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

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

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 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

A new semantic attribute deep learning with a linguistic attribute hierarchy for spam detection

The massive increase of spam is posing a very serious threat to email and SMS, which have become an important means of communication. Not only do spams annoy users, but they also become a security threat. Machine learning techniques have been widely used for spam detection. In this paper, we propose another form of deep learning, a linguistic attribute hierarchy, embedded with linguistic decision trees, for spam detection, and examine the effect of semantic attributes on the spam detection, represented by the linguistic attribute hierarchy. A case study on the SMS message database from the UCI machine learning repository has shown that a linguistic attribute hierarchy embedded with linguistic decision trees provides a transparent approach to in-depth analysing attribute impact on spam detection. This approach can not only efficiently tackle ‘curse of dimensionality’ in spam detection with massive attributes, but also improve the performance of spam detection when the semantic attributes are constructed to a proper hierarchy

An overview of artificial intelligence in product design for smart manufacturing

Artificial intelligence (AI) has received significant attention nearly from every part of the world because it is a critical technology approach to develop intelligent systems. The manufacturing sector is one part which exploits AI, especially in the product design stage towards smart manufacturing. The aim of this paper is to present an overview on how AI enhances the product design stage for smart manufacturing. First, the paper gives the overall understanding of smart manufacturing about its definition, importance, and characteristics. Then, it delivers a brief overview of product design and product design stages. The essential concepts of AI techniques as well as various AI applications in product design ranging from conceptual design, embodiment design and detail design are discussed. Finally, research challenges and future directions for using AI in product design are provided and discussed

An extended AI-experience : Industry 5.0 in creative product innovation

Creativity plays a significant role in competitive product ideation. With the increasing emergence of Virtual Reality (VR) and Artificial Intelligence (AI) technologies, the link between such technologies and product ideation is explored in this research to assist and augment creative scenarios in the engineering field. A bibliographic analysis is performed to review relevant fields and their relationships. This is followed by a review of current challenges in group ideation and state-of-the-art technologies with the aim of addressing them in this study. This knowledge is applied to the transformation of current ideation scenarios into a virtual environment using AI. The aim is to augment designers’ creative experiences, a core value of Industry 5.0 that focuses on human-centricity, social and ecological benefits. For the first time, this research reclaims brainstorming as a challenging and inspiring activity where participants are fully engaged through a combination of AI and VR technologies. This activity is enhanced through three key areas: facilitation, stimulation, and immersion. These areas are integrated through intelligent team moderation, enhanced communication techniques, and access to multi-sensory stimuli during the collaborative creative process, therefore providing a platform for future research into Industry 5.0 and smart product development

Challenges for the adoption of electric vehicles in Thailand : potential impacts, barriers, and public policy

The impacts of electric vehicles (EVs) to the current transportation and logistics system are an emerging topic that has recently garnered public interests in many countries. Several developing countries that rely on the large amount of production in automobiles manufacturing are preparing to adopt national strategies to mitigate the negative impacts from the shift toward electric vehicles. In addition, the restructuring of the transportation system and traffic regulations to prepare for the integration of electric vehicles into the current transportation model is also an important concern for policy makers. The study of potential impacts and barriers regarding the adoption of EVs would provide better insights that could aid the implementation of public policy. The topics that will be discussed here are both from technological standpoints such as differences in the general properties of EVs, in comparison to internal combustion engine vehicles (ICEVs), and social and environmental standpoints which are predicted to be pivotal drivers for their adoption. These features are collectively analysed to aid the relating implementation of industrial, transportation, and environmental public policies. Moreover, additional policy recommendations for the situation in Thailand are proposed based on this discussion. It is concluded that extensive public policy framework for the adoption of EVs and the development of EVs manufacturing industry is essential for the developing countries with less technological readiness to effectively integrate this new type of vehicular technology into its industrial and transportation economy

Three-dimensional subsurface defect shape reconstruction and visualisation by pulsed thermography

Defects detected by most thermographic inspection are represented in the form of 2D image, which might limit the understanding of where the defects initiate and how they grow over time. This paper introduces a novel technique to rapidly estimate the defect depth and thickness simultaneously based on one single-side inspection. For the first time, defects are reconstructed and visualised in the form of a 3D image using cost-effective and rapid pulsed thermography technology. The feasibility and effectiveness of the proposed solution is demonstrated through inspecting a composite specimen and a steel specimen with semi-closed airgaps. For the composite specimen, this technique can deliver comparatively low averaged percentage error of the estimated total 3D defect volume of less than 10%