Process modeling for robotic polishing
Abstract This paper presents the solutions adopted for a robotic polishing cell for mold manufacturing. Mold finishing is frequently carried out manually; these kinds of operations are iterative, time consuming and require experience. Automation can introduce cost reduction minimizing production times on such manual finishing operations. The research shown in this paper provides an overall solution without discarding aspects that could be relevant to the task considered in the mold making environment. Most of the mold makers usually employ CAD/CAM systems in order to design workpieces and to prepare the CNC programs to manufacture the parts, this CAD data can also be used to prepare programs for the actual robot finishing of these parts. In this work, an automatic planning and programming system based on data from a CAD system is described. The direct use of the paths implies difficulties of adaptation mostly when free-shaped surfaces are treated, so that the use of a constant contact force control has also been considered. To accomplish the automation of the process it is necessary to establish a behavior model of the system’s variables. This fact allows the definition of the task specifications of the tools and, as a result, both the planning system and the control strategy by stages of the polishing process.
A robotic die polishing system through fuzzy neural networks
Abstract This paper proposes a robotic die polishing system which uses multiple sensors to capture the images of the die texture and then decides the polishing direction. For each vision sensor, a multi-net invariant net (MNIN) model, which consists of feedforward artificial neural networks (ANNs) trained by error backpropagation (EBP) learning algorithm and data representation concept, is used for the purpose of pattern recognition. The multiple decisions from different MNINs are integrated together by using the other ANN. In addition, the training parameters, training rate, momentum, and steepness of activation function, of EBP learning algorithm are dynamically changed using three fuzzy models. The proposed system not only discerns patterns and creates strategies for polishing rough-machined dies that initially have a range of unpredictable surface finish due to machining variations including differences from tool changes and spindle vibrations, but is also capable of intergrating multiple sensory information. Moreover, the training speed of EBP learning algorithm is really improved through fuzzy modeling.
Development of CAM system for 5-Axis NC machining of sculptured surfaces
Developed in this research is a CAM system for 5-axis NC Machining of sculptured surfaces. We identify problems in generating 5-axis NC data and propose methods of overcoming them. Issues discussed in this paper are: kinematic modelling of NC machines; determination of cutter position (location and orientation); check of machine work-range; linear trajectory plann- ing ; calculation of feedrate number. The proposed system has been implemented in FORTRAN77 on the Personal IRIS EWS, and it also constitutes a module of the CAD/CAM system 'CASSET' developed in KIST CAD/CAM lab.