Collaborative Manufacturing System Research Articles

Federated deep reinforcement learning for dynamic job scheduling in cloud-edge collaborative manufacturing systems

The cloud-edge collaborative manufacturing system (CCMS) connects distributed factories to a cloud centre through cloud-edge collaborative communication, introducing both opportunities and challenges to conventional dynamic job scheduling. Enhancing each factory's scheduling performance by sharing general scheduling knowledge among heterogeneous factories under the consideration of data privacy protection remains challenging. To this end, this paper proposes to solve the dynamic job scheduling in the context of CCMS with a novel federated deep reinforcement learning (FDRL) approach. Within each factory, the scheduling objective involves minimising the makespan and energy consumption, accounting for machine warm-up procedures and real-time dynamics. To handle heterogeneous policy structures, we aggregate their hidden parameters through FDRL, with states, actions, and rewards designed to facilitate the aggregation. The two-phase algorithm, comprising iterative local training and global aggregation, trains the scheduling policies. Constraint items are introduced to the loss functions to smooth local training, and the global aggregation considers production scales and obtained objectives. The proposed approach enhances the solution quality and generalisation of each factory's scheduling policy without exposing original production data. Numerical experiments conducted on sixty scheduling instances validate the superiority of the proposed approach compared to twelve dynamic scheduling methods. Compared to independently trained DRL-based approaches, the proposed FDRL-based approach achieves up to an 8.9% reduction in makespan and a 22.3% decrease in energy consumption through knowledge sharing.

Industry 5.0 and sustainable manufacturing: a systematic literature review

PurposeIndustry 5.0 (I5.0) is eventually set to supersede Industry 4.0 (I4.0), despite the fact that I4.0 continues to gain ground in emerging nations like India. Now India is aspiring to be a global manufacturing hub, and I5.0 offers enormous potential to position India as a forerunner in intelligent and collaborative manufacturing systems. Therefore, this research article aims to understand the relationship between I5.0 and sustainable manufacturing (SM) thoroughly; pinpoint its impact and implementation challenges; analyze its impact on Triple-Bottom-Line (TBL) sustainability; and present an inclusive framework for I5.0 implementation for Indian manufacturing enterprises.Design/methodology/approachThe coexistence of two industrial revolutions raises questions, which necessitates debates and explanations. Thus, the systematic literature review (SLR) approach is used to address this issue and this study used Web of Science, Scopus, Science Direct and Google Scholar databases. Following a critical SLR, 82 research papers have been cited in this article, and the majority of cited articles were published from 2010 to 2022, to ensure a focused analysis of pertinent and recent scholarly contributions.FindingsI4.0 is considered to be technology-driven, however, I5.0 is perceived to be value-driven. I5.0 is not a replacement or a chronological continuation of the I4.0 paradigm. The notion of I5.0 offers a distinct perspective and emphasizes the necessity of research on SM within the TBL sustainability boundaries. I5.0 introduces a new TBL: resilience in value creation, human well-being and sustainable society. Indeed, I5.0 seems to be economically, socially, and environmentally sustainable while manufacturing products with high productivity.Practical implicationsTheoretical implications pertain to restructuring business models and workforce transformation, whereas practical implications underscore the significance for manufacturing enterprises to embrace I5.0 for their sustainable development. By understanding the nuanced relationship between I5.0 and SM, enterprises can navigate implementation challenges, maximize TBL sustainability and embrace an inclusive I5.0 framework for high productivity and resilience.Originality/valueThe existing literature presents the general notion of I5.0 but lacks in-depth TBL sustainability analysis. This research used a systematic and rigorous SLR approach that evaluates the existing literature, enables an in-depth understanding, identifies research gaps and provides evidence-based recommendations for the decision-making process. Furthermore, this research aims to stand on an unbiased assessment, exploring theoretical and practical implications of I5.0 implementation for manufacturing enterprises and suggesting future research avenues.

STEP-NC enabled edge–cloud collaborative manufacturing system for compliant CNC machining

In recent years, edge–cloud collaborative manufacturing systems have been applied to achieve accurate data tracking and intelligent machining analysis in the field of CNC machining. However, due to the lack of other dimensional tags, such as workstep and speed, which are related to process data, the current systems are still at a level of processing time-tag machining data. It is of great significance to address the problem of linking actual machining data with other dimensional tags, which can achieve more accurate and intelligent data interaction between design and manufacturing. This paper proposes an edge–cloud collaborative manufacturing system based on STEP-NC, which enables the exchange of machining data tagged by workstep, improves the accuracy of data traceability, and lays the foundation for more intelligent processing analysis. Especially, a method of dynamic task delivery and data subscription according to the environment is proposed to improve the manufacturing system’s two-way data flow interaction ability. On the basis of that, a manufacturing information segmentation method based on workstep is proposed, which makes it easier to sort information from the coupled data. In addition, the system is compatible with heterogeneous CNC machine tools to achieve large-scale industrial applications. Finally, a prototype system has been installed in a workshop located in COMAC, and corresponding experiments are carried out on the CNC machine tool for COMAC parts manufacturing, which verifies the feasibility of the proposed method.

