Machine utilisation and breaksdown modelling for measuring productivity using virtual engineering simulation modelling

Results accuracy and reliability of discrete event simulation (DES) models to predict the production line productivities are based on the unexpected breakdowns taken place by machine faults or human errors. Process modeller practices DES modelling to incorporate these breakdowns and corresponding mainte-nances up-to the machine level. But actually breakdowns are potentially taken place at process level com-ponents inside the machine/stations. Domains like Virtual Engineering (VE) allow user to emulate the ac-tual machine build from components using the CAD data and thus define the components level processes model exist inside the machine station. Therefore author came with idea to integrate VE and DES model up-to component level processes to get an improved simulation modelling to analyse the machines breakdowns for validating pre-build and after-build phases of machine development. Initially in this arti-cle it was proposed to produce an algorithm required to integrate and model the component–level DES model driven from the available emulated data models

Product to process lifecycle management in assembly automation systems

Presently, the automotive industry is facing enormous pressure due to global competition and ever changing legislative, economic and customer demands. Product and process development in the automotive manufacturing industry is a challenging task for many reasons. Current product life cycle management (PLM) systems tend to be product-focussed. Though, information about processes and resources are there but mostly linked to the product. Process is an important aspect, especially in assembly automation systems that link products to their manufacturing resources. This paper presents a process-centric approach to improve PLM systems in large-scale manufacturing companies, especially in the powertrain sector of the automotive industry. The idea is to integrate the information related to key engineering chains i.e. products, processes and resources based upon PLM philosophy and shift the trend of product-focussed lifecycle management to process-focussed lifecycle management, the outcome of which is the Product, Process and Resource Lifecycle Management not PLM only

Human system analysis for productivity indicators using virtual engineering simulation modelling

Continuous efforts are required to incorporate the human related variances when, to design human system. So far discrete event simulation techniques are used to document partially the human performance with respect to productivity using assumed and estimated data. This article proposes a new approach to enhance the estimated data used in discrete event simulation(DES) models with data available from virtual engineering(VE) models. Virtual models emulate the entire human and machine interacted processes, using the early available CAD data during production line design. Developing an integration between virtual engineering environment with DES model could help to validate and analyse the human system in modern manufacturing systems well before their physical appearance for productivities indicators. The on-going research particularly defines the algorithm used to model the human behaviour and model the probabilistic data available from the past activities to the discrete event simulation models through virtual engineering domain

Supplementary information files for 'A variability taxonomy to support automation decision-making for manufacturing processes'

Supplementary information files for 'A variability taxonomy to support automation decision-making for manufacturing processes'Abstract:Although many manual operations have been replaced by automation in the manufacturing domain, in various industries skilled operators still carry out critical manual tasks such as final assembly. The business case for automation in these areas is difficult to justify due to increased complexity and costs arising out of process variabilities associated with those tasks. The lack of understanding of process variability in automation design means that industrial automation often does not realise the full benefits at the first attempt, resulting in the need to spend additional resource and time, to fully realise the potential. This article describes a taxonomy of variability when considering automation of manufacturing processes. Three industrial case studies were analysed to develop the proposed taxonomy. The results obtained from the taxonomy are discussed with a further case study to demonstrate its value in supporting automation decision-making.</div