Digital Twin focuses on automatic model creation and model extension for a product throughout its lifecycle using artificial intelligence. To create an intelligent Digital Twin, the Digital Twin architecture must also include software services that support machine learning algorithms. These software services must have two characteristics. First, an interface to the real environment to dynamically receive feedback from an existing product or a product under development. Second, machine learning algorithms that analyze and manage the Digital Twin's models based on feedback from the real product. In this paper, we present the concept and implementation of a software service within the intelligent Digital Twin's architecture for automated requirements modeling as well as for modeling of the product system architecture. Since medical devices differ significantly in their applied technology, area of use, and application, the requirement engineering of medical devices needs to identify and specify the critical design requirements for each product individually. This makes automated requirement engineering and Model-based-Systems Engineering of medical devices very challenging. The implemented intelligent software service meets these challenges for product design in the healthcare sector.

Lean 4.0 Dynamic Tools for Polymeric Products Manufacturing in Industry 4.0

AbstractIn the polymer products industry, new technologies and Industry 4.0 concepts represent real solutions to increase efficiency and profitability through creating the intelligent and adaptable manufacturing system. At the same time, the existing manufacturing systems have made sustained efforts to implement Lean principles, techniques and tools to achieve process efficiency by eliminating waste and continuously improving performance. The production system architecture consists of active production resources, which participate directly in the production process flow, and passive elements, which have a supporting role. In Industry 4.0, these elements form a dynamic cyber‐physical production subsystem, through which different manufacturing solutions can be managed. Updating Lean production systems to Industry 4.0 level is obtained by implementing Lean tools and principles to the cyber‐physical system. This work presents the results of research on transforming a Lean instrument a into a Lean 4.0 solution, applicable to manufacturing cells processes, in an Industry 4.0 manufacturing system.

Research Fields and Challenges to implement Cyber-Physical Production Systems in SMEs: A Literature Review

Cyber-Physical Production Systems (CPPS) consists of the orchestration of single intelligent and connected cyber-physical systems (CPS) in order to perform what we call smart manufacturing. CPS collaborate in an intelligent way in order to obtain and maintain the optimum of the manufacturing process, handle disturbances and adapt to changing conditions. It might not be easy for small and medium-sized enterprises (SMEs) to implement such production system architectures in their shop floor. In this paper, we want to investigate existing scientific literature through a systematic literature review in order to identify the main research fields for implementing CPPS in smart SME factories. As a result, the identified research fields are critically discussed, highlighting those fields that can be identified as the most difficult challenges for SMEs in the near future and giving directions for future research activities.

A Dynamic System Model for Personalized Healthcare Delivery and Managed Individual Health Outcomes

The current healthcare system is facing an unprecedented chronic disease burden. This paper develops a healthcare dynamic model for personalized healthcare delivery and managed individual health outcomes. It utilizes a hetero-functional graph theory rooted in Axiomatic Design for Large Flexible Engineering Systems and Petri nets. The dynamics of the model builds upon a recently developed systems architecture for healthcare delivery which bears several analogies to the architecture of mass-customized production systems. At its essence, the model consists of two synchronized Petri nets; one for the healthcare delivery system and another for individuals' health state evolution. The model is demonstrated on two clinical case studies; one acute and another chronic. Together, the case studies show that the model applies equally to the care of both acute and chronic conditions, transparently describes health outcomes and links them to the evolution of the healthcare delivery system and its associated costs.

BDCPS — A Framework for Smart Manufacturing Systems using Blockchain Technology

Blockchain is going to be the most fundamental technology, and will change the world — going forward. In fact, the revolution has already begun. The birth of Industry 4.0 aka the Fourth Industrial Relution (I4.0), has created a need for autonomous and integrated, secure manufacturing systems. The current smart systems lack the decentralized decision making and real-time communication infrastructure, which is a condition for adaptive, smart manufacturing systems. In this paper, an autonomous, secure and collaborative platform based on Blockchain technology, is presented to adapt to such results. In support with Internet of Things (IoT) and cloud services, a Blockchain Driven Cyber Physical Production System (BDCPS) architecture is designed to communicate with machines, users, devices, suppliers and other peers. Using the Smart Contracts feature and trust-less peer-to-peer decentralized ledger feature, BDCPS will validate the claim with a small-scale real-life Blockchain with IoT system. This implementation case study will be running a private Blockchain on a single board computer, and bridged to a microcontroller containing IoT sensors. The applications of this system in automotive manufacturing industry are presented, to proceed towards Industry 4.0.

An integrated optimization of the floating and subsea layouts

The paper proposes a mathematical model to integrate the optimization of the offshore production system. The proposed Mixed Integer Linear Programming Model can be implemented as a tool to support the decision makers in the conceptual design phase of the oilfield development to determine the most feasible production system architecture. The model defines the number, capacity and location of platforms and manifolds, as well as, the optimal assignment of wells to these facilities, while searching for the minimum global investment costs. The mathematical tool trades off the minimization of the pipeline length, the expansion of the platform capacity, as well as the number of installed manifolds and platforms. One of the main benefits of the model is do not be closed into one subsea production layout defined into the literature, being able to design the optimal layout for each instance. Moreover, the establishment of a properly floating structures of each oilfield during the development plan, allows the early coupled dynamics analysis of the platform/mooring/risers concepts. Other than the model verification, a case study scenario is performed and discussed to evidence the relevance and the computational performance of the solution provided by the model.

