Capturing the strategy followed during a coordinate measuring machine (CMM) inspection planning session has been an extremely challenging issue due to the time-consuming nature of traditional methods, such as interviewing experts and technical documents data mining. This paper presents a methodology demonstrating how a motion capture-based system can facilitate direct and nonintrusive CMM operator logging for capturing planning strategies and representing in knowledge formats. With the use of recorded motion data, embedded knowledge and expertise can be captured automatically and formalized in various formats such as motion trajectory graphs, inspection plans, integrated definition (IDEF) model diagrams, and other representations. Additionally, a part program can be generated for driving a CMM to execute component measurement. The system's outputs can be used to help understand how a CMM inspection strategy is planned, as well as training aids for inexperienced operators and the rapid generation of part programs.
One of the most challenging tasks throughout the development and manufacturing of a product is the capturing and formalization of engineering knowledge and expertise. In the past, many researchers have successfully proposed different techniques for capturing knowledge during the design, process and assembly planning of a product. However, few efforts have focused on applying knowledge capture to the task of product verification for Coordinate Measuring Machine (CMM) inspection; most of these are manual, obtrusive for the user and time consuming since the main sources of knowledge come from documentation such as handbooks, guides or interview transcripts. This paper describes a tool for the automated logging of a planner’s actions while carrying out an inspection planning task in a virtual CMM measurement environment. The tool involves a combination of 3D motion tracking and a post-processor to decipher the context strategy in the form of an inspection plan. Various representations of a captured strategy will benefit CMM operators by providing them a tool for: understanding planning strategies, better training methods for inexperienced users and producing more efficient part programs in a shorter time.
The major objective of this study is the estimation of the environmental impacts of a company’s manufacturing system. For this purpose, environmental key performance indicators are selected related to the energy consumption and pollutant gas emissions. To obtain accurate results and achieve an evaluation of the production system, a discrete event simulation tool was equipped. In this way, the production processes of a company were modelled and simulated in order to be assessed with regards to their environmental performance and impacts. The assessment of the investigated manufacturing system was carried out, by comparing it against a hypothetical ideal system of 100% efficiency in order for potential reductions in energy consumption and carbon emissions to be identified. Additionally, a supplementary use of the proposed methodology is presented showing that modelling the production system in advance a company can save energy and associated costs as well as reduce carbon emissions.
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