V. Sundararajan

CyberCut: an internet-based CAD/CAM system

“CyberCut TM ” is a testbed for an Internet-based CAD/CAM system. It was specifically designed to be a networked, automated system, with a seamless communication flow from a client-side designer to a server-side machining service. The creation of CyberCut required several new software modules. These include: a) a Web-based design tool in which Design-forManufacturing information and machining rules constrain the designer to manufacturable parts; b) a geometric representation called SIF-DSG, for unambiguous communication between the client-side designer and the server-side process planner; c) an automated process planning system with several sub-modules that convert an incoming design to a set of tool-paths for execution on a 3-axis CNC milling machine. Using this software-pipeline, a CyberCut service, modeled on the MOSIS service for VLSI chips, has been now been launched for limited studentuse at a group of cooperating universities.

Volumetric feature recognition for machining components with freeform surfaces

Abstract This paper describes a procedure for the extraction of features of a part containing a combination of 2.5D features and freeform surfaces. This work invokes a previous algorithm that was designed to recognize machining features from 2.5D parts destined to be machined on a 3-axis milling machine. The essence of that algorithm was a volume decomposition based on a recursive descent into the part, yielding a feature graph that captured both the geometry and the spatial relationships of the features. This work augments the previous algorithm with the ability to handle a limited class of components having freeform surfaces. Freeform features are defined similar to the 2.5D features as comprising a planar contour, but substituting a bottom freeform surface for the depth. Covering faces, defined as projection of the freeform surface on the faces of the bounding box of the surface, are used as equivalent planar faces for performing the recursive descent. Inter-feature open edges are used to signal the relationship between the freeform feature and other neighboring features. Examples of molds and components that were machined using the proposed algorithms are also presented.

Identification of Multiple Feature Representations by Volume Decomposition for 2.5-Dimensional Components

Decomposition of computer-aided design models into features that can be directly manufactured and easily translated from one representation to another has been recognized as a necessity for robust automated process planning systems, The algorithms presented here yield multiple representations of features that can be used to generate plans that are easily and efficiently manufacturable by milling processes. Features are recognized from the faces of a prismatic stock by first identifying missing regions on the external faces of the stock and then recursively descending into the part. Each missing region corresponds to a feature. As the projection of the feature is swept into the part, changes in the cross-section are identified. These denote the beginning of new features which become children of the original feature. This process yields a set of six trees, each of which resembles a depth-first search tree and is a partial ordering of features within a setup. Multiple representations are investigated for those features that are accessible from more than one direction. Corresponding features in the different trees are linked by arcs, thus yielding a feature graph. The feature graph can then be used to generate optimal features for machining depending on design or manufacturing requirements such as fixtures, tolerances, corner radii, and tool accessibility.

Distributed Monitoring of Steady-State System Performance Using Wireless Sensor Networks

Wireless sensor networks provide a cost-effective alternative to monitoring system performance in real-time. In addition to the ability to communicate data without wires, the sensor nodes possess computing and memory capabilities that can be harnessed to execute signal processing and state-tracking algorithms. This paper describes the architecture and application layer protocols for the distributed monitoring of the steady-state performance of systems that have a finite number of states. Protocols are defined for two phases — the learning phase and the monitoring phase. In the learning phase, an expert user trains the wireless network to define the acceptable states of the system. The nodes are programmed with a set of algorithms for processing their readings. The nodes use these algorithms to compute invariant metrics on the sensor readings, which are then used to define the internal state of the node. In the monitoring phase, the nodes track their individual states by computing their state based on the sensor readings and then comparing them with the pre-determined values. If the system properties change, the nodes communicate with each other to determine the new state. If the new state is not one of the acceptable states determined in the learning phase, an alert is raised. This approach de-centralizes the monitoring and detection process by distributing both the state information and the computing throughout the network. The paper presents algorithms for the various processes of the system and also the results of testing the sensor network architecture on real-time models. The sensor network can be used in automotive engine test rigs to carry out long term performance analysis.© 2004 ASME

Energy Scavenging in Support of Ambient Intelligence: Techniques, Challenges, and Future Directions

Ambient intelligence (AmI) depends on the existence of vast quantities of wireless sensors distributed throughout the environment. While advances in IC fabrica- tion technologies, circuit designs, and networking techniques have greatly reduced the cost, size, and power consumption of potential wireless sensor platforms, the development of suitable power sources for many applications lags. The purpose of this chapter is both to review technologies for scavenging energy from the environment to power wireless sensors, and to discuss challenges and future research directions for vibration-based energy scaven- ging. Many potential energy scavenging technologies are presented along with current state of research and theoretical maximum power densities. Special focus is given to scavenging energy from mechanical vibrations extant in many environments. Results from vibration- based energy scavengers using piezoelectric structures developed by the authors are pre- sented demonstrating power production of approximately 375 mW=cm 3 . While wireless sensor nodes have successfully been powered by vibration-based energy scavengers, several improvements are possible, and indeed necessary, for widespread deployment. Three areas

Applications of Software Engineering to Manufacturing Process Planning

Agile methods of software development promote the use of flexible architectures that can be rapidly refactored and rebuilt as necessary for the project. In the mechanical engineering domain, software tends to be very complex and requires the integration of several modules that result from the efforts of large numbers of programmers over several years. Such software needs to be extensible, modular, and adaptable so that a variety of algorithms can be quickly tested and deployed. This paper presents an application of the unified process (UP) to the development of a research process planning system called CyberCut. UP is used to (I) analyze and critique early versions of CyberCut and (2) to guide current and future developments of the CyberCut system. CyberCut is an integrated process planning system that converts user designs to instructions for a computer numerical control (CNC) milling machine. The conversion process involves algorithms to perform tasks such as feature extraction, fixture planning, tool selection, and tool-path planning. The UP-driven approach to the development of CyberCut involves two phases. The inception phase outlines a clear but incomplete description of the user needs. The elaboration phase involves iterative design, development, and testing using short cycles. The software makes substantial use of design patterns to promote clean and well-defined separation between and within components to enable independent development and testing. The overall development of the software tool took about two months with five programmers. It was later possible to easily integrate or substitute new algorithms into the system so that programming resources were more productively used to develop new algorithms. The experience with UP shows that methodologies such as UP are important for engineering software development where research goals, technology, algorithms, and implementations show dramatic and frequent changes.

Design System for Composite Transmission Error Prediction for Automatic Transmissions

Noise in vehicular automatic transmissions is a complex phenomenon involving several interacting factors. One of the contributing factors to noise for a single pair of meshing gears has been shown to be the transmission error. The transmission error (TE) is defined in terms of deviation of the speed ratio from the ideal speed ratio. It has since been hypothesized that the composite transmission error in a planetary system would be the key contributor to noise in automatic transmissions. This composite error would have to include the contributions from individual meshes and account for the configuration of the transmission system. This paper describes a design system that enables engineers to predict and study effects of parameter variation on the composite transmission error. The designer first specifies the configuration of the transmission using canonical graphs. The graph contains the elements such as gears, clutches and brakes of the transmission system as its nodes and the relationship among them for the edges. The design system uses the graph to solve for the speeds and torques. The transmission errors for the individual meshes are computed and then combined into the composite transmission error using a simple average.© 2004 ASME