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Cloud manufacturing is a consumer-centric requirement-driven manufacturing paradigm that integrates distributed resources for providing services to consumers in an on-demand manner. Scheduling of multiple tasks is an important technical means for satisfying consumer requirements in cloud manufacturing. However, high individualised requirements and the associated complex task structures complicate the task scheduling in cloud manufacturing. This paper establishes a more comprehensive model for scheduling multiple distinct tasks with complicated manufacturing processes. The hierarchical relationships (a mixture of dependency and independency) of subtasks within tasks are considered. The objectives involve three kinds of time and cost factors, namely processing time, setup time, transfer time and the respective cost. In addition, service quality is also considered into the optimisation objective. Two multi-objective-meta-heuristic algorithms, i.e. ACO-based multi-objective algorithm (MACO) and NSGA-II-based multi-objective algorithm (MGA), are designed to solve the scheduling problem. A detailed analysis of the performance of the two algorithms is performed by applying them to several different scheduling instances. Experimental results indicate that in most cases the MACO algorithm can obtain a more diverse set of Pareto solutions hence offering more alternatives to meet widely different users' needs. [ABSTRACT FROM AUTHOR]

Nondestructive evaluation (NDE) techniques of phased array ultrasonic testing (PAUT) and digital X-ray radiography were employed on friction stir (FS)-welded Aluminum Alloy (AA)-2219-T87 specimens. PAUT intricacies required for scanning of FS-welded specimens with a 10-MHz 32-element transducer are discussed. The time corrected gain (TCG) calibration is required for scanning with an increase in index offset to compensate for decrease in A-Scan signal peak amplitude. Calibration techniques to find small defects with appropriate size tolerances are also established. The NDE technique of digital X-ray radiography is compared to PAUT, where it was found that a calibrated PAUT system is able to discover defects less than 0.2 mm where X-ray radiography could not. Incomplete penetration (IP), wormhole (WH), surface cavity (SC), and internal void (IV) defects are analyzed. Furthermore, an online PAUT system for FSW has been developed and successfully tested. The work provided herein will provide a gateway for an ultimate goal of an automated PAUT online sensing system. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Highlights: [•] The obstructive effect of CO on methanol decomposition on Pt nanoclusters on the Al2O3/NiAl(100) was studied. [•] The probability of dehydrogenation of methanol was decreased by pre-adsorbed CO to half. [•] The pre-adsorbed CO obstructed less effectively the C O bond scission of methanol. [•] The produced CO with atomic hydrogen from dehydrogenated methanol exhibited a comparable effect. [ABSTRACT FROM AUTHOR]