Large-scale hybrid task scheduling in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution

Large-scale hybrid task scheduling in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution

Optimal layout planning for human robot collaborative assembly systems and visualization through immersive technologies

The deployment of Industry 4.0 emerging technologies such as Augmented reality (AR), Virtual reality (VR), and collaborative Robots enhances flexibility and precision in the manufacturing systems. A flexible, and collaborative manufacturing system with robots and human integrates the prominent attributes of customized automation and fortify the safety of the human workforce. The proposed work involves the approach of AR-assisted assembly layout configurations for the Human-Robot Collaboration (HRC) production system, which is a typical example of the smart manufacturing system. The reconfiguration/configuration of the assembly layout is triggered with respect to the demand of new variants into the market and the upgradation of new technologies in the existing system. A multi-objective algorithm is proposed to maximize the production outlet with effective utilization of floor space, minimum idle time and optimum resource allocation. The selected work is branched into three phases: To begin with, the approach of task allocation is constructed to identify the suitable resources (human, robot) for the appropriate assembly tasks in the HRC system. The outcomes such as resource allocation, number of resources, and footprint of resources are considered as input to the optimization model. Further, modified particle swarm optimization (MPSO) is deployed to generate a feasible assembly layout for the HRC manufacturing system. Additionally, the breath-first approach has been utilized to generate the optimal aisle as a conveyor in the layout. Notably, the comparisons studies have been carried out in this work, which facilitates to explore the robustness of proposed modified PSO algorithms for the selected applications. Subsequently, the virtual and AR tool is deployed for the layout planer to explore and validate the obtained layout from the PSO model in both the virtual and physical environment of the industrial workspace. Finally, an industrial case study of rotary gear pump assembly system is adopted for the implementation of the proposed framework.

An interactive and integrated framework for collaborative product development in cloud manufacturing using STEP standard-based ontology model.

Due to the growing competition of firms in manufacturing industries, the enhancement of the design of new products for quick and collaborative interaction of stakeholders is inevitable. A customer-oriented approach during the enhancing of product design requires the support of various stakeholders' collaboration from their functionality perspectives. Accordingly, manufacturing firms try to increase the product quality through the continuous collaboration and speeding up of the design process. Moreover, new manufacturing paradigms like cloud manufacturing require global and distributed collaboration of stakeholders. One competitive key necessity for firms is the integration of stakeholders in the design and production stages. This article has proposed an interactive and integrated framework for a cloud collaborative manufacturing system. The proposed framework uses the international STEP standard which ensures data integrity in the whole product development processes. The framework enables stakeholders to work collaboratively and interactively in their disciplines, while it also ensures coherency of modifications to avoid conflicts. The proposed model is introduced using an improvisational design method based on the STEP standard to create an interactive platform with a service-oriented approach and supports stakeholders for their own semantics. In addition to examining the needs of an interactive system, the article provides an architecture for implementation of this perspective. A comprehensive case study is designed to show the capabilities of the proposed framework.

Hybrid flow-shop scheduling in collaborative manufacturing with a multi-crossover-operator genetic algorithm

Collaborative manufacturing systems have become a key component of Industry 4.0, supported by Industrial Information Integration Engineering (IIIE) applications. Efficient scheduling and coordination of manufacturing tasks are vital for these systems, directly impacting factory operational efficiency. One of the most prominent optimization problems in collaborative manufacturing is the hybrid flow shop scheduling problem with multiprocessor task (HFSPMT). This study proposes an improved genetic algorithm with multi-crossover-operator, called MCO-GA. MCO-GA introduces a novel crossover operator named SX, which demonstrates superior convergence efficiency compared to classical crossover operators. Furthermore,MCO-GA utilizes a probability selection method to autonomously choose between classical crossover operators and SX during the crossover stage. The effectiveness of MCO-GA is demonstrated through its successful application in solving a scheduling problem in a real-life wood manufacturing factory. Comparing MCO-GA with the state-of-the-art metaheuristic algorithms (HSA, OBL_HSA, and MGLS), it is observed that MCO-GA achieves significantly better average results in over 60% of instances. Additionally, MCO-GA outperforms OBL_HSA and MGLS in terms of computation time. These results highlight the effectiveness and efficiency of MCO-GA in solving the HFSPMT problem and its potential for improving scheduling and coordination in collaborative manufacturing systems.

