SPIDA: Abstracting and generalizing layout design cases

Abstraction and generalization of layout design cases generate new knowledge that is more widely applicable to use than specific design cases. The abstraction and generalization of design cases into hierarchical levels of abstractions provide the designer with the flexibility to apply any level of abstract and generalized knowledge for a new layout design problem. Existing case-based layout learning (CBLL) systems abstract and generalize cases into single levels of abstractions, but not into a hierarchy. In this paper, we propose a new approach, termed customized viewpoint - spatial (CV-S), which supports the generalization and abstraction of spatial layouts into hierarchies along with a supporting system, SPIDA (SPatial Intelligent Design Assistant)

Feature technology and its applications in computer integrated manufacturing

A Thesis submitted for the degree of Doctor of Philosophy of University of LutonComputer aided design and manufacturing (CAD/CAM) has been a focal research area for the manufacturing industry. Genuine CAD/CAM integration is necessary to make products of higher quality with lower cost and shorter lead times. Although CAD and CAM have been extensively used in industry, effective CAD/CAM integration has not been implemented. The major obstacles of CAD/CAM integration are the representation of design and process knowledge and the adaptive ability of computer aided process planning (CAPP). This research is aimed to develop a feature-based CAD/CAM integration methodology. Artificial intelligent techniques such as neural networks, heuristic algorithms, genetic algorithms and fuzzy logics are used to tackle problems. The activities considered include: 1) Component design based on a number of standard feature classes with validity check. A feature classification for machining application is defined adopting ISO 10303-STEP AP224 from a multi-viewpoint of design and manufacture. 2) Search of interacting features and identification of features relationships. A heuristic algorithm has been proposed in order to resolve interacting features. The algorithm analyses the interacting entity between each feature pair, making the process simpler and more efficient. 3) Recognition of new features formed by interacting features. A novel neural network-based technique for feature recognition has been designed, which solves the problems of ambiguity and overlaps. 4) Production of a feature based model for the component. 5) Generation of a suitable process plan covering selection of machining operations, grouping of machining operations and process sequencing. A hybrid feature-based CAPP has been developed using neural network, genetic algorithm and fuzzy evaluating techniques

Geometric Deep Learning for Computer-Aided Design: A Survey

Geometric Deep Learning techniques have become a transformative force in the field of Computer-Aided Design (CAD), and have the potential to revolutionize how designers and engineers approach and enhance the design process. By harnessing the power of machine learning-based methods, CAD designers can optimize their workflows, save time and effort while making better informed decisions, and create designs that are both innovative and practical. The ability to process the CAD designs represented by geometric data and to analyze their encoded features enables the identification of similarities among diverse CAD models, the proposition of alternative designs and enhancements, and even the generation of novel design alternatives. This survey offers a comprehensive overview of learning-based methods in computer-aided design across various categories, including similarity analysis and retrieval, 2D and 3D CAD model synthesis, and CAD generation from point clouds. Additionally, it provides a complete list of benchmark datasets and their characteristics, along with open-source codes that have propelled research in this domain. The final discussion delves into the challenges prevalent in this field, followed by potential future research directions in this rapidly evolving field.Comment: 26 pages, 14 figures, journal articl

Predicting Completion Time for Production Line in a Supply Chain System through Artificial Neural Networks

Completion time in manufacturing sector is the time needed to produce a product through production processes in sequence and it reflects the delivery performance of such company in supply chain system to meet customer demands on time. However, actual completion time always deviated from the standard completion time due to unavoidable factors and consequently affect delivery due date and ultimately lead to customer dissatisfaction. Therefore, this paper predicts completion time based on historical data of production line activities and discovers the most influential factor that contributes to the tardiness or a late jobs due date from its completion time. A well-known company in producing audio speaker is selected as a case company. Based on the review of previous works, it is found that Artificial Neural Networks (ANN) has superior capability in prediction of future occurrence by capturing the underlying relationship among variables through historical data. Besides, ANN is also capable to provide final weight for each of related variable. Variable with the highest value of final weight indicates the most influential variable and should be concerned more to solve completion time issue which has persisted among entities in supply chain system. The obtained result is expected to become an advantageous guidance for every entity in supply chain system to fulfil completion time requirement as requested by customer in order to survive in this turbulent market place

Survey on assembly sequencing: a combinatorial and geometrical perspective

A systematic overview on the subject of assembly sequencing is presented. Sequencing lies at the core of assembly planning, and variants include finding a feasible sequence—respecting the precedence constraints between the assembly operations—, or determining an optimal one according to one or several operational criteria. The different ways of representing the space of feasible assembly sequences are described, as well as the search and optimization algorithms that can be used. Geometry plays a fundamental role in devising the precedence constraints between assembly operations, and this is the subject of the second part of the survey, which treats also motion in contact in the context of the actual performance of assembly operations.Peer ReviewedPostprint (author’s final draft