Powertrain Assembly Lines Automatic Configuration Using a Knowledge Based Engineering Approach

Technical knowledge and experience are intangible assets crucial for competitiveness. Knowledge is particularly important when it comes to complex design activities such as the configuration of manufacturing systems. The preliminary design of manufacturing systems relies significantly on experience of designers and engineers, lessons learned and complex sets of rules and is subject to a huge variability of inputs and outputs and involves decisions which must satisfy many competing requirements. This complicated design process is associated with high costs, long lead times and high probability of risks and reworks. It is estimated that around 20% of the designer’s time is dedicated to searching and analyzing past available knowledge, while 40% of the information required for design is identified through personally stored information. At a company level, the design of a new production line does not start from scratch. Based on the basic requirements of the customers, engineers use their own knowledge and try to recall past layout ideas searching for production line designs stored locally in their CAD systems [1]. A lot of knowledge is already stored, and has been used for a long time and evolved over time. There is a need to retrieve this knowledge and integrate it into a common and reachable framework. Knowledge Based Engineering (KBE) and knowledge representation techniques are considered to be a successful way to tackle this design problem at an industrial level. KBE is, in fact, a research field that studies methodologies and technologies for capturing and re-using product and process engineering knowledge to achieve automation of repetitive design tasks [2]. This study presents a methodology to support the configuration of powertrain assembly lines, reducing design times by introducing a best practice for production systems provider companies. The methodology is developed in a real industrial environment, within Comau S.p.A., introducing the role of a knowledge engineer. The approach includes extraction of existing technical knowledge and implementation in a knowledge-based software framework. The macro system design requirements (e.g. cycle time, production mix, etc.) are taken as input. A user driven procedure guides the designer in the definition of the macro layout-related decisions and in the selection of the equipment to be allocated within the project. The framework is then integrated with other software tools allowing the first phase design of the line including a technical description and a 2D and 3D CAD line layout. The KBE application is developed and tested on a specific powertrain assembly case study. Finally, a first validation among design engineers is presented, comparing traditional and new approach and estimating a cost-benefit analysis useful for future possible KBE implementations

A manufacturing model to enable knowledge maintenance in decision support systems

The product development process, within a typical manufacturing company, utilises huge amounts of knowledge related to manufacturing and design activities. Knowledge based systems are increasingly being used to support manufacturing and design decisions. These systems are important tools for obtaining a competitive advantage and leverage using company "know-how". However, it is important to define suitable knowledge structures in the creation of these decision support systems. Due to the significant volume of knowledge generated in the manufacturing and design stage, there is a need to create structures and methods that readily manage and maintain the knowledge in order to a) assure the long-term use of these systems b) improve the company's competitiveness. The research reported in this thesis explores and defines a Manufacturing Facility Information and Knowledge Model (MFIKM) allowing a) the ability to store and manage various types of knowledge, b) the capturing of valuable new knowledge using a knowledge maintenance method. The understanding of an information and knowledge infrastructure using different types of knowledge categorisation has been explored. The major emphasis has been placed on understanding the facility knowledge structure related to processes and resources supporting process planning decisions. Using a knowledge maintenance life cycle as a method to maintain knowledge, it was possible to capture new and valuable machining knowledge using different types of representations. Knowledge models and methods are essential in the definition of structures to support manufacturing decisions allowing knowledge management and maintenance. It has been shown that the knowledge structures defined for the new model can serve as a source and repository for different types of knowledge allowing the support of manufacturing decisions with up-to-date knowledge. The framework defined enables the structuring of facility knowledge, processes, and resources, as super classes; improving the understanding of the relationships and dependencies among them, and allowing accessibility depending on the characteristics of each. A UML tool helped in the creation of new structures detailing attributes for the classes defined. An experimental system has been implemented using the object-oriented database ObjectStore© and the Visual C++ programming environment. The MFIKM has been explored using scenarios from machining knowledge to successfully demonstrate the feasibility of knowledge maintenance supporting process planning decisions using the knowledge structures defined.EThOS - Electronic Theses Online ServiceGBUnited Kingdo