Path planning for robot-assisted grinding processes

[[abstract]]Path planning for a robot-assisted surface finishing system with an active torque controller is presented. We utilize a dexterous manipulator to attain the desired position and orientation in three-dimensional space during finishing processes. A single-axis active controller consists of a DC motor and a software observer is attached to the robot wrist and used to actuate a pneumatic hand-grinder. The torque observer is designed to sense the grinding contact force based on the driving current and output position of the motor. Zigzag and fractal paths on curved surfaces are designed for the grinding processes. In order to determine an ideal grinding condition, Taguchi's method for experimental design is utilized. We choose four grinding conditions, namely, path pattern, grinding contact pressure, tool diameter, and feed rate. Tendencies of these factors can be found from the experiments. In this research, the prototype of a robot-assisted finishing system is constructed and tested on a Tatung A330 robot. The experimental results show that the robot-assisted finishing system functions well under a variety of grinding conditions.[[conferencetype]]國際[[conferencedate]]20010521~20010526[[conferencelocation]]Seoul, Kore

[[alternative]]Robot-Assisted Grinding and Polishing of Specimens with Free-Form Surfaces

計畫編號：NSC89-2212-E032-008研究期間：200008~200107研究經費：522,000[[sponsorship]]行政院國家科學委員

Vision-assisted robotic finishing of friction stir-welded corner joints

One required process in the fabrication of large components is welding, after which there may be a need for machining to achieve final dimensions and uniform surfaces. Friction stir-welding (FSW) is a typical example after which a series of deburring and grinding operations are carried out. Currently, the majority of these operations are carried out either manually, by human workers, or on machine tools which results in bottlenecks in the process flows. This paper presents a robotic finishing system to automate the finishing of friction stir-welded parts with minimum human involvement. In a sequence, the system can scan and reconstruct the 3D model of the part, localise it in the robot frame and generate a suitable machining path accordingly, to remove the excess material from FSW without violating process constraints. Results of the cutting trials carried out for demonstration have shown that the developed system can consistently machine the corner joints of an industrial scale part to desired surface quality which is around 1.25 μm in, Ra, the arithmetic average of the surface roughness

CONTROL AND PLANNING FOR MOBILE MANIPULATORS USED IN LARGE SCALE MANUFACTURING PROCESSES

Sanding operations in industry is one of the few manufacturing tasks that has yet to achieve automation. Sanding tasks require skilled operators that have developed a sense of when a work piece is sufficiently sanded. In order to achieve automation in sanding with robotic systems, this developed sense, or intelligence, that human operators have needs to be understood and implemented in order to achieve, at the minimum, the same quality of work. The system will also need to have the equivalent reach of a human operator and not be constrained to a single, small workspace. This thesis developed solutions for a control scheme and a path planning algorithm to provide the next steps into sanding automation. The control scheme uses found insights on how vibration forces evolve over time during sanding operations to estimate the quality of the surface and adapt the velocity of a sander, akin to how human operators do, with a robotic manipulator. The path planning algorithm was developed to allow for the use of mobile manipulators to perform the sanding tasks and giving the manipulator an equivalent reach to that of an operator

Self-optimizing process planning of multi-step polishing processes

Self-optimizing process planning is an essential approach for finding optimum process parameters and reducing ramp-up times in machining processes. For this purpose, polishing is presented as an application example. In conventional polishing processes, the process parameters are selected according to the operator’s expertise in order to achieve a high-quality surface in the final production step. By implementing machine learning (ML) models in process planning, a correlation between process parameter and measured surface quality is generated. The application of this knowledge automates the selection of optimal process parameters in computer-aided manufacturing (CAM) and enables a continuous adaptation of the NC-code to changing process conditions. Applying the presented ML-model, the prediction accuracy of 83% will adapt the process parameters to achieve the target roughness of 0.2 μm. The sample efficiency is shown by the decrease in root mean square error from 0.1–0.28 to 0.02–0.07 μm with additional polishing iterations

Distributed Analytics Framework for Integrating Brownfield Systems to Establish Intelligent Manufacturing Architecture

Intelligent manufacturing otherwise called as smart manufacturing concentrates upon optimising production and processes by making full use of data available. It is regarded as a new manufacturing model where the entire product life cycle can be simplified using various smart sensors, data-driven decision-making models, visualisation, intelligent devices, and data analytics. In the Industry 4.0 era, Industrial Internet of Things (IIoT) architecture platform is required to streamline and secure data transfer between machines, factories, etc. When certain manufacturing industry is equipped with this platform, an intelligent manufacturing model can be achieved. In today’s factories, most machines are brownfield systems and are not connected to any IoT platforms. Thus they cannot provide data or visibility into their performance, health, and optimal maintenance schedules, which would have improved their operational value. This paper attempts to bridge this gap by demonstrating how brownfield equipment can be IIoT enabled and how data analytics can be performed at the edge as well as cloud using two simple use cases involving industrial robot on the abrasive finishing process. The focus of this paper is on how a scalable data analytics architecture can be built for brownfield machines at the edge as well as the cloud

