Tutorials for Integrating CAD/CAM in Engineering Curricula

This article addresses the issue of educating engineering students with the knowledge and skills of Computer-Aided Design and Manufacturing (CAD/CAM). In particular, three carefully designed tutorials—cutting tool offsetting, tool-path generation for freeform surfaces, and the integration of advanced machine tools (e.g., hexapod-based machine tools) with solid modeling—are described. The tutorials help students gain an in-depth understanding of how the CAD/CAM-relevant hardware devices and software packages work in real-life settings. At the same time, the tutorials help students achieve the following educational outcomes: (1) an ability to apply the knowledge of mathematics, science, and engineering; (2) an ability to design a system, component, or process to meet the desired needs, (3) an ability to identify, formulate, and solve engineering problems; and (4) an ability to use the techniques, skills, and modern engineering tools that are necessary for engineering practice. The tutorials can be modified for incorporating other contemporary issues (e.g., additive manufacturing, reverse engineering, and sustainable manufacturing), which can be delved into as a natural extension of this study

Knowledge extraction from time series and its application to surface roughness simulation, Information, Knowledge, and Systems Management

Time series data frequently occurs in business, medicine, manufacturing, science, and other fields. Extraction of knowledge from time series data is therefore an important research topic for systems engineers. Various methods have so far been proposed wherein it is typically assumed that a piece of time series data possesses a set of trends that deterministically or stochastically repeat in time. However, for noisy time series data (data having no trend) the delay maps (return maps) (x(t),x(t+delta)), t = 0,1,…, delta = 1,2,…,N (N is a small integer), are more informative than the time series itself. This paper shows a knowledge extraction method that extracts a small set of “if…then… ” rules from the return maps of a given time series data. A JAVA ™ based tool is developed to automate the rule extraction process. This tool is also able to use the extracted rules recursively to simulate the qualitatively similar time series. The performance of the proposed knowledge extraction method (as well as the tool) is demonstrated by using an example time series (surface roughness profile of a machined surface). This exemplification demonstrates that the proposed knowledge extraction method can be used to enhance the performance of computer integrated manufacturing systems by giving those systems a means to exchange the information of nonlinear behaviors among the subsystems (process planning, quality control, and so on). Keywords

Recent developments in the application of machine-learning towards accelerated predictive multiscale design and additive manufacturing

The application of three-dimensional (3D) printing/Additive Manufacturing (AM) for developing multi-functional smart/intelligent composite materials is a highly promising area of engineering research. However, there is often no reliable means for predicting and modelling the material performance, and the wide-scale industrial adoption of AM is limited due to factors such as design barriers, limited materials library, processing defects and inconsistency in product quality. A comprehensive framework considering the generalised applicability of ML algorithms at sub-sequent stages of the AM process from the initial design to the post-processing stages in the literature is lacking. In this paper, the integration of various ML applications at various sub-processes is discussed, including pre-processing design stage, parameter optimisation, anomaly detection, in-situ monitoring, and the final post-processing stages. The challenges and potential solutions for standardising these integrated techniques have been identified. The article is promising for professionals and researchers in AM and AI/ML techniques

Fractals and additive manufacturing

Fractal geometry can create virtual models of complex shapes as CAD data, and from these additive manufacturing can directly create physical models. The virtual-model-building capacity of fractal geometry and the physical-model-building capacity of additive manufacturing can be integrated to deal with the design and manufacturing of complex shapes. This study deals with the manufacture of fractal shapes using commercially available additive manufacturing facilities and 3D CAD packages. Particular interest is paid to building physical models of an IFS-created fractal after remodeling it for manufacturing. This article introduces three remodeling methodologies based on binary-grid, convex/concave-hull, and line-model techniques. The measurements of the manufactured fractal shapes are also reported, and the degree of accuracy that can be achieved by the currently available technology is shown. © 2016, Fuji Technology Press. All rights reserved