Classification of Causes of Errors in the Human - Robot System

The very first classification of causes of errors in the human - robot system has been presented in this paper. This new model of error classification in the human - robot system has a global character. This means that it includes the causes of the errors of the individual components of the system, but also the errors that result from their interaction.The model also includes all the factors surrounding this system, which can act as the cause of errors in the human - robot system. This model distinguishes five main groups of causes of errors, described in the paper. The classification of errors in the human - robot system has great importance. It can serve to designers as a guide or reminder on factors that should be taken into account during designing, in order to reduce the errors in the human - robot system. In addition, this model can serve to assess the efficiency and possible causes of accidents in the human - robot system. Certain general solutions for the reduction of causes of errors in the human - robot system are also presented

Effects of machine stiffness and cutting tool design on the surface quality and flexural strength of edge trimmed carbon fibre reinforced polymers

A 22 full factorial design of experiment is used to investigate the effects of two machining platforms (5-axis elevated gantry versus 6-axis articulated robotic system) and two cutting tool designs (burr versus herringbone) on surface metrics and flexural strength of a 14 ply T300 2x2 carbon fibre reinforced polymer. A range of areal metrics were considered to characterise the surface with Sal and Stdi best able to represent differences due to the choice of robotic system or overhead gantry. The robotic system produces coupons with flexural strengths up to 26% higher than the overhead gantry. The choice of tool has a less significant effect however machine-tool interactions do play a role in the flexural strength. Analysis using scanning electron microscopy shows that defects may be obscured by smeared matrix which may contribute to overall flexural strength differences

Robotic machining evaluation of the positioning accuracy and the machined surface quality

Due to the importance of the surface quality of machined parts, many research works have been devoted to the surface irregularities and their generating mechanisms. However, the surface quality of the robotic machining operations has not been sufficiently investigated. Indeed, the relative works are restricted to the finishing operations such as grinding and deburring. In this work, the surface quality of the slot milling operation which is executed by an industrial robot on an aluminum block is investigated. For this purpose, several slots at different directions are machined on the block by applying various cutting parameters. In order to investigate the surface quality of the slots, the machined surfaces are evaluated by a mechanical profiler, and then the results are analyzed using the power spectrum density method. Moreover, to monitor the machining conditions, the machining forces are measured with a dynamometer table. To identify the generating factors of the irregularities, both the kinematic and the dynamic properties of the robot are experimentally examined. The kinematic properties of the robot are investigated by measuring its straightness using a laser tracker, and the dynamic properties are evaluated by applying the impact test. Lack of accuracy is one of the difficulties restricting the usage of robotic machining. Indeed, the poor accuracy of industrial robots makes the off-line programming uneffective. Consequently, the operators are forced to use on-line method which is a time consuming approach. However, if a robot is calibrated properly, the off-line method could be effectively applied. To this end, before analyzing the surface quality, the accuracy of the robot is investigated and improved using a hybrid calibration model considering both the geometric errors and the joint compliances

Automation and Robotics: Latest Achievements, Challenges and Prospects

This SI presents the latest achievements, challenges and prospects for drives, actuators, sensors, controls and robot navigation with reverse validation and applications in the field of industrial automation and robotics. Automation, supported by robotics, can effectively speed up and improve production. The industrialization of complex mechatronic components, especially robots, requires a large number of special processes already in the pre-production stage provided by modelling and simulation. This area of research from the very beginning includes drives, process technology, actuators, sensors, control systems and all connections in mechatronic systems. Automation and robotics form broad-spectrum areas of research, which are tightly interconnected. To reduce costs in the pre-production stage and to reduce production preparation time, it is necessary to solve complex tasks in the form of simulation with the use of standard software products and new technologies that allow, for example, machine vision and other imaging tools to examine new physical contexts, dependencies and connections

Design of an intelligent embedded system for condition monitoring of an industrial robot

