Networked Manufacturing Resources Research Articles

Feature-based function block control framework for manufacturing equipment in cloud environments

The ability to adaptively control manufacturing equipment in cloud environments is becoming increasingly more important. Industry 4.0, supported by Cyber Physical Systems and the concept of on-demand, scalable and pay-for-usage resource-sharing in cloud environments offers many promises regarding effective and flexible manufacturing. For implementing the concept of manufacturing services in a cloud environment, a cloud control approach for the sharing and control of networked manufacturing resources is required. This paper presents a cloud service-based control approach which has a product perspective and builds on the combination of event-driven IEC 61499 Function Blocks and product manufacturing features. Distributed control is realised through the use of a networked control structure of such Function Blocks as decision modules, enabling an adaptive run-time behaviour. The control approach has been developed and implemented as prototype systems for both local and distributed manufacturing scenarios, in both real and virtual applications. An application scenario is presented to demonstrate the applicability of the control approach. In this scenario, Assembly Feature-Function Blocks for adaptive control of robotic assembly tasks have been used.

A Grid-based Scheduling Method of Manufacturing Resources for Networked Manufacturing

To optimize allocation of the networked manufacturing resources and improve the networked manufacturing system’s productivity, one method of manufacturing resources scheduling was proposed based on routing and forwarding algorithm grid-based. At the same time, networked manufacturing mode manufacturing resources optimizing framework grid-based and scheduling architecture grid-based were established to analyze the mechanism of manufacturing resources scheduling. Then, a grid-based scheduling model of manufacturing resources for networked manufacturing was constructed by means of open loop and closed loop queuing network methods. Finally, the least task priority and the minimum expected waiting time optimizing strategies were proposed to realize manufacturing resources scheduling. The method can provide a new approach to enhance the efficiency of networked manufacturing process, and extend the knowledge of manufacturing resources optimizing and heighten the automation level of networked manufacturing process.

Feature-based control and information framework for adaptive and distributed manufacturing in cyber physical systems

Modern distributed manufacturing within Industry 4.0, supported by Cyber Physical Systems (CPSs), offers many promising capabilities regarding effective and flexible manufacturing, but there remain many challenges which may hinder its exploitation fully. One major issue is how to automatically control manufacturing equipment, e.g. industrial robots and CNC-machines, in an adaptive and effective manner. For collaborative sharing and use of distributed and networked manufacturing resources, a coherent, standardised approach for systemised planning and control at different manufacturing system levels and locations is a paramount prerequisite.In this paper, the concept of feature-based manufacturing for adaptive equipment control and resource-task matching in distributed and collaborative CPS manufacturing environments is presented. The concept has a product perspective and builds on the combination of product manufacturing features and event-driven Function Blocks (FB) of the IEC 61499 standard. Distributed control is realised through the use of networked and smart FB decision modules, enabling the performance of collaborative run-time manufacturing activities according to actual manufacturing conditions. A feature-based information framework supporting the matching of manufacturing resources and tasks, as well as the feature-FB control concept, and a demonstration with a cyber-physical robot application, are presented.

Manufacturing Resource Ontology Modeling Techniques Based on Carrier

Aimed at The key problem for manufacturing resource integration and retrieval of the networked manufacturing resources sharing and reuse, by analyzing the characteristics of the enterprise manufacturing resource and ontology,OWL ontology modeling method based on manufacturing resource carrier was proposed,manufacturing resource was integrated and index by modules; so, manufacturing resource was network reused and shared by carrier was evaluated and index, product development time was reduced, competitiveness was enhance. Finally, wrapper module of paper packaging machine as an example to proves the feasibility of the method.

