A modular hybrid simulation framework for complex manufacturing system design

For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated

Current practice and challenges towards handling uncertainty for effective outcomes in maintenance

The combination of viable heuristic attributes with statistical measurements presents significant challenges in industrial maintenance for complex assets under through-life service contracts. Techniques to obtain and process heuristic attributes raise numerous uncertainties which often go undefined and unmitigated. A holistic view of these uncertainties may improve decision-making capabilities and reduce maintenance costs and turnaround time. It is therefore necessary to identify and rank factors that influence uncertainties originating from challenges in the above context. This, along with an identification of who contributes to such challenges and current practice to handle them, sets the focus for this study. The influence of 32 categorised factors on uncertainty is assessed through a questionnaire completed by nine experienced maintenance managers from a leading defence company. The pedigree approach is applied to score validity of respondents’ answers according to their experience and job role to normalise scores. Results are discussed in interviews with respondents along with current practice in and ways to improve uncertainty assessment. Scores are weighted through the Analytical Hierarchy Process (AHP) in order to identify the most influential factors on uncertainty in maintenance. The analysis revealed that these include: intellectual property rights (IPR), maintainer performance, quality of information, resistance to change, stakeholder communication and technology integration. These are verified with 40 practitioners from various industrial backgrounds. From the interviews, it is deemed that a holistic view of heuristic and statistical attributes ultimately allows for more accomplished decision-making but requires trade-offs between quality and cost over the asset’s life cycle

A framework for cost evaluation in product service system configuration

Configuration systems are increasingly used as a means for efficient design of customised product service systems (PSS) to satisfy diverse customer needs. Cost evaluation is thereby important to assist the configuration engineers in making decisions on feasible configuration solutions. However, little research attention has been received until recently. To fill this gap, this paper contributes in developing a framework for cost evaluation in PSS configuration. A holistic view of PSS configuration, the three-dimensional PSS cost element, and a life cycle-oriented cost evaluation approach are successively proposed. The framework is thereby established with a number of parts, including the preparatory stage, the evaluation stage and the configuration stage. A pump PSS is illustrated to validate the developed framework. Four feasible configuration solutions in one configuration activity are evaluated and compared. The configuration engineers are thus assisted with the decision on selecting the one with the least cost

Conceptualising the impact of information asymmetry on through-life cost: case study of machine tools sector

Information asymmetry (IA) in terms of contextual variety and importance is one of the most challenging aspects of through-life costing in product-service systems (PSS). IA is an imbalance in the information, data and knowledge shared among the parties involved in a contractual agreement. In manufacturing systems under PSS, interaction and effective communication among several parties who are involved in a contractual agreement, rely on the continuity and accuracy of information and context. In such systems, contextual variety exhibits complexity and uncertainty in through-life costing and subsequently in PSS cost assessment. Although the economic aspect of PSS has been studied previously, the impact of IA on through-life cost and for different PSS solutions has not been detailed. Considering manufacturing value chains, this paper introduces a new concept of PSS-hierarchy to perform through-life costing in the presence of IA for various PSS solutions. Moreover, this paper proposes a generic life-cycle model for different PSS solutions to assess the total cost of ownership (TCO). The proposed model has been developed to support decisions on contract design in manufacturing systems. This study considers the manufacturer, service provider and customer perspectives to develop the TCO model using a machine tool manufacturing case study

A systematic review of augmented reality applications in maintenance

Augmented Reality (AR) technologies for supporting maintenance operations have been an academic research topic for around 50 years now. In the last decade, major progresses have been made and the AR technology is getting closer to being implemented in industry. In this paper, the advantages and disadvantages of AR have been explored and quantified in terms of Key Performance Indicators (KPI) for industrial maintenance. Unfortunately, some technical issues still prevent AR from being suitable for industrial applications. This paper aims to show, through the results of a systematic literature review, the current state of the art of AR in maintenance and the most relevant technical limitations. The analysis included filtering from a large number of publications to 30 primary studies published between 1997 and 2017. The results indicate a high fragmentation among hardware, software and AR solutions which lead to a high complexity for selecting and developing AR systems. The results of the study show the areas where AR technology still lacks maturity. Future research directions are also proposed encompassing hardware, tracking and user-AR interaction in industrial maintenance is proposed

Augmented Reality in Maintenance: An information-centred design framework

Augmented Reality (AR) visualization capabilities can impact on maintenance. From enhancing performance to retrieving feedback, AR can close the information loop between maintenance information systems and the operations supported. Though, the design of AR applications is not aligned with current information systems, which prevents maintenance information to be used and improved properly. In this paper, industrial collaboration contributed to determine a framework for AR integration in maintenance systems. The framework describes information types, formats and interactions modes for AR to enhance efficiency improvements in maintenance of complex equipment. Semi-structured interviews and surveys with maintainers were conducted to determine the maintenance challenges and also to validate the framework proposed. Therefore, exposing future research in topics such as multimodal interaction, information contextualization and performance analysis to achieve the complete integration of AR in maintenance

Development of an Augmented Reality Equipped Composites Bonded Assembly and Repair for Aerospace Applications