PhD ThesisIndustrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. There are significant implications for operator safety in the event of a robot malfunction or failure, and an unforeseen robot stoppage, due to different reasons, has the potential to cause an interruption in the entire production line, resulting in economic and production losses. Condition monitoring (CM) is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reduce maintenance costs. So, the main focus of this research is to design and develop an online, intelligent CM system based on wireless embedded technology to detect and diagnose the most common faults in the transmission systems (gears and bearings) of the industrial robot joints using vibration signal analysis. To this end an old, but operational, PUMA 560 robot was utilized to synthesize a number of different transmission faults in one of the joints (3 - elbow), such as backlash between the gear pair, gear tooth and bearing faults. A two-stage condition monitoring algorithm is proposed for robot health assessment, incorporating fault detection and fault diagnosis. Signal processing techniques play a significant role in building any condition monitoring system, in order to determine fault-symptom relationships, and detect abnormalities in robot health. Fault detection stage is based on time-domain signal analysis and a statistical control chart (SCC) technique. For accurate fault diagnosis in the second stage, a novel implementation of a time-frequency signal analysis technique based on the discrete wavelet transform (DWT) is adopted. In this technique, vibration signals are decomposed into eight levels of wavelet coefficients and statistical features, such as standard deviation, kurtosis and skewness, are obtained at each level and analysed to extract the most salient feature related to faults; the artificial neural network (ANN) is then used for fault classification. A data acquisition system based on National Instruments (NI) software and hardware was initially developed for preliminary robot vibration analysis and feature extraction. The transmission faults induced in the robot can change the captured vibration spectra, and the robot’s natural frequencies were established using experimental modal analysis, and also the fundamental fault frequencies for the gear transmission and bearings were obtained and utilized for preliminary robot condition monitoring. In addition to simulation of different levels of backlash fault, gear tooth and bearing faults which have not been previously investigated in industrial robots, with several levels of ii severity, were successfully simulated and detected in the robot’s joint transmission. The vibration features extracted, which are related to the robot healthy state and different fault types, using the data acquisition system were subsequently used in building the SCC and ANN, which were trained using part of the measured data set that represents the robot operating range. Another set of data, not used within the training stage, was then utilized for validation. The results indicate the successful detection and diagnosis of faults using the key extracted parameters. A wireless embedded system based on the ZigBee communication protocol was designed for the application of the proposed CM algorithm in real-time, using an Arduino DUE as the core of the wireless sensor unit attached on the robot arm. A Texas Instruments digital signal processor (TMS320C6713 DSK board) was used as the base station of the wireless system on which the robot’s fault diagnosis algorithm is run. To implement the two stages of the proposed CM algorithm on the designed embedded system, software based on the C programming language has been developed. To demonstrate the reliability of the designed wireless CM system, experimental validations were performed, and high reliability was shown in the detection and diagnosis of several seeded faults in the robot. Optimistically, the established wireless embedded system could be envisaged for fault detection and diagnostics on any type of rotating machine, with the monitoring system realized using vibration signal analysis. Furthermore, with some modifications to the system’s hardware and software, different CM techniques such as acoustic emission (AE) analysis or motor current signature analysis (MCSA), can be applied.Iraqi government, represented by the Ministry of Higher Education and Scientific Research, the Iraqi Cultural Attaché in London, and the University of Technology in Baghda

Edge milled carbon fibre reinforced polymers: surface metrics and mechanical performance

Carbon fibre reinforced thermoset polymer (CFRP) components are becoming increasingly prevalent in aerospace and automotive industries where reduced weight and increased fuel efficiency is required. The manufacturing process typically requires the net shape to be edge trimmed, using a milling process, to achieve final part shape. The cutting process can cause defects on the trimmed edge which, due to the anisotropic nature of the CFRP material, may not be adequately captured by traditional, metallic material based surface quality metrics. More fundamentally, the effect on mechanical performance, in particular flexural strength, is not well understood. The aim of this project is to investigate links between machined edge surfaces and static flexural properties. The effects of machine stiffness and cutting tool design, the effects of tool coating and tool wear, and finally, the effect of machining temperature on the surface quality and subsequent flexural strength are assessed. This is completed through the use of a robust framework to assess materials, machines and tools used in experimentation. Dynamometer data is captured and assessed through an original metric and current state-of-the-art 3D areal metrics are used to assess the machined surface topography. Additionally, scanning electron microscopy (SEM) is used to provide further qualitative data. Chips are collected and analysed, in a first for composite materials, to determine average geometry and changes due to machining variables. Finally, to address the shortcomings of current available metrics, a novel metric to observe sub-surface defects is proposed, validated and used to assess effects of machining variables on edge quality. It has been found that edge quality does alter the mechanical strength of edge trimmed CFRP through static four-point bend analysis. Flexural strength of coupons machined by the 6-axis robotic system is 25.9% greater than the 5-axis gantry. Tool wear and machining at elevated temperatures can reduce flexural strength by 7.1 and 8.7%, respectively. Design of experiment (DoE) and analysis of variance (ANOVA) methods employed to show statistical correlations with machining variables and surface metrics. The edge quality of CFRP, machined using prescribed variables, has been successfully linked to amplitude and volumetric 3D areal metrics (p < 0.05). Cutting mechanisms of different fibre orientations have been successfully characterised through SEM and areal analysis. Analysis of machining chips has confirmed cutting mechanism changes when the CFRP material is pre-heated up to glass transition onset. A novel, validated strategy for measuring sub-surface defects, was able to observe defects in edge trimmed samples, particularly in the 90° fibre region where matrix smearing previously prevented observation of damage