A Hybrid GA-TS Algorithm for Optimizing Networked Manufacturing Resources Configuration

In recent years, more and more researchers have focused on various significant issues of resource configuration, such as maximum computing performance and minimum response time, especially taking existing resource utilization into account. The goal of this research is to design a resource configuration optimization system u nder networked manufacturing environment. A prediction mechanism is realized by using support vector regression (SVR) to estimate resource utilization according to network protocol of each manufacturing process, while redistributing resources based on the current status of existing resources pool. A resource configuration mechanism applying genetic algorithm working along with the tabu search process (GA-TS) is proposed in this study to determine the redistribution of resources. The experimental results show that the pro posed scheme achieves an effective configuration via reaching the balance between the utilization of resources within existing resources an d network protocol of each manufacturing process between potential resource requirements and the network resource providers.

Dynamic Evaluation Model of Networked Manufacturing Resources Based on Grey Target Decision

Dynamic Evaluation Model of Networked Manufacturing Resources Based on Grey Target Decision

Research on Strategies of Networked Manufacturing Resources Configuration Based on Evolutionary Game

Economic theories of managing resources, traditionally assume that individuals are perfectly rational and thus able to compute the optimal configuration strategy that maximizes their profits. The current paper presents an alternative approach based on bounded rationality and evolutionary mechanisms. It is assumed that network node users face a choice between two resource strategies in real networked manufacturing resources configuration problem (NMRCP). The evolution of the distribution of strategies in the population is modeled through a replicator dynamics equation. The latter captures the idea that strategies yielding above average profits are more demanded than strategies yielding below average profits, so that the first type ends up accounting for a larger part in the population. From a mathematical perspective, the combination of resource and evolutionary processes leads to complex dynamics. The paper presents the existence and stability conditions for each steady-state of the system. A main result of the paper is that under certain conditions both strategies can survive in the long-run.

Manufacturing Resources Optimization Deployment for Complicated Parts Based on Network

Agility of physical manufacturing unit is the competitive advantage in the global manufacturing environment. It is believed that the agility can be realized by dynamically optimization deployment of networked manufacturing resources. To solve this problem, logical manufacturing unit (LMU) and logical manufacturing process (LMP) are proposed and defined to decompose and model networked manufacturing task according to the process of complex part. When selecting manufacturing resources for these manufacturing tasks, many factors should be taken into account. However, manufacturing cost, time to market and manufacturing quality are the most important factors. In this paper, networked manufacturing resources pre-deployment is carried out to find candidate manufacturing resources based on manufacturing resources abilities, such as part family, geometric feature, material type, rough type, dimension range, machining method, precision grade and production type. Then, taking transportation time and cost besides manufacturing time, cost and quality into consideration, the objectives and restrictions of manufacturing resources optimization deployment are analyzed, and manufacturing resources optimization deployment problem is considered as a multi-objectives optimization problem.

Characteristics of Networked Manufacturing Resources

Along with the networked manufacturing technology development and deep research of resources classification, the question of resource management appears prominently. Resources attribute constitution has been analyzed under the network manufacture environment from the resources classification management; the resources attribute and the data exchange form have been proposed under the isomerism environment

Research on resources optimisation deployment model and algorithm for collaborative manufacturing process

Agility is the competitive advantage in the global manufacturing environment. It is believed that agility can be realised by networked manufacturing resource optimisation deployment. However, this is a challenge to us now. To solve this question, logical manufacturing unit and logical manufacturing process are proposed to decompose and model the networked manufacturing task, and networked manufacturing resources are organised and modelled based on physical manufacturing unit. During the deployment of manufacturing resources to the task, many factors should be taken into consideration. Of these, manufacturing cost, time and quality are the most important factors. In this paper, before these factors are considered, networked manufacturing resources pre-deployment is carried out to find the candidate manufacturing resources on the basis of manufacturing abilities. Then, resources optimisation deployment is modelled as a multi-objectives optimisation. This optimisation problem is solved based on genetic algorithm after transforming the multi-objectives optimisation problem to a single objectives optimisation problem. Although we may not find the optimal solution for the problem by genetic algorithm, the better and feasible solution is produced. Thus, this algorithm is efficient and can be applicable to practical problem. At last, an illustrative example is presented to show the application of the proposed algorithm.