2 resultados para product development process


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The stretch blow moulding (SBM) process is the main method for the mass production of PET containers. And understanding the constitutive behaviour of PET during this process is critical for designing the optimum product and process. However due to its nonlinear viscoelastic behaviour, the behaviour of PET is highly sensitive to its thermomechanical history making the task of modelling its constitutive behaviour complex. This means that the constitutive model will be useful only if it is known to be valid under the actual conditions of interest to the SBM process. The aim of this work was to develop a new material characterization method providing new data for the deformation behaviour of PET relevant to the SBM process. In order to achieve this goal, a reliable and robust characterization method was developed based on an instrumented stretch rod and a digital image correlation system to determine the stress-strain relationship of material in deforming preforms during free stretch-blow tests. The effect of preform temperature and air mass flow rate on the deformation behaviour of PET was also investigated.

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This paper examines the integration of a tolerance design process within the Computer-Aided Design (CAD) environment having identified the potential to create an intelligent Digital Mock-Up [1]. The tolerancing process is complex in nature and as such reliance on Computer-Aided Tolerancing (CAT) software and domain experts can create a disconnect between the design and manufacturing disciplines It is necessary to implement the tolerance design procedure at the earliest opportunity to integrate both disciplines and to reduce workload in tolerance analysis and allocation at critical stages in product development when production is imminent.
The work seeks to develop a methodology that will allow for a preliminary tolerance allocation procedure within CAD. An approach to tolerance allocation based on sensitivity analysis is implemented on a simple assembly to review its contribution to an intelligent DMU. The procedure is developed using Python scripting for CATIA V5, with analysis results aligning with those in literature. A review of its implementation and requirements is presented.