A complexity assessment framework with structure entropy for a cloud‐edge collaborative manufacturing system

AbstractThe Industrial Internet of Things (IIoT), along with 5G and beyond networks, is driving a new era of revolution in intelligent manufacturing. However, the integration of more heterogeneous entities and intricate communication protocols complicates the enhanced manufacturing system, posing challenges for quantitatively assessing its complexity. To tackle this issue, a complexity assessment framework for the IIoT‐enabled collaborative manufacturing system is proposed by combining the complex network and information entropy theory. Firstly, industrial entities in the physical space are mapped into a two‐tier complex network taking into account the weights of various access communications. Secondly, an importance‐aware structure entropy is introduced to capture the complexity of industrial networks from the communication perspective in the system. The experiments conducted on various network topological structures validate the proposed method and provide guidance for system design.

Multi-objective collaborative assembly line design problem with the optimisation of ergonomics and economics

Manufacturing systems are socio-technical systems, with explicit interactions between humans and technologies in shared workspaces. These shared workspaces could also be called hybrid collaborative manufacturing systems, which involve workers as well as technological equipment and combine the benefits of human workers and new Industry 4.0 technologies, such systems are particularly useful in a context requiring flexibility and adaptability. Furthermore, the new Industry 5.0 approach has the objective to shift toward more human-centric and resilient manufacturing systems. The key problems to solve in the design of collaborative manufacturing systems are the combinatorial assembly line balancing problem and the equipment selection problem. An efficient and sustainable line requires a cost-effective choice of equipment while improving the ergonomics and the safety of workers. Both decisions of balancing workload and the assignment of equipment impact the ergonomics of a collaborative system and present conflicting criteria. To this end, we propose a multi-objective approach, the objectives are the optimisation of the investment costs and the ergonomics with a fatigue and recovery criterion. We propose to linearise the fatigue and recovery to formulate a new Mixed Integer Linear Programming formulation. We developed an exact multi-objective solving algorithm based on the ϵ-constraint to obtain the trade-off between these objectives. We conducted numerical experiments with different instances from the literature with promising results for instances with up to 45 operations. Finally, we discuss insightful managerial conclusions and future research perspectives.

A fusion-based spiking neural network approach for predicting collaboration request in human-robot collaboration

• Compared with the traditional assembly method, a fusion-based spiking neural network approach for predicting collaboration request proposed in this paper has the following advantages. • A human collaboration request prediction method under the joint influence of multi-channel information is proposed. • The FSNN method for asynchronous monitoring is proposed to reduce the workload of non-time-series feature detection. • The coding methods of different input information, and the threshold adaptive updating method under the influence of different neurons are proposed. • Compared with different prediction methods, the superiority of the FSNN scheme is proved. In human-robot collaborative (HRC) manufacturing systems, how the collaborative robots engage in the collaborative tasks and complete the corresponding work in a timely manner according to the actual state has been a critical factor that hinders the efficiency of HRC. Inappropriate collaborative behaviors will result in a poor perceptual experience for human operators (e.g., robots starting action too early or too late). To address this issue, a fusion-based spiking neural networks (FSNNs) approach for collaboration request prediction is proposed, aiming to find the right collaboration timing for robots in HRC assembly system and to minimize human aversion without affecting human operation behaviors. By encoding human behavior, product state and robot pose into spiking signals that can be processed by FSNNs, the spatio-temporal coupling relationship between those three aspects can be comprehensively analyzed, and to solve the appropriate timing of robot participation in collaboration. Finally, demonstrative experiments are carried out on the HRC assembly of generator end caps in the lab environment. Compared with the baseline methods, the decision accuracy of the proposed one is improved by nearly 30%, which further proves its effectiveness.

