The use of virtual fixtures, jigs and gauges in dimensional variation analysis simulation models.

This paper describes the use and deployment of, virtual fixtures, jigs and gauges to locate, align and measure features in simulation models used in the dimensional variation analysis (DVA) of assembly systems. The particular example chosen in this paper is a McPherson strut suspension. The correct use of virtual fixtures, jigs and gauges can significantly improve the accuracy and realism of the simulation model and thus the DVA output. In kinematic assembly systems, the use of virtual fixture, jigs and gauges is often essential to produce a working simulation model

The dimensional variation analysis of complex mechanical systems

Dimensional variation analysis (DVA) is a computer based simulation process used to identify potential assembly process issues due the effects of component part and assembly variation during manufacture. The sponsoring company has over a number of years developed a DVA process to simulate the variation behaviour of a wide range of static mechanical systems. This project considers whether the current DVA process used by the sponsoring company is suitable for the simulation of complex kinematic systems. The project, which consists of three case studies, identifies several issues that became apparent with the current DVA process when applied to three types of complex kinematic systems. The project goes on to develop solutions to the issues raised in the case studies in the form of new or enhanced methods of information acquisition, simulation modelling and the interpretation and presentation of the simulation output Development of these methods has enabled the sponsoring company to expand the range of system types that can be successfully simulated and significantly enhances the information flow between the DVA process and the wider product development process

Feature-based hybrid inspection planning for complex mechanical parts

Globalization and emerging new powers in the manufacturing world are among many challenges, major manufacturing enterprises are facing. This resulted in increased alternatives to satisfy customers\u27 growing needs regarding products\u27 aesthetic and functional requirements. Complexity of part design and engineering specifications to satisfy such needs often require a better use of advanced and more accurate tools to achieve good quality. Inspection is a crucial manufacturing function that should be further improved to cope with such challenges. Intelligent planning for inspection of parts with complex geometric shapes and free form surfaces using contact or non-contact devices is still a major challenge. Research in segmentation and localization techniques should also enable inspection systems to utilize modern measurement technologies capable of collecting huge number of measured points. Advanced digitization tools can be classified as contact or non-contact sensors. The purpose of this thesis is to develop a hybrid inspection planning system that benefits from the advantages of both techniques. Moreover, the minimization of deviation of measured part from the original CAD model is not the only characteristic that should be considered when implementing the localization process in order to accept or reject the part; geometric tolerances must also be considered. A segmentation technique that deals directly with the individual points is a necessary step in the developed inspection system, where the output is the actual measured points, not a tessellated model as commonly implemented by current segmentation tools. The contribution of this work is three folds. First, a knowledge-based system was developed for selecting the most suitable sensor using an inspection-specific features taxonomy in form of a 3D Matrix where each cell includes the corresponding knowledge rules and generate inspection tasks. A Travel Salesperson Problem (TSP) has been applied for sequencing these hybrid inspection tasks. A novel region-based segmentation algorithm was developed which deals directly with the measured point cloud and generates sub-point clouds, each of which represents a feature to be inspected and includes the original measured points. Finally, a new tolerance-based localization algorithm was developed to verify the functional requirements and was applied and tested using form tolerance specifications. This research enhances the existing inspection planning systems for complex mechanical parts with a hybrid inspection planning model. The main benefits of the developed segmentation and tolerance-based localization algorithms are the improvement of inspection decisions in order not to reject good parts that would have otherwise been rejected due to misleading results from currently available localization techniques. The better and more accurate inspection decisions achieved will lead to less scrap, which, in turn, will reduce the product cost and improve the company potential in the market