As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

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Digital twin-based sustainable intelligent manufacturing: a review

Digital servitization in manufacturing: A systematic literature review and research agenda

For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand man ...

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2018.1449978

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Cloud manufacturing is an emerging service-oriented business model that integrates distributed manufacturing resources, transforms them into manufacturing services, and manages the services centrally. Cloud manufacturing allows multiple users to request services at the same time by submitting their requirement tasks to a cloud manufacturing platform. The centralized management and operation of manufacturing services enable cloud manufacturing to deal with multiple manufacturing tasks in parallel. An important issue with cloud manufacturing is therefore how to optimally schedule multiple manufacturing tasks to achieve better performance of a cloud manufacturing system. Task workload provides an important basis for task scheduling in cloud manufacturing. Based on this idea, we present a cloud manufacturing multi-task scheduling model that incorporates task workload modelling and a number of other essential ingredients regarding services such as service efficiency coefficient and service quantity. Then we investigate the effects of different workload-based task scheduling methods on system performance such as total completion time and service utilization. Scenarios with or without time constraints are separately investigated in detail. Results from simulation experiments indicate that scheduling larger workload tasks with a higher priority can shorten the makespan and increase service utilization without decreasing task fulfilment quality when there is no time constraint. When time constraint is involved, the above strategy enables more tasks to be successfully fulfilled within the time constraint, and task fulfilment quality also does not deteriorate. Multi-task scheduling problems in cloud manufacturing are the focus.Task inherent attributes are considered.A model for attribute-based multi-task scheduling is built.With no time constraint, scheduling large tasks with a higher priority can effectively decrease the total completion time.With time constraint, the scheduling method can make more large tasks successfully executed.

Workload-based multi-task scheduling in cloud manufacturing

Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to real-time big data analytics and cybersecurity.

/pdf/manufacturing-big-data-ecosystem-a-systematic-literature-qykba7oqcb.pdf

Manufacturing big data ecosystem: A systematic literature review

Globalization, servitization, and customization in the marketplace are changing the way manufacturing enterprises do their business. Cloud manufacturing (CMfg) offers a possibility to perform large-scale manufacturing collaboration. However, CMfg systems are immature in many aspects. Service selection and scheduling is a key issue for practical implementation of CMfg. In this paper, a service selection and scheduling model is established, with criteria TQCS (time, quality, cost, and service) being considered. A fuzzy decision-making theory is adopted to transform TQCS values into relative superiority degrees. This is different from the traditional linear weighted method in most previous research, which results in large values of non-standardization error. The four relative superiority degrees are then combined linearly into an overall objective, in which the weight coefficients are calculated through analytic hierarchy process (AHP). Afterwards, ant colony optimization (ACO) is repurposed for the established service selection and scheduling model. Meanwhile, a selection mechanism is added to ACO (now ACOS) to enhance its validity. The simulation results demonstrate the practicality of the proposed model and the effectiveness of ACOS compared with other widely used algorithms.

A TQCS-based service selection and scheduling strategy in cloud manufacturing

Aiming at the shortcomings of manufacturing enterprises, cloud manufacturing (CMfg), as a new type of service-oriented manufacturing mode, is put forward recently. Due to the rapid development of information and other high-tech technologies, CMfg excellently satisfies the development requirements of modern manufacturing and gains some popularity. As one of the key characteristic for CMfg system, the management, sharing, and scheduling of resources and tasks will directly affect the accuracy and efficiency of CMfg service platform. In order to achieve the effective management, convenient use, and reliable transactions of resources and tasks, a framework of trust evaluation system in CMfg is established and a trust evaluation model of CMfg service platform oriented to mechanical manufacturing field is proposed. In this system, the weights of six trust evaluation indexes are obtained through analytic hierarchy process (AHP) method. Meanwhile, the quantitative and update algorithm for direct trust service is designed through a discrete method. Furthermore, the recommendation trust service is extracted through cloud theory model and cloud focus evaluation method. Experiments based on virtual transaction data are conducted to verify the validity and efficiency of the proposed trust evaluation model.

