A Comparison of Point Data Selection Schemes for Evolutionary Surface Reconstructions

This article presents a study of the application of Computational Intelligence (CI) methods to the problem of optimal surface reconstruction using triangulations and NURBS (Non-Uniform Rational B-Splines) surface approximations on digitized point data. In mechanical engineering surface reconstructions are used to transform physical objects into mathematical representations for computer aided design purposes. In order to record the geometrical shape of the objects, tactile or optical sensors generate point sets with a huge number of sample points. The number and distribution of these points are decisive for the quality and computational efficiency of the numerical surface representations. Triangulations and NURBS are widely used in CAD/CAM-applications, because they belong to a class of very exible discrete interpolation and approximation methods. In order to verify the suitability of surface model independent point selection schemes and to find model dependent sampling point distributions, optimal surface reconstructions are used

Dynamic Neighborhood Structures in Parallel Evolution Strategies

Parallelizing is a straightforward approach to reduce the total computation time of evolutionary algorithms. Finding an appropriate communication network within spatially structured populations for improving convergence speed and convergence probability is a difficult task. A new method that uses a dynamic communication scheme in an evolution strategy will be compared with conventional static and dynamic approaches. The communication structure is based on a socalled diffusion model approach. The links between adjacent individuals are dynamically chosen according to deterministic or probabilistic rules. Due to self-organization effects, efficient and stable communication structures are established that perform robust and fast on a multimodal test function

Determination of thermal wave reflection coefficient to better estimate defect depth using pulsed thermography

Thermography is a promising method for detecting subsurface defects, but accurate measurement of defect depth is still a big challenge because thermographic signals are typically corrupted by imaging noise and affected by 3D heat conduction. Existing methods based on numerical models are susceptible to signal noise and methods based on analytical models require rigorous assumptions that usually cannot be satisfied in practical applications. This paper presents a new method to improve the measurement accuracy of subsurface defect depth through determining the thermal wave reflection coefficient directly from observed data that is usually assumed to be pre-known. This target is achieved through introducing a new heat transfer model that includes multiple physical parameters to better describe the observed thermal behaviour in pulsed thermographic inspection. Numerical simulations are used to evaluate the performance of the proposed method against four selected state-of-the-art methods. Results show that the accuracy of depth measurement has been improved up to 10% when noise level is high and thermal wave reflection coefficients is low. The feasibility of the proposed method in real data is also validated through a case study on characterising flat-bottom holes in carbon fibre reinforced polymer (CFRP) laminates which has a wide application in various sectors of industry

Taxonomy and uncertainties of cloud manufacturing

The manufacturing industry is currently undergoing rapid changes because of the rapid growth of advanced technologies in information systems and networks, which allow for collaboration around the world. This combination of the latest information technologies and advanced manufacturing networks has led to the growth of a new manufacturing model known as cloud manufacturing. Because cloud manufacturing is considered an emerging research area, there are significant gaps in the literature regarding the concept of cloud manufacturing, its implementation, and in particular the uncertainties coming with this new technology. This research aims to explain the concept of cloud manufacturing, its capabilities and potential. This work also introduces cloud manufacturing taxonomy, and investigates uncertainties that come with employing cloud manufacturing. Finally, proposals for future research in the context of cloud manufacturing are presented to address opportunities in cloud manufacturing

A comparison of resource allocation process in grid and cloud technologies

Grid Computing and Cloud Computing are two different technologies that have emerged to validate the long-held dream of computing as utilities which led to an important revolution in IT industry. These technologies came with several challenges in terms of middleware, programming model, resources management and business models. These challenges are seriously considered by Distributed System research. Resources allocation is a key challenge in both technologies as it causes the possible resource wastage and service degradation. This paper is addressing a comprehensive study of the resources allocation processes in both technologies. It provides the researchers with an in-depth understanding of all resources allocation related aspects and associative challenges, including: load balancing, performance, energy consumption, scheduling algorithms, resources consolidation and migration. The comparison also contributes an informal definition of the Cloud resource allocation process. Resources in the Cloud are being shared by all users in a time and space sharing manner, in contrast to dedicated resources that governed by a queuing system in Grid resource management. Cloud Resource allocation suffers from extra challenges abbreviated by achieving good load balancing and making right consolidation decision

A Design Approach to IoT Endpoint Security for Production Machinery Monitoring

The Internet of Things (IoT) has significant potential in upgrading legacy production machinery with monitoring capabilities to unlock new capabilities and bring economic benefits. However, the introduction of IoT at the shop floor layer exposes it to additional security risks with potentially significant adverse operational impact. This article addresses such fundamental new risks at their root by introducing a novel endpoint security-by-design approach. The approach is implemented on a widely applicable production-machinery-monitoring application by introducing real-time adaptation features for IoT device security through subsystem isolation and a dedicated lightweight authentication protocol. This paper establishes a novel viewpoint for the understanding of IoT endpoint security risks and relevant mitigation strategies and opens a new space of risk-averse designs that enable IoT benefits, while shielding operational integrity in industrial environments

The Internet connected production line : realising the ambition of cloud manufacturing

This paper outlines a vision for Internet connected production complementary to the Cloud Manufacturing paradigm, reviewing current research and putting forward a generic outline of this form of manufacture. This paper describes the conceptual positioning and practical implementation of the latest developments in manufacturing practice such as Redistributed manufacturing, Cloud manufacturing and the technologies promoted by Industry 4.0 and Industrial Internet agendas. In the illustration of the outline of web enabled production a case study is presented based on automotive manufacture. Existing and future needs for customized production and the manufacturing flexibility required are examined. Future directions for manufacturing, enabled by web based connectivity are then examined, concluding that the need for humans to remain ‘in the loop’ while automation develops is an essential ingredient of all future manufacturing scenarios