The prosperity of aircraft transportation together with revolutionary promotion of composite components used in commercial aircraft pose enormous challenges to the aircraft composite assembly and repair (especially uncertainties associated with polymer process parameters control, barely visible defects and non-destructive inspection capability to inspect zero-thickness defects). Therefore, an industry solution owning merits on reliability and repeatability of assembly process is at high demand. Augmented Reality (AR), a human computer interaction technology, possesses its exclusive superiority on its capability of inflicting digital mock-up into physical environment. The above property of AR provides colossal opportunities to be utilised into industrial applications to contribute the realisation of automated, efficient, streamlined and reliable process and assembly. The current ongoing research aims at developing an AR System integrated into aircraft composite bonded assembly and repair as a guidance tool to instruct technicians’ repairing operation, mitigate human errors, and reduce duration of repair and assembly. Upon the accomplishment of the System, the researchers would aim to investigate the incorporation of machine learning and deep network algorithms to enable and significantly improve the interactions between the multitude of process parameters involved in the composites assembly control procedures, solely relying upon the AR geometric data. This will ultimately lead to dramatic reduction of sensors in aircraft assembly, mitigation of in-process analysis time, reduction of process and post-process inspection time, and a higher quality assembly. Stepped scarf composite repair embedded with soft composite patches was selected as the archetype to be brought into effect though hard patches were partially examined as well. The AR System has focused on composite patches assembly and vacuum bagging process to address the predicament of miscellaneous steps and fibre directions

An effective uncertainty based framework for sustainable industrial product-service system transformation

Industrial Product-Service Systems (IPS2) can provide insights to enhance the environmental sustainability and lower environmental impact. However, its successful realisation for preventing the production of waste, while increasing efficiencies in the uses of energy and human capital remains a highly convoluted problem. This research article aims to address this issue by presenting an innovative uncertainty-based framework that can be used to assist in achieving increased sustainability within the context of IPS2. The developed framework explains the drivers for decision-making and cost to enable sustainability improvements in transforming to industrial services. This is based on academic literature, and multiple case studies of seven industrial companies with over 30 h of semi-structured interviews. The validation of the framework through two case studies demonstrates that uncertainty management can enable resource efficiency and offer sustainable transformation to service provision

Cost uncertainty management and modelling for industrial product-service systems

Globally manufacturing based industries are typically transforming operations to enhance the delivery of services throughout equipment use. Within the defence industry, Contracting for Availability (CfA) has emerged as an approach that is increasingly dominating the interation between the customer and the manufacturers. This application serves as an example for an Industrial Product-Service System, and sets the context to this research. Predicting the delivery of services, particularly at the bidding stage, creates enhanced complexity and unpredictability in costs due to uncertainties. Driven by this contextual challenge the aim of this research is to develop a framework for cost uncertainty management and modelling at the bidding stage of CfA in the defence industry. The thesis presents the existing literature associated to uncertainty in cost estimation, whilst the current practice is demonstrated based on interaction with seven organisations involved in the defence industry. A software prototype, Uncertainty Tool for Assessment and Simulation of Cost (U-TASC), has been developed to implement an integrated cost uncertainty management and modelling framework. The cost uncertainty management framework offers a systematic procedure at the bidding stage to guide subject matter experts to focus the attention on influential uncertainties, while also proposing suitable mitigation strategies. In contrast, the cost uncertainty modelling framework involves a step by step procedure to make use of subjective opinion collated from subject matter experts to reflect the influence of uncertainty in cost estimates. The thesis also presents an agent based model that takes into account the influence of dynamic uncertainty (e.g. failure rate) on cost estimates over time. This is applied within a service supply network, where the interaction between the stakeholders represents a typical CfA with incentives and risk sharing scenarios. The frameworks embedded in U-TASC are validated and verified through three case studies including, a naval radar, aircraft carrier, and naval electronic system. The outcomes indicate that reliable and useful results are generated and the tool is highly applicable. On the other hand, the framework for the agent based model is validated through expert opinion and a pilot case study in the naval domain.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Quantifying uncertainty in pulsed thermographic inspection by analysing the thermal diffusivity measurements of metals and composites

Pulsed thermography has been used significantly over the years to detect near and subsurface damage in both metals and composites. Where most of the research has been in either improving the detectability and/or its applicability to specific parts and scenarios, efforts to analyse and establish the level of uncertainty in the measurements have been very limited. This paper presents the analysis of multiple uncertainties associated with thermographic measurements under multiple scenarios such as the choice of post-processing algorithms; multiple flash power settings; and repeat tests on four materials, i.e., aluminium, steel, carbon-fibre reinforced plastics (CFRP) and glass-fibre reinforced plastics (GFRP). Thermal diffusivity measurement has been used as the parameter to determine the uncertainty associated with all the above categories. The results have been computed and represented in the form of a relative standard deviation (RSD) ratio in all cases, where the RSD is the ratio of standard deviation to the mean. The results clearly indicate that the thermal diffusivity measurements show a large RSD due to the post-processing algorithms in the case of steel and a large variability when it comes to assessing the GFRP laminates