Latest Papers on Cloud Manufacturing (Source: Scopus – June 20, 2017)

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ABSTRACT: Traditionally, the resources of embedded devices which are employed for process control at shop floors were resource constrained. However, advances in embedded system technologies permit the enhancement of the processing and storage capabilities of embedded devices. Therefore, semantic descriptions of manufacturing systems can now be hosted and computed at the device level. This fact permits the creation of a decentralised solution for controlling processes at the lowest level of the manufacturing enterprises and the reduction in the time and effort requirements for the configuration and information exchange. The eScop project presented the Open Knowledge-Driven Manufacturing Execution System (OKD-MES) solution, which enables monitoring and controlling production systems openly and allows runtime re-configurability of interconnected industrial equipment and services. This research work presents how part of the OKD-MES functionality can be handled at lower level. More precisely, the OKD-MES representation and management of knowledge can be decentralised and handled at the shop floor level, where the industrial machines are connected to devices that are capable of controlling the execution of processes. The main objective of this paper is to describe a decentralised vision for the OKD-MES framework, which is a centric solution in terms of knowledge management. Moreover, the article also discusses some of the advantages to be gained from decentralising the management of knowledge model semantic descriptions.

ABSTRACT: Optimization of the process of product lifecycle management is an increasingly important objective for manufacturing enterprises to improve their sustainable competitive advantage. Originally, this approach was developed to integrate the business processes of an organization and more effectively manage and utilize the data generated during lifecycle studies. With emerging technologies, product embedded information devices such as radio frequency identification tags and smart sensors are widely used to improve the efficiency of enterprises’ routine management on an operational level. Manufacturing enterprises need a more advanced analysis approach to develop a solution on a strategic level from using such lifecycle Big Data. However, the application of Big Data in lifecycle faces several challenges, such as the lack of reliable data and valuable knowledge that can be employed to support the optimized decision-making of product lifecycle management. In this paper, a framework for Big Data driven product lifecycle management was proposed to address these challenges. Within the proposed framework, the availability and accessibility of data and knowledge related to lifecycle can be achieved. A case study was presented to demonstrate the proof-of-concept of the proposed framework. The results showed that the proposed framework was feasible to be adopted in industry, and can provide an overall solution for optimizing the decision-making processes in different phases of the whole lifecycle. The key findings and insights from the case study were summarized as managerial implications, which can guide manufacturers to ensure improvements in energy saving and fault diagnosis related decisions in the whole lifecycle.

ABSTRACT: The role of digital technologies in service business transformation is under-investigated. This paper contributes to filling this gap by addressing how the Internet of things (IoT), cloud computing (CC) and predictive analytics (PA) facilitate service transformation in industrial companies. Through the Data–Information–Knowledge–Wisdom (DIKW) model, we discuss how the abovementioned technologies transform low-level entities such as data into information and knowledge to support the service transformation of manufacturers. We propose a set of digital capabilities, based on the extant literature and the findings from four case studies. Then, we discuss how these capabilities support the service transformation trajectories of manufacturers. We find that IoT is foundational to any service transformation, although it is mostly needed to become an availability provider. PA is essential for moving to theperformance provider profile. Besides providing scalability in all profiles, CC is specifically used to implement an industrialiser strategy, therefore leading to standardised, repeatable and productised offerings.

ABSTRACT: Cyber-physical systems are integrations of computation, networking, and physical processes and they are increasingly finding applications in manufacturing. Cloud manufacturing integrates cloud computing and service-oriented technologies with manufacturing processes and provides manufacturing services in manufacturing clouds. A cyber physical system for manufacturing is not a manufacturing cloud if it does not use virtualization technique in cloud computing and service oriented architecture in service computing. On the other hand, a manufacturing cloud is not cyber physical system if it does not have components for direct interactions with machine tools and other physical devices. In this paper, a new paradigm of Cyber-Physical Manufacturing Cloud (CPMC) is introduced to bridge gaps among cloud computing, cyber physical systems, and manufacturing. A CPMC allows direct operations and monitoring of machine tools in a manufacturing cloud over the Internet. A scalable and service-oriented layered architecture of CPMC is developed. It allows publication and subscription of manufacturing web services and cross-platform applications in CPMC. A virtualization method of manufacturing resources in CPMC is presented. In addition, communication mechanisms between the layers of the CPMC using communication protocols such as MTConnect, TCP/IP, and REST are discussed. A CPMC testbed is developed and implemented based on the proposed architecture. The testbed is fully operational in two geographically distributed sites. The developed testbed is evaluated using several manufacturing scenarios. Its testing results demonstrate that it can monitor and execute manufacturing operations remotely over the Internet efficiently in a manufacturing cloud.

ABSTRACT: As a new service-oriented smart manufacturing paradigm, cloud manufacturing (CMfg) aims at fully sharing and circulation of manufacturing capabilities towards socialization, in which composite CMfg service optimal selection (CCSOS) involves selecting appropriate services to be combined as a composite complex service to fulfill a customer need or a business requirement. Such composition is one of the most difficult combination optimization problems with NP-hard complexity. For such an NP-hard CCSOS problem, this study proposes a new approach, called multi-population parallel self-adaptive differential artificial bee colony (MPsaDABC) algorithm. The proposed algorithm adopts multiple parallel subpopulations, each of which evolves according to different mutation strategies borrowed from the differential evolution (DE) to generate perturbed food sources for foraging bees, and the control parameters of each mutation strategy are adapted independently. Moreover, the size of each subpopulation is dynamically adjusted based on the information derived from the search process. Different scales of the CCSOS problems are conducted to validate the effectiveness of the proposed algorithm, and the experimental results show that the proposed algorithm has superior performance over other hybrid and single population algorithms, especially for complex CCSOS problems.