AR-assisted digital twin-enabled robot collaborative manufacturing system with human-in-the-loop

The teleoperation and coordination of multiple industrial robots play an important role in today’s industrial internet-based collaborative manufacturing systems. The user-friendly teleoperation approach allows operators from different manufacturing domains to reduce redundant learning costs and intuitively control the robot in advance. Nevertheless, only a few preliminary works have been introduced very recently, let alone its effective implementation in the manufacturing scenarios. To address the gap, this research proposes a novel multi-robot collaborative manufacturing system with human-in-the-loop control by leveraging the cutting-edge augmented reality (AR) and digital twin (DT) techniques. In the proposed system, the DTs of industrial robots are firstly mapped to physical robots and visualize them in the AR glasses. Meanwhile, a multi-robot communication mechanism is designed and implemented, to synchronize the state of robots in the twin. Moreover, a reinforcement learning algorithm is integrated into the robot motion planning to replace the conventional kinematics-based robot movement with corresponding target positions. Finally, three interactive AR-assisted DT modes, including real-time motion control, planned motion control, and robot monitoring mode are generated, which can be readily switched by human operators. Two experimental studies are conducted on (1) a single robot with a commonly used peg-in-hole experiment, and (2) the motion planning of multi-robot collaborative tasks, respectively. From the experimental results, it can be found that the proposed system can well handle the multi-robot teleoperation tasks with high efficiency and owns great potentials to be adopted in other complicated manufacturing scenarios in the near future.

Robust scheduling and dispatching rules for high-mix collaborative manufacturing systems

Motivated by the increasing demand of mass customisation in production systems, this paper proposes a robust and adaptive scheduling and dispatching method for high-mix human-robot collaborative manufacturing facilities. Scheduling and dispatching rules are derived to optimally track the desired production within the mix, while handling uncertainty in job processing times. The sequencing policy is dynamically adjusted by online forecasting the throughput of the facility as a function of the scheduling and dispatching rules. Numerical verification experiments confirm the possibility to accurately track highly variable production requests, despite the uncertainty of the system.

Sigurnost 4.0 za kolaborativnu robotiku u fabrikama budućnosti

Technology changes present a constant drive for evolvement in the manufacturing industry. This development has brought about a complete change in the way the industry implements technologies. The complexity of state-of-the-art technologies is on the increase as new and unforeseen perils continue to emerge. One of the main challenges being faced is the adaptation of manufacturing systems to the latest safety and security considerations. The research hypothesis being investigated is that a logically structured procedure incorporating safety and security would be able to assist in designing an ergonomic and collaborative manufacturing system while identifying and analysing risks, eventually establishing feasible solutions for these specific burdens. This paper therefore contributes a methodology which was developed to address issues of safety and security in the design and implementation of cyber-physical production systems in collaborative environments.

Modeling and Analyzing Supporting Systems for Smart Manufacturing Systems with Stochastic, Technical and Economic Dependences

Smart manufacturing systems are triggerring the next industrial revolution.They are intended to be collaborative manufacturing systems that respond in real time to meet the system’s changing demands and conditions. Different types of dependencies among system components are introduced to enable this and to improve system performance, including structural, stochastic, technical and economic dependences. Supporting systems are also introduced to this aim, through specified interfaces. In this paper, the role of maintenance policy, spare part inventory and buffer size as supporting systems of smart systems is considered. Load-sharing dependence, adaptive control with feedback and economic dependence are specifically considered, and their effect is studied via Monte Carlo simulation. Results show that smart systems with properly designed supporting systems have undoubtedly increased system complexity and dependencies, but can indeed increase availability and production volume, and system efficiency overall, with total cost reduced.

Blockchain-Based Trust Mechanism for IoT-Based Smart Manufacturing System

Integrated and collaborative manufacturing system develops as massive data are obtained by the Internet of Things (IoT) technology. However, the “trust tax” imposed on manufacturers during their countless collaborations with customers, suppliers, distributors, governments, service providers, and other manufacturers is very high. Blockchain is an emerging technology that can lead to more transparent, secure, and efficient transactions. It represents a new paradigm, as well as new thinking, of how data can be securely stored, integrated, and communicated among different stakeholders, organizations, and systems that unnecessarily trust each other. Blockchain is greatly useful for reducing the “trust tax,” especially beneficial for the small- and medium-sized enterprises that must tolerate much heavier trust tax than the established manufacturers. This paper investigates the blockchain-based security and trust mechanism and elaborates a particular application of blockchain for quality assurance, which is one of the strategic priorities of smart manufacturing. Data generated in a smart manufacturing process can be leveraged to retrieve material provenance, facilitate equipment management, increase transaction efficiency, and create a flexible pricing mechanism. The dairy industry is used to instantiate the value propositions of blockchain for quality assurance.