Trust evaluation model of cloud manufacturing service platform

The development of new generation information technology has brought opportunities for industrial production model innovation. Especially, the cloud computing, Internet of Things, and big data technology are widespread applied in industrial fields. Based on this tendency, a service-oriented networked manufacturing model called cloud manufacturing (CM) was proposed in 2010. In order to realize this manufacturing model, one of the key technologies is how to achieve the discovery of manufacturing service which has not found a suitable solution. In this paper, a manufacturing service discovery framework based on agent is provided. The architecture consists of two parts: one is that manufacturing task agent and manufacturing service agent based on the expansion of the object model, and the other one is task and service matching process knowledge base. Furthermore, a structural matching method is proposed to implement the static parameters matching of task agent and service agent, and a multi-agent system bid mechanism is built to accomplish the dynamic parameters matching of the two agents. A simulation environment based on JADE has been properly developed. The simulation shows that the discovery method can effectively achieve the manufacturing service discovery in CM environment, which provides technical support for the cloud manufacturing service platform development.

Agent-based manufacturing service discovery method for cloud manufacturing

Cloud manufacturing (CMfg) is a new service-oriented networked manufacturing paradigm inspired by cloud computing. It provides high-efficiency and intelligent manufacturing services by organizing isolated manufacturing resources in a collaborative manner. Since the proposition of this concept in 2010, relevant research has mainly focused on theoretical frameworks of the CMfg system. However, actual applications of the machining service, which is a key part in the CMfg service platform, are hardly ever studied. In order to explore a feasible machining service mode, prime granularities of machining services are analyzed based on the current state of the manufacturing industry. Then a novel part manufacturing service combined with working procedure manufacturing service (PMS + WPMS) prime collaboration mode is proposed, followed by research of machining resource integration methods. To facilitate prospective implementations, information models of machining services are constructed using Web ontology language (OWL). The prime collaboration mode is expanded to a complete CMfg machining service platform. Furthermore, a working procedure priority-based algorithm (WPPBA) is designed for resource selection in CMfg. Finally, simulation experiments based on actual manufacturing data are conducted, in which the test results demonstrate the feasibility of the proposed service mode and the effectiveness of WPPBA compared with genetic algorithm (GA) and particle swarm optimization (PSO). This research provides essential guidance for CMfg applications.

Study on machining service modes and resource selection strategies in cloud manufacturing

Soft actuators are widely studied due to their high flexibility and adaptability. In this paper, a soft electrothermal actuator with directly printed microfilament heater was designed, fabricated, and tested. In this soft actuator, a polyimide (PI) film and a polydimethylsiloxane (PDMS) layer were selected as the two structural layers because of their distinct thermal expansion properties. Embedded between these two layers, a metallic microfilament heater that was made of low-melting-point metal alloy (i.e. Bi58/Sn42) was directly printed with custom-designed pattern using an electrohydrodynamic (EHD) printing process. The pattern of the microfilament heater and the thickness of the soft thermoelectric actuator were carefully designed towards the improved actuation capability. When an electric current was applied, the high temperature generated by the resistive heater causes large mismatch in the thermal expansion of the two structural layers, which bent the actuator. The fabricated soft actuator can achieve a maximum bending curvature of 1.0 cm−1 under an extremely low voltage about 3V. The soft actuator has been demonstrated for potential applications in soft robotics as a soft lifter and a soft gripper. At the full capability, the soft lifter provided a maximum actuation force about 3 mN, and was able to fully lift a load that was twice of its own weight. A four-finger soft gripper was designed and fabricated using four soft actuators to mimic humanoid motions and was implemented for pick-and-place operation for delicate objects.

Yang Cao

Papers

A TQCS-based service selection and scheduling strategy in cloud manufacturing

Trust evaluation model of cloud manufacturing service platform

Agent-based manufacturing service discovery method for cloud manufacturing

Study on machining service modes and resource selection strategies in cloud manufacturing

High-performance low-voltage soft electrothermal actuator with directly printed micro-heater