A novel fluid architecture for cyber-physical production systems

ABSTRACTCloud computing has revolutionised the conceptualisation of IT environments in most fields of the economy. Nevertheless, it still requires adequate pre-requisites both in the business strategy and the computational architecture to be adopted successfully and profitably. This is especially true in the context of manufacturing where production services are very sensitive with respect to their distribution and localisation within the boundaries of the computing constellation. The paper aims at clarifying the concept and the application of Fluid Computing in Cyber-Physical Productions Systems, connecting the idea of computational sedimentation to the novel Fluid Manufacturing Architecture (FMA). The FMA encompasses the computing layers of cloud, fog, edge and mist, and extends them with a new one called Dew. The Dew represents the entry point where manufacturing legacy devices are converted into network-able, embedded components for the distributed production scenarios of Industry 4.0. The application of the FMA in a pilot factory has been investigated. The preliminary results of an agent-based simulation case-study for collaborative manufacturing services within the FMA are finally presented.

Resilient architecture for cyber-physical production systems

Production systems are a typical example of cyber-physical systems (CPS) in which a variety of machines, actuators, sensors and control systems are interwoven to produce products as efficiently as possible. Even though sophisticated condition monitoring systems are deployed, stoppage, breaks, and other types of failures still happen. To avoid catastrophic operational disruptions, desirably the production system itself resiliently and autonomously responses to failures. This paper reports a design method for a resilient architecture of a cyber-physical production system that can deal with disturbances and failures in a discrete-event process. A physical demonstrator was built to demonstrate its reconfiguration capabilities.

Modelling production system architectures in the early phases of product development

This article suggests a framework for modelling a production system architecture in the early phases of product development. The challenge in these phases is that the products to be produced are not completely defined and yet decisions need to be made early in the process on what investments are needed and appropriate to enable determination of obtainable product quality. In order to meet this challenge, it is suggested that a visual modelling framework be adopted that clarifies which product and production features are known at a specific time of the project and which features will be worked on – leading to an improved basis for prioritizing activities in the project. Requirements for the contents of the framework are presented, and literature on production and system models is reviewed. The production system architecture modelling framework is founded on methods and approaches in literature and adjusted to fit the modelling requirements of a production system architecture at an early phase of development. The production system architecture models capture and describe the structure, capabilities and expansions of the production system architecture under development. The production system architecture modelling framework is tested in a case study, and the results indicate that the modelling process facilitates identification of critical factors of the production system architecture, that the production system architecture models capture and describe the structure, capabilities and expansions of a production system architecture under development, and that the production system architecture models can facilitate dialogue on the production system architecture between heterogeneous stakeholder groups.

BIOSOARM: a bio-inspired self-organising architecture for manufacturing cyber-physical shopfloors

Biological collective systems have been an important source of inspiration for the design of production systems, due to their intrinsic characteristics. In this sense, several high level engineering design principles have been distilled and proposed on a wide number of reference system architectures for production systems. However, the application of bio-inspired concepts is often lost due to design and implementation choices or are simply used as heuristic approaches that solve specific hard optimization problems. This paper proposes a bio-inspired reference architecture for production systems, focused on highly dynamic environments, denominated BIO-inspired Self-Organising Architecture for Manufacturing (BIOSOARM). BIOSOARM aims to strictly adhere to bio-inspired principles. For this purpose, both shopfloor components and product parts are individualized and extended into the virtual environment as fully decoupled autonomous entities, where they interact and cooperate towards the emergence of a self-organising behaviour that leads to the emergence of the necessary production flows. BIOSOARM therefore introduces a fundamentally novel approach to production that decouples the system’s operation from eventual changes, uncertainty or even critical failures, while simultaneously ensures the performance levels and simplifies the deployment and reconfiguration procedures. BIOSOARM was tested into both flow-line and “job shop”-like scenarios to prove its applicability, robustness and performance, both under normal and highly dynamic conditions.

Design of a decentralized modular architecture for flexible and extensible production systems

In the European manufacturing industry, production batches are decreasing, resulting in an increase in required changeovers. Companies are pushed to respond quickly and cost-efficient to changing markets. One way for Small and Medium sized Enterprises (SMEs) to become more agile, is to incorporate industrial robots in their production processes; larger enterprises already use this versatile piece of equipment for large batch sizes. To do so, a new perspective and approach is required, tailored to dynamic manufacturing systems in which production systems and components can be easily reconfigured, altered, swapped or replaced. A modular system architecture was developed within project SInBot upon which challenges in dynamic cooperation between robots and humans were projected. The developed system uses decentralized control and distributed intelligence, linked through an extensible, flexible, and fault-tolerant communication architecture. The rapidly growing complexity of traditional systems is compared to its decentralized counterpart to illustrate some of the many advantages of this new system architecture. A manufacturing scenario analogue is discussed and the approach to verify the performance of inner-system components, as well as the benefits of this approach. Key challenges that are encountered in implementing the cooperation into the current industrial environments are identified and projected upon the verification system.