Directly 3D-printed monolithic soft robotic gripper with liquid metal microchannels for tactile sensing

One of the most valuable contributions robotics can offer is support to daily human activities, yet rigid robots often fail to comply with safety regulations in the proximity of humans. Soft robotics takes inspiration from living organisms’ ability to adapt to their environment using flexible structures. These systems have to generate mechanical forces and simultaneously sense their environment. We developed a soft gripper with integrated sensing microstructures by monolithically 3D printing the structure. The rubber gripper mimics the versatile sensing and actuation abilities of living organisms. This is done using stereolithographic printing technology, rubber material, and resistive, pressure sensitive EGaIn microchannels. Printed microscale pressure sensing cavities are filled with liquid metal and act as resistive pressure sensors. They imitate human haptic perception and provide a sensitivity of 0.5% kPa−1. Simultaneously, a soft-robotic actuator design, which is derived from pneumatic networks, delivers a force of 2.5 N with 16 kPa of actuating pressure and an average efficiency of 0.56 mW kPa−1. Monolithically 3D printed systems promise numerous advantages since the compliance matching between multi-modal capillary sensing networks and actuators enables scale production of smart soft manipulators. Potential applications include collaborative manufacturing and medical support systems such as exoskeletons.

Empowering Extreme Automation via Zero-Touch Operations and GPU Parallelization

ZTP and deployment of GPU-based computing are the new terms for empowering extreme automation. With notions associated with these terms come new challenges and opportunities, as well as a dramatic shift in the landscape of programming the future collaborative manufacturing systems. We have only touched the surface of this exciting topic.

An event-driven integrative framework enabling information notification among manufacturing resources

ABSTRACT Industry 4.0 paradigm envisions a new generation of collaborative manufacturing system where all the components are connected exploiting the Industrial Internet of Things (IIoT) protocol. Through this pervasive connection, sensors, machines, robots and other production equipment can communicate and share information with their surroundings resources. In order to contribute to realise such a vision, this paper introduces an event-driven framework that enables the interaction of heterogeneous distributed resources, according to the principles of Industry 4.0 paradigm. The overall aim of this research is providing new mechanisms to notify significant information produced within the factory towards enabled and interested production resources. In particular, combining various technologies such as IIoT, Semantic Web, and Multi-Agent based systems, the proposed framework allows the resources to be updated about changes occurred in their context. Under these conditions, the framework can be exploited in all the scenarios where the cooperation among resources is a strict requirement, thus contributing to support various ad-hoc services that drive modern factories towards a more sustainable, efficient, and competitive manufacturing system. Moreover, the validity of the framework is demonstrated leveraging the implementation of its instance within a real case study.

Context-sensitive reconfiguration of collaborative manufacturing systems

Abstract To stay competitive in a highly dynamic environment, manufacturing companies have to quickly react to changing customer demands. Manufacturing systems may only serve demands that are covered by their capabilities. The manufacturability of a product can be analyzed by comparing the provided capabilities against the product’s requirements. The manufacturing capabilities depend on the current system and subsystem configuration. If a product cannot be manufactured, first, it must be analyzed whether any valid configuration exists that provides the required capabilities, and second, the system has to be reconfigured according to the new configuration. To the best of our knowledge no existing method exists that enables these previously mentioned steps. In this paper, we introduce a method for context-sensitive reconfiguration of multiple collaborating manufacturing systems that might come from different vendors to create a customized product on demand. We utilize variability models to capture the possible configuration space and describe influences of product properties on features of the variability model by providing a context-sensitive variability model. We apply our method to a demonstrator and show that we enable modeling of reconfigurable manufacturing systems that are automatically reconfigured on context change.

Using Cyber PLC to Link Physical Operations with Cyber Control Decisions

The Manufacturing Control System (MCS) is the control core of modern automated manufacturing systems. As today’s devices become more automated, expectations for intelligent MCS (iMCS) are growing. Traditional MCS architectures based on rigid supervisory control and data acquisition (SCADA), such as MT-Link, face difficulties in meeting the expectations. The iMCS is considered the plant’s control system and can handle dynamic events occurring in the manufacturing system. With iMCS, manufacturing systems are flexible, powerful and adaptable to the interferences.This study introduces the architecture and application of iMCS. Within them, in addition to the Cyber PLC, edge computing, fog computing and cloud computing are the backbone. By taking advantage of the four complementary technologies, today’s physical PLCs can be replaced by Cyber PLC. With Cyber PLC, there is no need to translate the messages of different protocols from equipment. Data loss, overcrowded network transmissions, and exchanges on multiple databases can also be improved in the distributed collaborative manufacturing systems. With Cyber PLC, it is expected that the traditional dispersed physical PLC control elements over factories will no longer needed. Maintenance and management of MCS will be much improved.