Cloud-based Control of Thermal Based Manufacturing Processes

Papacharalampopoulos, Alexios; Stavridis, John; Stavropoulos, Panagiotis; Chryssolouris, George

doi:10.1016/j.procir.2016.09.036

Cited by 20 publications

(8 citation statements)

References 21 publications

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“…As highlighted in (Mourtzis, Vlachou, & Milas, 2016) big data within manufacturing companies is not necessarily only about products and customers, a single machine tool monitoring system could easily generate nearly 0.5TB of data a year. Also, network-based control systems will generate potentially large quantities of data (Papacharalampopoulos, Stavridis, Stavropoulos, & Chryssolouris, 2016), with both of these sources containing potentially useful data. Chen and Zhang (Philip Chen & Zhang, 2014) stated that a large number of techniques and technologies to capture, curate, analyse and visualize big data have been developed by scientists in different fields such as computer science, economics, mathematics and statistics, but they are some distance away from being able to meet a wide variety of needs since that the field of big data analysis also encompasses data mining, machine learning, neural networks, social network analysis, signal processing, pattern recognition, optimization methods and visualization.…”

Section: Big Data Analyticsmentioning

confidence: 99%

Cloud-based big data analytics for customer insight-driven design innovation in SMEs

Ying

Soroka

Han

et al. 2020

International Journal of Information Management

121

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Fast development of IT and ICT facilitate customers to post a large volume of their concerns and expectation online, which are widely accepted to be a valuable resource for product designers. However, it is found that only a small number of small and medium-sized enterprises (SMEs) have capabilities to leverage customer online insights for design innovation, which often demonstrate a significant share in national economies growth. To discover the beneath reasons regarding the barrier that prevent them to make effective utilization, in this study, as a concrete example, manufacturing SMEs in the South Wales and Greater Manchester industrial areas of the UK are focused and their potential motivations for using and knowledge of big data-based customer analytics are investigated. An exploratory survey was conducted in terms of the type of customer data they have, the storage approaches, the volume of customer data, etc. Next, a carefully devised exploratory study was undertaken to understand how SMEs perceive the relations between customer data and product design, how about their expectations from big customer data analytics and what really challenges SMEs to exploit the value of big customer data. Besides, a demonstration platform is developed to present SMEs an automatic process of analysing customer online reviews and the capacity on customer insights acquisition and strategic decision making. Finally, findings from two focus groups indicate the different managerial and technical considerations required for SMEs considering implementing big data and customer analytics. This study encourages SMEs to welcome big customer data and suggests that a cloud-based approach may be the most appropriate way of giving access to big data analytics techniques.

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Section: Big Data Analyticsmentioning

confidence: 99%

Cloud-based big data analytics for customer insight-driven design innovation in SMEs

Ying

Soroka

Han

et al. 2020

International Journal of Information Management

121

View full text Add to dashboard Cite

show abstract

“…Prior to the implementation, the users' and the system's requirements had to be identified. The user requirements were extracted based on the information that should be available for the process, whilst similar user-interfaces were developed for other applications [37][38][39]. From a user point of view, two (2) categories/types that could interact with the platform, each one of them having access specific areas of the collaborative workspace were identified:…”

Section: Platform Requirementsmentioning

confidence: 99%

A three-stage quality diagnosis platform for laser-based manufacturing processes

Stavropoulos

Papacharalampopoulos

Stavridis

et al. 2020

Int J Adv Manuf Technol

Self Cite

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Diagnosis systems for laser processing are being integrated into industry. However, their readiness level is still questionable under the prism of the Industry’s 4.0 design principles for interoperability and intuitive technical assistance. This paper presents a novel multifunctional, web-based, real-time quality diagnosis platform, in the context of a laser welding application, fused with decision support, data visualization, storing, and post-processing functionalities. The platform’s core considers a quality assessment module, based upon a three-stage method which utilizes feature extraction and machine learning techniques for weld defect detection and quality prediction. A multisensorial configuration streams image data from the weld pool to the module in which a statistical and geometrical method is applied for selecting the input features for the classification model. A Hidden Markov Model is then used to fuse this information with earlier results for a decision to be made on the basis of maximum likelihood. The outcome is fed through web services in a tailored User Interface. The platform’s operation has been validated with real data.

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“…through polytopes) [11], that must not be confused with statistical control. Optimal control [12], next, can be used to address the various extra (manufacturing) criteria, such as energy efficiency, while the practicality and the generality can be addressed through some intelligent control approach, such as neural modelling, or even neural control [13,14].…”

Section: Introductionmentioning

confidence: 99%

Manufacturing Process Optimization via Digital Twins: Definitions and Limitations

Papacharalampopoulos

Stavropoulos

2022

Lecture Notes in Mechanical Engineering

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Manufacturing process real-time optimization has been one of the main digital twins’ operations. It is of utmost importance to the processes, since it enables the feedback of a digital twin towards the real world. However, it is quite difficult to be implemented, since it requires modelling of the process, adaptivity of both the model and the process, real-time communication and link to other functionalities. Under the framework of formalizing such activities, the current work attempts to categorize the types of manufacturing process real-time optimization and show their limitations. For the sake of simplicity, generic process models are adopted and then the requirements for the process control are given, driving the aforementioned definitions. Specific numerical examples are used to illustrate the definitions, while the latter presented herein span all categories of real-time optimization as well as all manufacturing performance indicators. Finally, both mathematically and physics-wise, the limitations are discussed.

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Cloud-based Control of Thermal Based Manufacturing Processes

Cited by 20 publications

References 21 publications

Cloud-based big data analytics for customer insight-driven design innovation in SMEs

Cloud-based big data analytics for customer insight-driven design innovation in SMEs

A three-stage quality diagnosis platform for laser-based manufacturing processes

Manufacturing Process Optimization via Digital Twins: Definitions and Limitations

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