ABSTRACT: With many industries increasingly relying on leased equipment and machinery, many original equipment manufacturers (OEMs) are turning to product-service packages where they deliver (typically lease) the physical assets. An integrated service contract will be offered for the asset. A classic example being Rolls Royce power-by-the-hour aircraft engines. Service contracts offered by original equipment manufacturers have predominantly focused on maintenance and upkeep activities for a single asset. Interestingly enough, manufacturing industries are beginning to adopt the product-service paradigm. However, one of the unique aspects in manufacturing settings is that the leased system is often not a single asset but instead a multi-unit system (e.g., an entire production line). In this paper, we develop a lease-oriented maintenance methodology for multi-unit leased systems under product-service paradigm. Unlike traditional maintenance models, we propose a leasing profit optimization (LPO) policy to adaptively compute optimal preventive maintenance (PM) schedules that capture the following dynamics: (1) the structural dependencies of the multi-unit system, (2) opportunistic maintenance of multiple system components, and (3) leasing profit savings (LPSs). We demonstrate the performance of our multi-unit maintenance policy by using a leased automotive manufacturing line and investigate its impact on leasing profits.

ABSTRACT: Energy consumption in manufacturing has risen to be a global concern. Material selection in the product design phase is of great significance to energy conservation and emission reduction. However, because of the limitation of the current life-cycle energy analysis and optimization method, such concerns have not been adequately addressed in material selection. To fill in this gap, a process to build a comprehensive multi-objective optimization model for automated multimaterial selection (MOO–MSS) on the basis of cloud manufacturing is developed in this paper. The optimizing method, named local search-differential group leader algorithm (LS-DGLA), is a hybrid of differential evolution and local search with the group leader algorithm (GLA), constructed for better flexibility to handle different needs for various product designs. Compared with a number of evolutionary algorithms and nonevolutionary algorithms, it is observed that LS-DGLA performs better in terms of speed, stability, and searching capability.

ABSTRACT: This paper proposes a multi-objective hybrid artificial bee colony (MOHABC) algorithm for service composition and optimal selection (SCOS) in cloud manufacturing, in which both the quality of service and the energy consumption are considered from the perspectives of economy and environment that are two pillars of sustainable manufacturing. The MOHABC uses the concept of Pareto dominance to direct the searching of a bee swarm, and maintains non-dominated solution found in an external archive. In order to achieve good distribution of solutions along the Pareto front, cuckoo search with Levy flight is introduced in the employed bee search to maintain diversity of population. Furthermore, to ensure the balance of exploitation and exploration capabilities for MOHABC, the comprehensive learning strategy is designed in the onlooker search so that every bee learns from the external archive elite, itself and other onlookers. Experiments are carried out to verify the effect of the improvement strategies and parameters’ impacts on the proposed algorithm and comparative study of the MOHABC with typical multi-objective algorithms for SCOS problems are addressed. The results show that the proposed approach obtains very promising solutions that significantly surpass the other considered algorithms.

ABSTRACT: The Service Oriented Architecture (SOA) is quite established in enterprise environments as a pattern for software integration. However, traditional industrial architecture based on a vertical hierarchy is still used in old manufacturing organizations, making it difficult to reach the new requirements in industrial automation. There are works related with the use of SOA WS-* stack based frameworks, but this paradigm is not optimal due to the overhead in communication protocols. From this point of view, we propose a lightweight SOA architectural model based on the use of RESTful style for design and modeling of industrial processes. The aim is to explore a low developed research area to improve interoperability and flexibility in certain demanding manufacturing scenarios while similar performance of current industrial control standard is achieved. Theoretical advantages are presented and experimentally evaluated. In the evaluation, a prototype of the lightweight RESTful Framework and several scenarios of industrial devices are presented and response times are measured using two different RESTful protocols: HTTP and CoAP. From the results obtained it can be concluded that the performance of SOA with REST in industrial environments is suitable for current needs.

ABSTRACT: Many nonlinear customer satisfaction-related factors significantly influence the future customer demand for service-oriented manufacturing (SOM). To address this issue and enhance the prediction accuracy, this article develops a novel customer demand prediction approach for SOM. The approach combines the phase space reconstruction (PSR) technique with the optimized least square support vector machine (LSSVM). First, the prediction sample space is reconstructed by the PSR to enrich the time-series dynamics of the limited data sample. Then, the generalization and learning ability of the LSSVM are improved by the hybrid polynomial and radial basis function kernel. Finally, the key parameters of the LSSVM are optimized by the particle swarm optimization algorithm. In a real case study, the customer demand prediction of an air conditioner compressor is implemented. Furthermore, the effectiveness and validity of the proposed approach are demonstrated by comparison with other classical predication approaches.

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