Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera

Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 μm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model

Thermal error modelling of a gantry-type 5-axis machine tool using a Grey Neural Network Model

This paper presents a new modelling methodology for compensation of the thermal errors on a gantry-type 5-axis CNC machine tool. The method uses a “Grey Neural Network Model with Convolution Integral” (GNNMCI(1, N)), which makes full use of the similarities and complementarity between Grey system models and artificial neural networks (ANNs) to overcome the disadvantage of applying either model in isolation. A Particle Swarm Optimisation (PSO) algorithm is also employed to optimise the proposed Grey neural network. The size of the data pairs is crucial when the generation of data is a costly affair, since the machine downtime necessary to acquire the data is often considered prohibitive. Under such circumstances, optimisation of the number of data pairs used for training is of prime concern for calibrating a physical model or training a black-box model. A Grey Accumulated Generating Operation (AGO), which is a basis of the Grey system theory, is used to transform the original data to a monotonic series of data, which has less randomness than the original series of data. The choice of inputs to the thermal model is a non-trivial decision which is ultimately a compromise between the ability to obtain data that sufficiently correlates with the thermal distortion and the cost of implementation of the necessary feedback sensors. In this study, temperature measurement at key locations was supplemented by direct distortion measurement at accessible locations. This form of data fusion simplifies the modelling process, enhances the accuracy of the system and reduces the overall number of inputs to the model, since otherwise a much larger number of thermal sensors would be required to cover the entire structure. The Z-axis heating test, C-axis heating test, and the combined (helical) movement are considered in this work. The compensation values, calculated by the GNNMCI(1, N) model were sent to the controller for live error compensation. Test results show that a 85% reduction in thermal errors was achieved after compensation

Rehabilitation Engineering

Population ageing has major consequences and implications in all areas of our daily life as well as other important aspects, such as economic growth, savings, investment and consumption, labour markets, pensions, property and care from one generation to another. Additionally, health and related care, family composition and life-style, housing and migration are also affected. Given the rapid increase in the aging of the population and the further increase that is expected in the coming years, an important problem that has to be faced is the corresponding increase in chronic illness, disabilities, and loss of functional independence endemic to the elderly (WHO 2008). For this reason, novel methods of rehabilitation and care management are urgently needed. This book covers many rehabilitation support systems and robots developed for upper limbs, lower limbs as well as visually impaired condition. Other than upper limbs, the lower limb research works are also discussed like motorized foot rest for electric powered wheelchair and standing assistance device

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

Optimization of Manufacturing Production and Process

This chapter mainly introduces production processing optimization, especially for machining processing optimization on CNC. The sensor collects the original vibration data in time domain and converts them to the main feature vector using signal processing technologies, such as fast Fourier transform (FFT), short-time Fourier transform (STFT), and wavelet packet in the 5G AI edge computing. Subsequently, the main feature will be sent for cloud computing using genetic programming, Space Vector Machine (SVM), etc. to obtain optimization results. The optimization parameters in this work include machining spindle rotation velocity, cutting speed, and cutting depth, while, the result is the optimized main spindle rotation speed range of CNC, which met machining roughness requirements. Finally, the relationship between vibration velocity and machining quality is further studied to optimize the three operational parameters

