Cloud Manufacturing Environment Research Articles

An Improved Multi-Rules-Based ACO algorithm for FJSS Problem in Cloud Manufacturing Environment

An Improved Multi-Rules-Based ACO algorithm for FJSS Problem in Cloud Manufacturing Environment

ManuService ontology: a product data model for service-oriented business interactions in a cloud manufacturing environment

The ever-increasing distributed, networked and crowd-sourced cloud environment imposes the need of a service-oriented product data model for explicit representation of service requests in global manufacturing-service networks. The work in this paper aims to develop such a description framework for products based on semantic web technologies to facilitate the make-to-individual production strategy in a cloud manufacturing environment. A brief discussion on the requirements of a product data model in cloud manufacturing and research on product data modelling is given in the first part. A systematic ontology development methodology is then proposed and elaborated. The ontology called ManuService has been developed, consisting of all necessary concepts for description of products in a service-oriented business environment. These concepts include product specifications, quality constraints, manufacturing processes, organisation information, cost expectations, logistics requirements, and etcetera. ManuService ontology provides a module-based, reconfigurable, privacy-enhanced and standardised approach to modelling customised manufacturing service requests. An industrial case is presented to demonstrate possible applications using ManuService ontology. Comprehensive discussions are given thereafter, including a pilot application of a software package for semantic-based product design and a semantic web-based module for intelligent knowledge-based decision-making based on ManuService. ManuService forms the basis for collaborative service-oriented business interactions, intelligent and secure service provision in cloud manufacturing environment.

Research on 3D reconstruction method of human-computer interaction scene based on support vector machine in cloud manufacturing environment

In three dimensional (3D) reconstruction of human-computer interaction scene images in cloud manufacturing environment, the traditional method recovers the depth information directly for projection mapping, which reduces 3D image reconstruction accuracy. A 3D reconstruction method for human-computer interaction scene images is proposed based on support vector machine, the human-computer interaction scene image in cloud manufacturing environment is pretreated, using extremum algorithm to detect noise in human-computer interaction scene images, through multi windows filtering method to filter pixels contaminated by noise. Using dynamic scene estimation method to enhance the human-computer interaction scene images in cloud manufacturing environment. Corner features of the human-computer interaction image are inquired, and extracted feature vectors are input into support vector machine to learn, and the immune algorithm is introduced to adjust the parameters and get the outlined structure of 3D human-computer interaction scene images, to realize the image 3D reconstruction. The experimental results show that in cloud manufacturing environment, the proposed method has high accuracy on reconstruction and authenticity.

Manufacturing in the cloud: A human factors perspective

Cloud manufacturing adopts a cloud computing paradigm as the basis for delivering shared, on-demand manufacturing services. The result is customer-centric supply chains that can be configured for cost, quality, speed and customisation. While the technical capabilities required for cloud manufacturing are a current focus, there are many emerging questions relating to the impact, both positive and negative, on the people consuming or supporting cloud manufacturing services. Human factors can have a pivotal role in enabling the success and adoption of cloud manufacturing, while ensuring the safety, well-being and optimum user experience of those involved in a cloud manufacturing environment. This paper presents these issues, structured around groups of users (service providers, application providers and consumers). We also consider the issues of collaboration that are likely to arise from the manufacturing cloud. From this analysis we discuss the central role of human factors as an enabler of cloud manufacturing, and the opportunities that emerge.

Cloud Gathering Model of Manufacturing Resources Based on P2P

Under the environment of cloud manufacturing, in view of the self-organization of manufacturing resources based on the ideas of the hybrid P2P and Chord agreement, three layer network models of manufacturing resources have been constructed. According to the structure of geographical area, the bottom centralized resources ring was built in each area; logic ring was structured by the gathering nodes, namely regional resources managers; logic main ring was formed by super nodes. And logic rings are DHT overlay, which are two layers of network. This paper expounds the dynamic organization way for manufacturing resources of two layers of DHT and the layered trust mechanism of the gathering network. In the end, the prototype instance of the resources cloud was presented.

Composition of Resource-Service Chain for Cloud Manufacturing

In distributed manufacturing systems, manufacturing resource composition is one of the most important problems. This is because efficiency of resource selection and resource utilization can all be improved if it is tackled well. However, most of the existing methods neglect temporal relationship between resources. This leads to an inefficient use of resources, because all resources have to be kept available before a business process is started. A temporal composition of resources is more suitable, as it expresses the scheduling and the flow of servicing to a business process. Therefore, resource services invoked in sequential order are called the resource-service chain (RSC), in view that distributed resources are encapsulated into cloud services in a cloud manufacturing (CMfg) environment. We propose an approach, called RSC composition algorithm (RSCCA) that can better cope with the temporal relationship between the resource services in a business process. Specifically, a two-stage composition method based on the degrees of dependency between resource services in workflow is proposed. To begin, in the build-time stage algorithm, RSCCA resolves initial compositions based on task relatedness and temporal dependencies between resource services, and then calculates the usage frequencies of ICs by mining workflow log at workflow runtime stage. Based on this, RSCCA can compose individual resource services as more than sets, especially as chains, allowing flow directions and dynamics to be considered. RSCCA has been tested with different data sets and the results show that it can be very promising.

Intelligent Service Platform of Manufacturing Process and Tool Based on Data Warehouse

Cloud manufacturing takes the advanced information technology as the media to provide life cycle manufacturing resource integration and shares services such as product development and manufacturing. Machine tool as a cloud manufacturing services terminal is an important part of product manufacturing. Machining process and cutting tools as the ways of realizing the manufacturing are indispensable, they are services interface in the cloud manufacturing system. The process data sharing service framework in the cloud manufacturing environment is presented. The basic data of cutting, machining process and tool in data warehouse are the foundation and core of data service in this framework. Data mining technology is taken as data query and processing method. Intelligent matching strategy based on machining feature recognition technique, process parameters and machining tool is established. On the basis of this, the technology of machining equipment inquiry and adaptation method are discussed. Finally, the framework of machining process and tool information service strategy is obtained.

Cloud-based Job Dispatching Using Multi-criteria Decision Making

The dynamic nature of modern workshops and the change in availability of manufacturing equipment affects the allocation of manufacturing jobs. In order to cope with these requirements a module that ranks manufacturing equipment and proposes the best fitted machine to perform a specific manufacturing task according to capabilities, availability and suitability is needed. This paper presents such a framework for allocating tasks to manufacturing equipment according to the capabilities, the availability and the running cost of the manufacturing equipment. The decisions are made by using a multi-criteria decision making tool running in a Cloud environment with data being fed through web based protocols. The novelty of the proposed system lies in the fact that the solution is based on a Cloud Manufacturing environment and the selection process is based on the latest information regarding the machine tool data, received through a web interface. The functionality of the module is illustrated through a case study.

An integrated solution—KAGFM for mass customization in customer-oriented product design under cloud manufacturing environment

The global prosperous development of Internet has revolutionized the traditional business mode of manufacturing industry (e.g., the emergence of agile mass customization) as it ubiquitously provides the stable and high-speed Internet access for conveniently gathering huge quantities of raw data (e.g., personal information, product reviews, sales, and other context information) during the process that end-users enjoy their products and services. Thus, based on the outputs of deep processing of these data, manufacturing enterprises have the potential to respond rapidly to the changing customer needs and markets under the dynamic environment of multi-site design and cloud manufacturing, which is becoming a core competitiveness of enterprises. However, due to a large amount of data scattered in various distributed databases or clouds of enterprises has not yet been exploited effectively, this valuable competence gained from mass customization is still not practically available in the modern manufacturing industry. In order to provide a feasible treatment for mass customization in product design under cloud manufacturing environment, the three critical problems identified in this paper must be solved comprehensively. Thus, this research has proposed a novel integrated solution (named “KAGFM”) to realize effective mass customization for customer-oriented product design in an intelligent computerized manner based on big data mining, that is, manufacturing enterprises can adjust product design schemes keenly adapting with the specific requirements of a certain group of customers. By taking advantage of the cloud manufacturing, this solution can extract a set of fuzzy IF-THEN rules from huge quantities of raw data through integrating the artificial neural network (ANN), genetic algorithm (GA), and fuzzy rules seamlessly together to precisely extract and express semantic relationships between individual data sets (including personal attributes and preferences) and key product design elements. This paper tests and evaluates the proposed solution through a case study on mass customization for concept mobile phone design. Experimental results have showed that it can provide a better customized product design mode in context of cloud manufacturing through reducing data processing time, improving data utilization rate and facilitating enterprises to make a quick and correct response to changes of customer preferences.

A modular structure data modeling method for generalized products

Traditional product modeling methods mainly aim at physical products. However, with cloud manufacturing, the efficient management and utilization of big data under the support of cloud service platform are a challenge, and establishing a reasonable data model is the core of product life cycle data management for generalized products. Aiming at big data management of generalized product modules, the definition, description, and classification of general modules are given in this paper firstly. Then, modular master structure data models are built, including the composition of the generalized modular master structure, property description of generalized part variables, connection objects among the generalized parts, etc. Thirdly, modular instance structure data models of the generalized product are established, the formation process of the instance structure model is analyzed, and the structure mapping views in different phases of the product life cycle are also presented. Finally, based on the secondary development of a product life cycle management (PLM) system, the generalized product data models and the modular structure models of the transformer are carried out. The traditional integrated product models are enriched and improved so the demand of modular data management of generalized products is satisfied under the environment of cloud manufacturing.

Customized Encryption of Computer Aided Design Models for Collaboration in Cloud Manufacturing Environment

Computer aided design (CAD) models reflect design goals, intentions, and functions so that they are the key intellectual properties of companies. In a Cloud manufacturing environment, how to effectively protect the sensitive feature information for a model owner while enabling the owner to flexibly share the rest of the CAD model with collaborators is an important yet challenging research issue. In this paper, an innovative partial encryption approach, which is able to represent a CAD model into the granularity of sharing information in order to address various collaboration scenarios and customized requirements from the model owner and collaborators, is presented. The approach is composed of a customized encryption algorithm for a CAD model, a key based customized authorization algorithm for collaborators to decrypt shared features in the model, and a customized geometric transformation method to support the effective protection model-based visualization of the model for collaboration. With this approach, a CAD model can be flexibly encrypted to realize partial sharing of features and safe protection of the rest of the model according to collaboration requirements. Meanwhile, during encryption and decryption, the CAD model is always manifold no matter which feature is encrypted or decrypted to ensure user friendliness, model validity, and robustness of the approach. A case study is used to verify and illustrate the effectiveness of the approach. This research is a new attempt to design a content-based and customized encryption approach applicable to CAD model-based collaboration in a Cloud manufacturing environment.

A Cloud Manufacturing Resource Allocation Model Based on Ant Colony Optimization Algorithm

Resources should be allocated efficiently in a cloud manufacturing environment, given specific cloud manufacturing task. We study on the optimal resource allocation after the qualitative analysis of the match between the tasks and the resources in this work. Along this line, many factors should be significant, including time, cost and quality of services. Moreover, the workload of equipments should also be considered, in order to achieve load balance and improve the efficiency of manufacturing and the productivity. Therefore, in this paper, based on a four-dimensional objective function, that is, time, cost, quality of services and the load balance, we adapt the Ant Colony Optimization (ACO) algorithm to find the optimal solution. We also present a case study to evaluate our model.

Research on the Key Technology of Manufacturing Services Modeling under Cloud Manufacturing Environments

To realize sharing and synergy between manufacturing resources and manufacturing capability in cloud manufacturing, the manufacturing service model is divided into function model and non-function model. Elements and description of function model is given, and function model determines manufacturing services’ combination planning. Also the composition of non-function model and each part’s weight is presented, and non-function model is the basis to evaluate the priority of manufacturing services. For the case of resource in cloud manufacturing services, sharing ontology and private ontology are researched. Sharing ontology is common description of the whole manufacturing domain, and private ontology is individual description of a specific manufacturing platform. The transformation between sharing ontology and private ontology provides a way for isomerism resources to invoke each other.

Research on the Selection Problem of Machine Tools in Cloud Manufacturing Environment

This paper proposes a method to choose machine resources in order to realize the on-demand use of machine resources in cloud manufacturing environment. A convergence mode of the machine resources is described and the selection process is given. A multi-level matching process of machine tools is proposed. Different matching methods are designed for different parameter types of machining tasks and machine resources, and then machine resources are screened according to the requirements of machining tasks to form the machine resources candidate sets. Then a multi-objective optimal selection model of machine resources is constructed, regarding minimization of costs and time and maximization of service quality and reputation as the target, which is solved by using genetic algorithms. Finally, the algorithm is analyzed and validated with an example, and a kind of solution thinking and method is provided to select machine tools in manufacturing cloud environment.

Supporting capacity sharing in the cloud manufacturing environment based on game theory and fuzzy logic

This paper proposes a Framework for Capacity Sharing in Cloud Manufacturing (FCSCM) able to support the capacity sharing issue among independent firms. The success of geographical distributed plants depends strongly on the use of opportune tools to integrate their resources and demand forecast in order to gather a specific production objective. The framework proposed is based on two different tools: a cooperative game algorithm, based on the Gale–Shapley model, and a fuzzy engine. The capacity allocation policy takes into account the utility functions of the involved firms. It is shown how the capacity allocation policy proposed induces all firms to report truthfully their information about their requirements. A discrete event simulation environment has been developed to test the proposed FCSCM. The numerical results show the drastic reduction of unsatisfied capacity obtained by the model of cooperation implemented in this work.

Ontology for manufacturing resources in a cloud environment

Cloud manufacturing is a model for enabling on-demand network access to a shared pool of reconfigurable manufacturing resources that can be rapidly encapsulated, provisioned and released as manufacturing services. Enterprises involved in cloud manufacturing networks share their heterogeneous business models, manufacturing resources and knowledge to provide high quality consumable manufacturing services. Therefore, there is a need to develop a resource description protocol and service description language to cover all the aspects of cloud-based business collaborations in manufacturing industry. This information model needs to be inclusive of representation of all phases of a product’s lifecycle, service model description, and other essential information contributed to e-businesses. This paper presents an ontology-based approach to enable sematic interoperability throughout the whole process of service provision in the clouds. The detailed requirements for enabling cloud-based data exchange are discussed. A generic ontology development process is then proposed with special focus on reusing existing international and/or industrial standards. Specifically highlighted is a systematic guidance on developing ontologies. The utilisation of the proposed ontology in resource virtualisation and resource retrieval in cloud manufacturing environments is also elaborated.

Virtualise manufacturing capabilities in the cloud: requirements, architecture and implementation

In recent years, cloud manufacturing concept has been proposed by taking advantage of cloud computing to enhance the performance of manufacturing industry. Cloud manufacturing can be perceived as two types, i.e., manufacturing version of computing cloud, and a distributed environment that is networked around manufacturing cloud. This paper discusses manufacturing resources, abilities and relevant essentials from the service-oriented perspective. The functional requirements of a cloud manufacturing environment are also discussed, along with an interoperable manufacturing system framework. Cloud resource integration methods have been developed based on the function block and software agent technologies. It is possible to achieve a collaborative, intelligent, and distributed environment via cloud manufacturing technologies.

Connecting factories and related IT environments to manufacturing clouds

Cloud manufacturing is an emerging concept related to the management of flexible production networks such as virtual enterprises. It not only suggests management approaches such as service-orientation in manufacturing but also provides solutions for efficient execution of the production network management by means of IT tools. Such tools are in most cases represented by cloud manufacturing platforms to which the managed production facilities are connected. However, when integrating factories and their IT infrastructure to cloud manufacturing environments, considerable effort is necessary to implement appropriate IT interfaces. In order to overcome this issue, this paper suggests a concept for a configurable interface component, i.e. factory adapter, which enables to easily configure and execute data exchange between factory-internal IT systems with manufacturing clouds. Especially small and medium-sized companies as well as enterprises participating in short-term or ad-hoc production networks will benefit from a factory adapter which helps to decrease efforts for IT interface implementation during supply chain integration.

Cloud service selection based on trust evaluation for cloud manufacturing environment

For cloud manufacturing environment,many manufacturing cloud services have the same or similar function,so it is difficult to get the most suitable cloud services.This study designed a selection method of the manufacturing cloud services based on trust evaluation.How to select cloud services was described by abstraction;the reliability,usability,timeliness,price and honesty were used as the trust characteristics together;and the evaluation time and effect of estimators' honesty on the service's credibility were also taken into account;and then the overall credibility was calculated from all above data by weighted average method.Furthermore,with all factors such as the cloud services' function,workload,current state and physical distance considered in package,the method was built to guide the cloud service selection by matching the services' function,workload and price and combining the trust evaluation.The results of simulation experiments show that the service selection method is able to recognize entities of manufacturing cloud services,and it improves the rate of the cloud service trades and meets users' functional and non-functional requests better.

Quantum Harmony Search Algorithm for Assembly Sequence Planning of Design Knowledge Resources in the Cloud Manufacturing Environment

Facing the condition of scattered and disorder design knowledge and insufficient innovative capacity of Small and Medium Enterprises (SMEs) in Cloud Manufacturing environment, making for the design of knowledge assembled into orderly combination of knowledge resources, this paper presents the service capacity of evaluation model of knowledge resources and programming knowledge resources in the assembly sequence the mathematical model of the planning and design to solve the model of quantum harmony search algorithm (QHS). QHS algorithm bases on the Latin hypercube (LH), and improves the global search capability through the introduction of quantum coding and quantum gate transformation. Taking mold design knowledge of SMEs resources planning issues as example, according to the mathematical model, QHS algorithm aims to solve the knowledge resources assembly sequence of the optimal design in the cost and quality constraints, and verifies the feasibility and practicality of the method. This paper has practical significance to improve the overall SMEs innovation and design capabilities and make the reuse of the design knowledge more efficiency in the Cloud Manufacturing environment.