Fog computing and convolutional neural network enabled prognosis for machining process optimization

Cloud enabled prognosis systems have been increasingly adopted by manufacturing industries. The effectiveness of the cloud systems is, however, crippled by the high latency of data transfer between shop floors and the cloud.To overcome the limitation, this paper presents an innovative fog enabled prognosis system for machining process optimization. The system functions include: (1) dynamic prognosis - Convolutional Neural Network (CNN) based prognosis is implemented to detect potential faults from customized machining processes. Preprocessing mechanisms of the CNN are designed for partitioning and de-noising monitored signals to strengthen the performance of the system in practical manufacturing situations; (2) an innovative fog enabled prognosisarchitecture for machining process optimization – it consists of a terminal layer, a fog layer and a cloud layer to minimize data traffic and improve system efficiency. Under the architecture, monitored signals during machining collected on the terminal layer are processed using the trained CNN deployed on the fog layer to efficiently detect abnormal situations. Intensive computing activities like training of the CNN and system re-optimization responding to detected faults are carried out dynamically on the cloud layer to leverage its computation powers. The system was validated in a UK machining company. With the system deployment, the efficiency of energy and production was improved for 29.25% and 16.50% on average. In comparison with a cloud system, this fog system achieved 70.26% reduction in the bandwidth requirement between shop floors and cloud, and 47.02% reduction in data transfer time. This research, sponsored by EU projects, demonstrates that industrialartificial intelligence can facilitate smart manufacturing practices effectively

Agent-based real-time assembly line management for wireless job shop environment

Recent developments in wireless technologies have created opportunities for developing next-generation manufacturing systems with real-time traceability, visibility and interoperability in shop floor planning, execution and control. This paper discusses how to deploy wireless and intelligent technologies to convert physical objects in manufacturing systems into smart objects to introduce and improve the interoperability and visibility between them and thus with manufacturing decision support systems. A reference architecture for wireless manufacturing (WM) is proposed where three types of smart objects are identified. At the same time, the concept of smart object agent (SOA) is presented and the corresponding framework of smart objects management system (SOMS) is constructed. Under this framework and the concept of SOA, a SOA-based WM environment is studied and demonstrated using a near real-life simplified product assembly line for the collection and synchronization of the real-time field data from manufacturing workshops. © 2010 IEEE.published_or_final_versionThe IEEE International Conference on Mechatronics and Automation (ICMA) 2010, Xi'an, China, 4-7 August 2010. In Proceedings of the IEEE International Conference on Mechatronics and Automation, 2010, p. 2013-201

Three-Axis Distributed Fiber Optic Strain Measurement in 3D Woven Composite Structures

Recent advancements in composite materials technologies have broken further from traditional designs and require advanced instrumentation and analysis capabilities. Success or failure is highly dependent on design analysis and manufacturing processes. By monitoring smart structures throughout manufacturing and service life, residual and operational stresses can be assessed and structural integrity maintained. Composite smart structures can be manufactured by integrating fiber optic sensors into existing composite materials processes such as ply layup, filament winding and three-dimensional weaving. In this work optical fiber was integrated into 3D woven composite parts at a commercial woven products manufacturing facility. The fiber was then used to monitor the structures during a VARTM manufacturing process, and subsequent static and dynamic testing. Low cost telecommunications-grade optical fiber acts as the sensor using a high resolution commercial Optical Frequency Domain Reflectometer (OFDR) system providing distributed strain measurement at spatial resolutions as low as 2mm. Strain measurements using the optical fiber sensors are correlated to resistive strain gage measurements during static structural loading. Keywords: fiber optic, distributed strain sensing, Rayleigh scatter, optical frequency domain reflectometr

Supporting adaptiveness of cyber-physical processes through action-based formalisms

Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system

Agile manufacturing: General challenges and an IoT@Work perspective

This paper describes the potential impact of the Internet of Things (IoT) technologies and architecture on factory automation. Whereas, IoT use cases range from intelligent infrastructure and smart cities to health care and shopping assistants, it is important to note that factory automation could benefit as well from an IoT approach. In this paper, we argue that there will not be one IoT but many IoTs that could differ in the type of infrastructure they are running or applications they support. In IoT@Work we focus on the potential of making manufacturing environments more agile and flexible. We explain how the IoT-centric architecture for manufacturing also needs a deep understanding of the manufacturing system and its state today. We, therefore, do a reverse engineering based on the requirements and the description of the agility expected in the automation system itself

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment

Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in the entire ship design, manufacturing and operation process to achieve these goals. This paper highlights smart design, manufacturing and operation as the way forward in an industry 4.0 (i4) era from designing for better energy efficiency to more intelligent ships and smart operation through-life. The paper (i) draws parallels between ship design, manufacturing and operation processes, (ii) identifies key challenges facing such a temporal (lifecycle) as opposed to spatial (mass) products, (iii) proposes a closed-loop ship lifecycle framework and (iv) outlines potential future directions in smart design, manufacturing and operation of ships in an industry 4.0 value chain so as to achieve more energy-efficient vessels. Through computational intelligence and cyber-physical integration, we envision that industry 4.0 can revolutionise ship design, manufacturing and operations in a smart product through-life process in the near future