Cognitive Sensor Monitoring of Machining Processes for Zero Defect Manufacturing

The topic of this thesis, focused on cognitive sensor monitoring of machining processes for zero defect manufacturing, has been addressed within the framework of the international research project EC FP7 CP-IP “IFaCOM – Intelligent Fault Correction and self Optimizing Manufacturing systems” (2011-2015; FoF NMP – 285489) and the national MIUR PON Project on “Development of eco-compatible materials and technologies for robotised drilling and assembly processes – STEP FAR” (2014-2016). The vision of the IFaCOM project is to achieve near zero defect level of manufacturing with particular emphasis on the production of high value, large variety and high performance products. This goal is achieved through the development of improved methodologies for monitoring and control of the performance of manufacturing processes with the aim to detect abnormal process conditions leading to defects on the produced parts. The overall aim of the STEP FAR project is the study of issues related to drilling and cutting techniques of advanced lightweight components, such as composite material parts, and their relative assembly, using cooperating anthropomorphic robots. The use of innovative materials and processes developed in this research will lead to a reduction in weight and environmental impact in the construction and maintenance of primary aircraft structures. At least a 5% reduction in weight of the structures is foreseen without increase of costs (a possible rise in the cost of raw materials is compensated with the reduction of process costs). In aeronautical industry the reduction of the weight of the aircraft is becoming an increasingly important aim both for environmental requirements (lower emissions) and contraction of the management costs (lower fuel consumption). Therefore new structural architectures through the use of innovative materials and technologies have been developed. One of the innovative processes analysed in this project is the drilling via machining of carbon fibre reinforced plastic (CFRP) stack-ups. In the framework of these projects, this thesis work is focused on the development of cognitive condition monitoring procedures for zero defect machining processes with reference to two different industrial manufacturing applications. The thesis is organized as follows: Chapter 2 reviews the general concept of sensor monitoring of manufacturing processes and provides a comprehensive survey of sensor technologies, advanced signal processing techniques, sensor fusion approach, and cognitive decision making strategies for process monitoring. In Chapter 3, the Strecon industrial case, as a partner of the IFaCOM project, is discussed and analysed. The STRECON end-user case is focused on improving repeatability and predictability of the surface finish produced by a Robot Automated Polishing (RAP) process. In order to establish a robust method for the detection of the polishing process end-point, i.e. the determination of the right moment for tool and abrasive paste change, STRECON sensor system selection focuses on monitoring the progress of the surface quality during the polishing process by means of variation in VQCs (Vital Quality Characteristics), i.e. roughness and gloss of the polished surface. The output data have been used to train a neural network. The employed NN learning procedure was the leave-k-out method where k cases from the training set are put aside in turn, while the other cases are used for NN training. In Chapter 4, the Alenia Aermacchi industrial case, as coordinator and partner of the STEP FAR project, is discussed and analysed. The Alenia Aermacchi user case is based on the analysis of drilling of stacks made of two overlaid carbon fibre reinforced plastic composite laminates. In this case, a neural network based cognitive paradigm based on a bootstrap procedure has been used for the identification of correlations with tool wear development and product hole quality. Finally, Chapter 5 reports the concluding remarks and future developments of this work

Robust Thermal Error Modeling and Compensation for CNC Machine Tools.

Thermal errors are one of the most significant factors affecting machine tool accuracy. Error compensation has been widely used to reduce the thermal errors, the robustness of the thermal error models, however, still needs to be improvement. Currently, five-axis machine tools are becoming more important and extensively utilized in industry. In this regard, the geometric errors of rotary axis must be properly measured and corrected to assure the accuracy of five-axis machining. Thermal error model, relating temperature variations to thermal errors, is the core of an effective thermal error compensation strategy. Thermal modal analysis, unveiling the essence of thermo-elastic process, is explored for the determination of temperature sensor placement based on the finite element analysis and eigen analysis. Thermal error models are thus derived based on the temperature variations collected from the specified temperature sensors. The robustness of the derived models is investigated in terms of linear extrapolation and frequency sensitivity. Numerical simulation and experiments are conducted to illustrate the existence of thermal modes and validate the robustness of the thermal error models. Thermal loop analysis is developed for the thermal error compensation of an entire machine tool. A machine tool is first decomposed into several thermal links along an identified thermal loop. For each thermal link, the thermal modal analysis is carried out for the derivation of thermal error model. These thermal links are finally reassembled for the thermal error prediction of the entire machine tool. The thermal loop analysis mitigates the inaccurate modeling of machine joints, and extensively facilitates the utilization of the finite element method in the thermal error modeling and compensation. Calibration of rotary axis of five-axis machine tools is usually time-consuming and laborious by using laser interferometer or autocollimator systems. The Telescopic Magnetic Ball Bar is explored to estimate error components induced by the rotational motion of a rotary axis. The calibration algorithm is developed based on the rigorous mathematical derivation. The setup errors, including parameter variation and eccentricity, have been accounted for through the numerical simulation, enabling the practical utilization of this method. This approach shows the advantages of easy setup and quick assessment.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60857/1/zhujie_1.pd

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools