Knowledge Management for Maintenance, Repair and Service of Manufacturing System

Manufacturing equipment, such as numerical controlled machines and assembly cranes, requires constant maintenance and service in their operating lifecycle. Equipment maintenance plays an important role in avoiding unexpected failures and ensuring production efficiency. During maintenance operations, much data is generated and stored in databases. It is essential for manufacturing companies to develop a system to integrate equipment condition monitoring, fault prediction and knowledge base to support maintenance decisions. A case study, carried out within a power generator manufacturing organisation, was conducted to understand what the maintenance process is and how maintenance knowledge is currently managed. It was concluded that maintenance process is less efficient, and maintenance records, stored within internal databases, are not consistent, which makes knowledge hard to share, learn from and reuse. This paper proposes a Knowledge Management System for Maintenance, Repair and Service in Manufacturing Systems to support better maintenance decision and improve maintenance efficiency

Web-enabled, Real-time, Quality Assurance for Machining Production Systems

AbstractIn order to maintain the close control of production quality, frequent measurement and process parameter adjustments are desirable. In the discrete parts industry, part inspection is intended to be a metric for the process quality but quality control is typically done long after the part has been machined. The long latency between machining and quality assessment makes it difficult to incorporate quality feedback into production. Quality assurance relies on continuous real–time quality feedback, which is not a complex concept. However, the collection and representation of the necessary process data and quality measurement data is challenging. This paper discusses Web-enabled, real-time quality data and statistics based on the integration of two manufacturing open specifications: MTConnect and Quality Measurement Results (QMResults). A pilot implementation that integrates the two technologies and produces Web-enabled, real-time quality results in a standard XML representation from Computer Numerical Control (CNC) machine tool inspections will be discussed

A Cloud-based Framework for Shop Floor Big Data Management and Elastic Computing Analytics

Advanced digitalization together with the rise of disruptive Internet technologies are key enablers of a fundamental paradigm shift observed in industrial production. This is known as the fourth industrial revolution (Industry 4.0) which proposes the integration of the new generation of ICT solutions for the monitoring, adaptation, simulation, and optimisation of factories. With the democratization of sensors and actuators, factories and machine tools can now be sensorized and the data generated by these devices can be exploited, for instance, to optimise the utilization of the machines as well as their operation and maintenance. However, analyzing the vast amount of generated data is resource demanding both in terms of computing power and network bandwidth, thus requiring highly scalable solutions. This paper presents a novel big data approach and analytics framework for the management and analysis of machine generated data in the cloud. It brings together standard open source technologies and the exploitation of elastic computing, which, as a whole, can be adapted to and deployed on different cloud computing platforms. This enables reducing infrastructure costs, minimizing deployment difficulty and providing on-demand access to a virtually infinite set of computing power, storage and network resources.H202