Geometrical Error Analysis and Correction in Robotic Grinding

The use of robots in industrial applications has been widespread in the manufacturing tasks such as welding, finishing, polishing and grinding. Most robotic grinding focus on the surface finish rather than accuracy and precision. Therefore, it is important to advance the technology of robotic machining so that more practical and competitive systems can be developed for components that have accuracy and precision requirement. This thesis focuses on improving the level of accuracy in robotic grinding which is a significant challenge in robotic applications because of the kinematic accuracy of the robot movement which is much more complex than normal CNC machine tools. Therefore, aiming to improve the robot accuracy, this work provides a novel method to define the geometrical error by using the cutting tool as a probe whilst using Acoustic Emission monitoring to modify robot commands and to detect surfaces of the workpiece. The work also includes an applicable mathematical model for compensating machining errors in relation to its geometrical position as well as applying an optimum grinding method to motivate the need of eliminating the residual error when performing abrasive grinding using the robot. The work has demonstrated an improved machining precision level from 50µm to 30µm which is controlled by considering the process influential variables, such as depth of cut, wheel speed, feed speed, dressing condition and system time constant. The recorded data and associated error reduction provide a significant evidence to support the viability of implementing a robotic system for various grinding applications, combining more quality and critical surface finishing practices, and an increased focus on the size and form of generated components. This method could provide more flexibility to help designers and manufacturers to control the final accuracy for machining a product using a robot system

Three-dimensional histological specimen preparation for accurate imaging and spatial reconstruction of the middle and inner ear

PURPOSE:    This paper presents a highly accurate cross-sectional preparation technique. The research aim was to develop an adequate imaging modality for both soft and bony tissue structures featuring high contrast and high resolution. Therefore, the advancement of an already existing microgrinding procedure was pursued. The central objectives were to preserve spatial relations and to ensure the accurate three-dimensional reconstruction of histological sections. METHODS:    Twelve human temporal bone specimens including middle and inner ear structures were utilized. They were embedded in epoxy resin, then dissected by serial grinding and finally digitalized. The actual abrasion of each grinding slice was measured using a tactile length gauge with an accuracy of one micrometre. The cross-sectional images were aligned with the aid of artificial markers and by applying a feature-based, custom-made auto-registration algorithm. To determine the accuracy of the overall reconstruction procedure, a well-known reference object was used for comparison. To ensure the compatibility of the histological data with conventional clinical image data, the image stacks were finally converted into the DICOM standard. RESULTS:    The image fusion of data from temporal bone specimens’ and from non-destructive flat-panel-based volume computed tomography confirmed the spatial accuracy achieved by the procedure, as did the evaluation using the reference object. CONCLUSION:    This systematic and easy-to-follow preparation technique enables the three-dimensional (3D) histological reconstruction of complex soft and bony tissue structures. It facilitates the creation of detailed and spatially correct 3D anatomical models. Such models are of great benefit for image-based segmentation and planning in the field of computer-assisted surgery as well as in finite element analysis. In the context of human inner ear surgery, three-dimensional histology will improve the experimental evaluation and determination of intra-cochlear trauma after the insertion of an electrode array of a cochlear implant system

A general material removal strategy based on surface sampling and reconstruction on unknown objects

In most material removal processes, the size and shape of the stock material, the desired surface and the orientation of the part are known. If some or all of these factors are unknown, typical automatic systems will not be able to handle the situation. In reality, most of these cases are subsequently handled by human operators. This results in low productivity and inconsistency in the production and potential ergonomic problems for the human operators. Therefore, a new system needs to be designed to meet the requirements for material removal with unknown objects.;This dissertation presents a feasible and efficient automatic system for material removal with unknown processing factors. The characteristics of this type of processes were investigated. The corresponding inputs of the system were decided, while balancing the ease of use and the complexity of the system. A simple point sampling strategy was developed to sample the reference points, which are used to create the approximated surface for the unknown objects with a modified triangular based surface approximation method. A universal layer based path planning method was developed to guide the tool among the layers within the designated working area to remove the excess material effectively and efficiently without changing the programming codes.;This system was verified by simulations and a prototype of the grinding system