The integrated use of enterprise and system dynamics modelling techniques in support of business decisions

Enterprise modelling techniques support business process re-engineering by capturing existing processes and based on perceived outputs, support the design of future process models capable of meeting enterprise requirements. System dynamics modelling tools on the other hand are used extensively for policy analysis and modelling aspects of dynamics which impact on businesses. In this paper, the use of enterprise and system dynamics modelling techniques has been integrated to facilitate qualitative and quantitative reasoning about the structures and behaviours of processes and resource systems used by a Manufacturing Enterprise during the production of composite bearings. The case study testing reported has led to the specification of a new modelling methodology for analysing and managing dynamics and complexities in production systems. This methodology is based on a systematic transformation process, which synergises the use of a selection of public domain enterprise modelling, causal loop and continuous simulationmodelling techniques. The success of the modelling process defined relies on the creation of useful CIMOSA process models which are then converted to causal loops. The causal loop models are then structured and translated to equivalent dynamic simulation models using the proprietary continuous simulation modelling tool iThink

Focused ion beam milling of brass for microinjection mould fabrication

In this paper focused ion beam (FIB) milling (sputtering) is demonstrated for the fabrication of brass microinjection moulding inserts which have been previously conventionally milled. It is found that FIB milling of the α phase of the material results in much smoother final surfaces than the β phase. An annealing procedure for minimizing the effects of differential sputtering has also been performed. Further with the help of Scanning Electron Microscopy (SEM) and White Light Interferometry (WLI) measurements the FIB milling yield for 70-30 cartridge brass is determined and analysed. Finally, FIB milling of 5µm square trenches with a flat bottom surface is demonstrated

Apparent beam size definition of focused ion beams based on scanning electron microscopy images of nanodots

In this paper the new term apparent beam size of Focused Ion Beam (FIB) is introduced and an original method of its evaluation is demonstrated. Traditional methods of measuring the beam size, like the knife edge method, provide information about the quality of the beam itself but practically they do not give information on the FIB sputtering resolution. To do this, it is necessary to take into account the material dependant interaction of the beam with the specimen and the gas precursor in the vacuum chamber. The apparent beam size can be regarded as the smallest possible dot that FIB can sputter in a given specimen. The method of evaluating it, developed in this paper, is based on the analysis of a series of scanning electron images of FIB produced nanodots. Results show that the apparent beam size can be up to 5 times larger than the actual physical size of the beam and it is significantly influenced by the presence of gas precursor. It is also demonstrated that the apparent beam size can be used as a reference value for optimisation of the beam step during raster scanning

Redesign methodology for mechanical assembly

Design for assembly is the concept of carrying out critical thought early in the design stage to create assembly easement at the production stage. In the aerospace industry, products have very long lives, frequently being optimised rather than introducing new products. This has meant older products, which are stable income generators, have not benefited from the latest design for assembly methods and manufacturing technology suffers from obsolescence. It has been established that a large percentage of overall product cost is determined at the design stage; thus, existing products suffer from preloaded costs. This paper takes existing design for assembly methodologies and analyses them with respect to the unique challenges involved in legacy product redesign. Several novel factors that contribute to redesign analysis are identified such obsolescence impact and a holistic operation difficulty assessment. A tool is developed to identify potential redesign for assembly projects. The tool is demonstrated through the application of real data and comparing against business decisions. The tool was found to provide a strong indication of where profitable projects may be launched

Conceptual framework for ubiquitous cyber-physical assembly systems in airframe assembly

Current sectoral drivers for the manufacturing of complex products - such as airframe assembly -require new manufacturing system paradigms to meet them. In this paper, we propose a conceptual framework for cyber-physical systems driven by ubiquitous context-awareness by drawing together a unique and coherent vision that merges several extant concepts. This framework leverages recent progress in agent-based systems, exible manufacturing, ubiquitous computing, and metrology-driven robotic assembly in the Evolvable Assembly Systems project. As such, although it is adapted for and grounded in manufacturing facilities for airframe assembly, it is not specifically tailored to that application and is a much more general framework. As well as outlining our conceptual framework, we also provide a vision for assembly grounded in a review of existing research in the area

Focused ion beam milling of brass for microinjection mould fabrication

In this paper focused ion beam (FIB) milling (sputtering) is demonstrated for the fabrication of brass microinjection moulding inserts which have been previously conventionally milled. It is found that FIB milling of the α phase of the material results in much smoother final surfaces than the β phase. An annealing procedure for minimizing the effects of differential sputtering has also been performed. Further with the help of Scanning Electron Microscopy (SEM) and White Light Interferometry (WLI) measurements the FIB milling yield for 70-30 cartridge brass is determined and analysed. Finally, FIB milling of 5µm square trenches with a flat bottom surface is demonstrated

A Learning Method for Automated Disassembly

While joining tolerances, and therefore forces, are known in the assembly process, the determination of disassembly forces is not possible. This is caused by changes in the product properties during the product operation, which has multiple reasons such as thermal or mechanical stress on the product. Regarding the planning of disassembly tasks, disassembly times and tools cannot be planned properly. They have to be determined in the process or stay undefined, which can result in damaging of the product. This article shows an approach to describe the necessary disassembly forces without having to investigate the complex physical influences caused by the usage of the product. To do so, a Learning Method is developed, which is sustained by a Lookup-Table for the estimation of disassembly forces based on basic input data such as hours of operation and operating characteristics. Missing values will be interpolated by using multiple linear regression. The concept will be illustrated in the example of a turbine blade connection

Common shared system model for evolvable assembly systems

A vital aspect of distributed control in an adaptable production system is coherence between each system resource. The Evolvable Assembly Systems project addresses this challenge using a common shared system model. This paper provides an overview of the project and the shared system model approach as implemented in a real world demonstration cell

Tool wear classification using time series imaging and deep learning

Abstract: Tool condition monitoring (TCM) has become essential to achieve high-quality machining as well as cost-effective production. Identification of the cutting tool state during machining before it reaches its failure stage is critical. This paper presents a novel big data approach for tool wear classification based on signal imaging and deep learning. By combining these two techniques, the approach is able to work with the raw data directly, avoiding the use of statistical pre-processing or filter methods. This aspect is fundamental when dealing with large amounts of data that hold complex evolving features. The imaging process serves as an encoding procedure of the sensor data, meaning that the original time series can be re-created from the image without loss of information. By using an off-the-shelf deep learning implementation, the manual selection of features is avoided, thus making this novel approach more general and suitable when dealing with large datasets. The experimental results have revealed that deep learning is able to identify intrinsic features of sensory raw data, achieving in some cases a classification accuracy above 90%

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