Improving robotic machining accuracy through experimental error investigation and modular compensation

Machining using industrial robots is currently limited to applications with low geometrical accuracies and soft materials. This paper analyzes the sources of errors in robotic machining and characterizes them in amplitude and frequency. Experiments under different conditions represent a typical set of industrial applications and allow a qualified evaluation. Based on this analysis, a modular approach is proposed to overcome these obstacles, applied both during program generation (offline) and execution (online). Predictive offline compensation of machining errors is achieved by means of an innovative programming system, based on kinematic and dynamic robot models. Real-time adaptive machining error compensation is also provided by sensing the real robot positions with an innovative tracking system and corrective feedback to both the robot and an additional high-dynamic compensation mechanism on piezo-actuator basis

Control of the interaction of a gantry robot end effector with the environment by the adaptive behaviour of its joint drive actuators

The thesis examines a way in which the performance of the robot electric actuators can be precisely and accurately force controlled where there is a need for maintaining a stable specified contact force with an external environment. It describes the advantages of the proposed research, which eliminates the need for any external sensors and solely depends on the precise torque control of electric motors. The aim of the research is thus the development of a software based control system and then a proposal for possible inclusion of this control philosophy in existing range of automated manufacturing techniques.The primary aim of the research is to introduce force controlled behaviour in the electric actuators when the robot interacts with the environment, by measuring and controlling the contact forces between them. A software control system is developed and implemented on a robot gantry manipulator to follow two dimensional contours without the explicit geometrical knowledge of those contours. The torque signatures from the electric actuators are monitored and maintained within a desired force band. The secondary aim is the optimal design of the software controller structure. Experiments are performed and the mathematical model is validated against conventional Proportional Integral Derivative (PID) control. Fuzzy control is introduced in the software architecture to incorporate a sophisticated control. Investigation is carried out with the combination of PID and Fuzzy logic which depend on the geometrical complexity of the external environment to achieve the expected results

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Development of an integrated robotic polishing system

This thesis presents research carried out as part of a project undertaken in fulfilment of the requirements of Loughborough University for the award of Philosophical Doctorate. The main focus of this research is to investigate and develop an appropriate level of automation to the existing manual finishing operations of small metallic components to achieve required surface quality and to remove superficial defects. In the manufacturing industries, polishing processes play a vital role in the development of high precision products, to give a desired surface finish, remove defects, break sharp edges, extend the working life cycle, and meet mechanical specification. The polishing operation is generally done at the final stage of the manufacturing process and can represent up to a third of the production time. Despite the growth automated technology in industry, polishing processes are still mainly carried out manually, due to the complexity and constraints of the process. Manual polishing involves a highly qualified worker polishing the workpiece by hand. These processes are very labour intensive, highly skill dependent, costly, error-prone, environmentally hazardous due to abrasive dust, and - in some cases - inefficient with long process times. In addition, the quality of the finishing is dependent on the training, experience, fatigue, physical ability, and expertise of the operator. Therefore, industries are seeking alternative solutions to be implemented within their current processes. These solutions are mainly aimed at replacing the human operator to improve the health and safety of their workforce and improve their competitiveness. Some automated solutions have already been proposed to assist or replace manual polishing processes. These solutions provide limited capabilities for specific processes or components, and a lack of flexibility and dexterity. One of the reasons for their lack of success is identified as neglecting the study and implementing the manual operations. This research initially hypothesised that for an effective development, an automated polishing system should be designed based on the manual polishing operations. Therefore, a successful implementation of an automated polishing system requires a thorough understanding of the polishing process and their operational parameters. This study began by collaborating with an industrial polishing company. The research was focused on polishing complex small components, similar to the parts typically used in the aerospace industry. The high level business processes of the polishing company were capture through several visits to the site. The low level operational parameters and the understanding of the manual operations were also captured through development of a devices that was used by the expert operators. A number of sensors were embedded to the device to facilitate recording the manual operations. For instance, the device captured the force applied by the operator (avg. 10 N) and the cycle time (e.g. 1 pass every 5 sec.). The capture data was then interpreted to manual techniques and polishing approaches that were used in developing a proof-of-concept Integrated Robotic Polishing System (IRPS). The IRPS was tested successfully through several laboratory based experiments by expert operators. The experiment results proved the capability of the proposed system in polishing a variety of part profiles, without pre-existing geometrical information about the parts. One of the main contributions made by this research is to propose a novel approach for automated polishing operations. The development of an integrated robotic polishing system, based on the research findings, uses a set of smart sensors and a force-position-by-increment control algorithm, and transpose the way that skilled workers carry out polishing processes

Robots in machining

Robotic machining centers offer diverse advantages: large operation reach with large reorientation capability, and a low cost, to name a few. Many challenges have slowed down the adoption or sometimes inhibited the use of robots for machining tasks. This paper deals with the current usage and status of robots in machining, as well as the necessary modelling and identification for enabling optimization, process planning and process control. Recent research addressing deburring, milling, incremental forming, polishing or thin wall machining is presented. We discuss various processes in which robots need to deal with significant process forces while fulfilling their machining task

Burrs understanding, modeling and optimization during slot milling of aluminium alloys

Nowadays due to global competition, manufacturing industries must provide high quality products on time and within the cost constraints to remain competitive. High quality mechanical parts include those with better surface finish and texture, dimension and form accuracies, reduced tensile residual stress and burr-free. The burr formation is one of the most common and undesirable phenomenon occurring in machining operations, which reduces assembly and machined part quality. Therefore, it is desired to eliminate the burrs or reduce the effort required to remove them. Amongst machining operations, slot milling has a more complex burr formation mechanism with multiple burrs appear in machined part edges with non-uniform dimensions. The ultimate goal of this research work is burr minimization in slot milling operation. To this end, new strategies for understanding, modeling and optimizing burrs during slot milling of aluminum alloys are proposed for improving the part quality and ultimately reducing the non-value added expenses caused by deburring processes. In order to have a better understanding of slot milling burr formation mechanism, multi-level experimental studies and statistical methods are used to determine the effects of machining conditions, tooling and workpiece materials on burrs size (height and thickness) when using dry high speed condition. It was found that optimum setting levels of process parameters to minimize each burr are dissimilar. The analysis of results shows that cutting tool, feed per tooth and depth of cut have certain level of influence on slot milling burrs. However most of the burrs are strongly affected by interaction effects between process parameters that consequently complicate developing burr size prediction models. An analytical model is proposed to predict the thickness of the largest burr during slot milling of ductile materials. The model is based on the geometry of burr formation and continuity of work at the transition from chip formation to burr formation, which also takes into account the effect of the cutting force involved in the machining process. A computational model is also developed to predict the exit up milling side burr thickness based on the use of cutting parameters and material properties such as yield strength and specific cutting force coefficient, which are the only unknown variables in the model. Both analytical and computational models are validated using experimental results obtained during slot milling of 2024-T351 and 6061-T6 aluminium alloys. Machining parameters optimization to minimize the burr size could have a negative impact on other machining performance characteristic, such as surface finish, tool life and material removal rate. Therefore, surface finish is also investigated with burr formation in this research work. For simultaneous multiple responses optimization, a new modification to the application of Taguchi method is suggested by proposing fitness mapping function and desirability index. The proposed modification is validated by simultaneous minimization of surface roughness and thickness of five burrs during slot milling of 6061-T6 aluminium alloy. The optimization results demonstrate the potential and capability of the proposed approach

Real-time control of industrial robots in multiple microcomputers

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