Application of multivariate statistical process control to fuel cell manufacturing

Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.

Design, development and testing of a CAD integrated design for environment software tool.

The research described in this thesis was developed as part o f the Information Management for Green Design (IMA GREE) Project. The 1MAGREE Project was founded by Enterprise Ireland under a Strategic Research Grant Scheme as a partnership project between Galway Mayo Institute o f Technology and C1MRU University College Galway. The project aimed to develop a CAD integrated software tool to support environmental information management for design, particularly for the electronics-manufacturing sector in Ireland.

Development of a disassembly methodology for DFE

The research described in this thesis was developed as part of the Information Management for Green Design (IMAGREE) Project. The IMAGREE Project was funded by Enterprise Ireland under Strategic Research Grant Scheme as a partnership project between Galway-Mayo Institute of Technology and CIMRU University of Galway. The project aimed to develop a CAD integrated software tool to support environmental information management for design.

An analysis of the use of photogrammetric sorting to audit construction and demolition waste production on site

This study analyses the area of construction and demolition waste (C & D W) auditing. The production of C&DW has grown year after year since the Environmental Protection Agency (EPA) first published a report in 1996 which provided data for C&D W quantities for 1995 (EPA, 1996a). The most recent report produced by the EPA is based on data for 2005 (EPA, 2006). This report estimated that the quantity of C&DW produced for that period to be 14 931 486 tonnes. However, this is a ‘data update’ report containing an update on certain waste statistics so any total provided would not be a true reflection of the waste produced for that period. This illustrates that a more construction site-specific form of data is required. The Department of Building and Civil Engineering in the Galway-Mayo Institute of Technology have carried out two recent research projects (Grimes, 2005; Kelly, 2006) in this area, which have produced waste production indicators based on site-specific data. This involved the design and testing of an original auditing tool based on visual characterisation and the application of conversion factors. One of the main recommendations of these studies was to compare this visual characterisation approach with a photogrammetric sorting methodology. This study investigates the application of photogrammetric sorting on a residential construction site in the Galway region. A visual characterisation study is also carried out on the same project to compare the two methodologies and assess the practical application in a construction site environment. Data collected from the waste management contractor on site was also used to provide further evaluation. From this, a set of waste production indicators for new residential construction was produced: □ 50.8 kg/m2 for new residential construction using data provided by the visual characterisation method and the Landfill Levy conversion factors. □ 43 kg/m2 for new residential construction using data provided by the photogrammetric sorting method and the Landfill Levy conversion factors. □ 23.8 kg/m2 for new residential construction using data provided by Waste Management Contractor (WMC). The acquisition of the data from the waste management contractor was a key element for testing of the information produced by the visual characterisation and photogrammetric sorting methods. The actual weight provided by the waste management contractor shows a significant difference between the quantities provided.