Robot Autonomy for Surgery

Autonomous surgery involves having surgical tasks performed by a robot operating under its own will, with partial or no human involvement. There are several important advantages of automation in surgery, which include increasing precision of care due to sub-millimeter robot control, real-time utilization of biosignals for interventional care, improvements to surgical efficiency and execution, and computer-aided guidance under various medical imaging and sensing modalities. While these methods may displace some tasks of surgical teams and individual surgeons, they also present new capabilities in interventions that are too difficult or go beyond the skills of a human. In this chapter, we provide an overview of robot autonomy in commercial use and in research, and present some of the challenges faced in developing autonomous surgical robots

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

Agent-based real-time assembly line management for wireless job shop environment

Recent developments in wireless technologies have created opportunities for developing next-generation manufacturing systems with real-time traceability, visibility and interoperability in shop floor planning, execution and control. This paper discusses how to deploy wireless and intelligent technologies to convert physical objects in manufacturing systems into smart objects to introduce and improve the interoperability and visibility between them and thus with manufacturing decision support systems. A reference architecture for wireless manufacturing (WM) is proposed where three types of smart objects are identified. At the same time, the concept of smart object agent (SOA) is presented and the corresponding framework of smart objects management system (SOMS) is constructed. Under this framework and the concept of SOA, a SOA-based WM environment is studied and demonstrated using a near real-life simplified product assembly line for the collection and synchronization of the real-time field data from manufacturing workshops. © 2010 IEEE.published_or_final_versionThe IEEE International Conference on Mechatronics and Automation (ICMA) 2010, Xi'an, China, 4-7 August 2010. In Proceedings of the IEEE International Conference on Mechatronics and Automation, 2010, p. 2013-201

Derivation of a cost model to aid management of CNC machine tool accuracy maintenance

Manufacturing industries strive to produce improved component accuracy while not reducing machine tool availability or production throughput. The accuracy of CNC production machines is one of the critical factors in determining the quality of these components. Maintaining the capability of the machine to produce in-tolerance parts can be approached in one of two ways: run to failure or periodic calibration and monitoring. The problem is analogous to general machine tool maintenance, but with the clear distinction that the failure mode of general machine tool components results in a loss of production, whereas that of accuracy allows parts to be produced, which are only later detected as non-conforming as part of the quality control processes. This distinction creates problems of cost-justification, since at this point in the manufacturing chain, any responsibility of the machine is not directly evident. Studies in the field of maintenance have resulted in cost calculations for the downtime associated with machine failure. This paper addresses the analogous, unanswered problem of maintaining the accuracy of CNC machine tools. A mathematical cost function is derived that can form the basis of a strategy for either running until non-conforming parts are detected or scheduling predictive CNC machine tool calibrations. This is sufficiently generic that it can consider that this decision will be based upon different scales of production, different values of components etc. Therefore, the model is broken down to a level where these variables for the different inputs can be tailored to the individual manufacturer

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic

Radio Frequency Identification (RFID) based wireless manufacturing systems, a review

Radio frequency identification (RFID) is one of the most promising technological innovations in order to track and trace products as well as material flow in manufacturing systems. High Frequency (HF) and Ultra High Frequency (UHF) RFID systems can track a wide range of products in the part production process via radio waves with level of accuracy and reliability.   As a result, quality and transparency of data across the supply chain can be accurately obtained in order to decrease time and cost of part production. Also, process planning and part production scheduling can be modified using the advanced RFID systems in part manufacturing process. Moreover, to decrease the cost of produced parts, material handling systems in the advanced assembly lines can be analyzed and developed by using the RFID. Smart storage systems can increase efficiency in part production systems by providing accurate information from the stored raw materials and products for the production planning systems. To increase efficiency of energy consumption in production processes, energy management systems can be developed by using the RFID-sensor networks. Therefore, smart factories and intelligent manufacturing systems as industry 4.0 can be introduced by using the developed RFID systems in order to provide new generation of part production systems. In this paper, a review of RFID based wireless manufacturing systems is presented and future research works are also suggested. It has been observed that the research filed can be moved forward by reviewing and analyzing recent achievements in the published papers

Development of the UMAC-based control system with application to 5-axis ultraprecision micromilling machines

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Increasing demands from end users in the fields of optics, defence, automotive, medical, aerospace, etc. for high precision 3D miniaturized components and microstructures from a range of materials have driven the development in micro and nano machining and changed the manufacturing realm. Conventional manufacturing processes such as chemical etching and LIGA are found unfavourable or limited due to production time required and have led mechanical micro machining to grow further. Mechanical micro machining is an ideal method to produce high accuracy micro components and micro milling is the most flexible enabling process and is thus able to generate a wider variety of complex micro components and microstructures. Ultraprecision micromilling machine tools are required so as to meet the accuracy, surface finish and geometrical complexity of components and parts. Typical manufacturing requirements are high dimensional accuracy being better than 1 micron, flatness and roundness better than 50 nm and surface finish ranging between 10 and 50 nm. Manufacture of high precision components and parts require very intricate material removal procedure. There are five key components that include machine tools, cutting tools, material properties, operation variables and environmental conditions, which constitute in manufacturing high quality components and parts. End users assess the performance of a machine tool based on the dimensional accuracy and surface quality of machined parts including the machining time. In this thesis, the emphasis is on the design and development of a control system for a 5-axis bench-type ultraprecision micromilling machine- Ultra-Mill. On the one hand, the developed control system is able to offer high motion and positioning accuracy, dynamic stiffness and thermal stability for motion control, which are essential for achieving the machining accuracy and surface finish desired. On the other hand, the control system is able to undertake in-process inspection and condition monitoring of the machine tool and process. The control of multi-axis precision machines with high-speed and high-accuracy motions and positioning are desirable to manufacture components with high accuracy and complex features to increase productivity and maintain machine stability, etc. The development of the control system has focused on fast, accurate and robust positioning requirements at the machine system design stage. Apart from the mechanical design, the performance of the entire precision systems is greatly dependent on diverse electrical and electronics subsystems, controllers, drive instruments, feedback devices, inspection and monitoring system and software. There are some variables that dynamically alter the system behaviour and sensitivity to disturbance that are not ignorable in the micro and nano machining realm. In this research, a structured framework has been developed and integrated to aid the design and development of the control system. The framework includes critically reviewing the state of the art of ultraprecision machining tools, understanding the control system technologies involved, highlighting the advantages and disadvantages of various control system methods for ultraprecision machines, understanding what is required by end-users and formulating what actually makes a machine tool be an ultraprecision machine particularly from the control system perspective. In the design and development stage, the possession of mechatronic know-how is essential as the design and development of the Ultra-Mill is a multidisciplinary field. Simulation and modelling tool such as Matlab/Simulink is used to model the most suitable control system design. The developed control system was validated through machining trials to observe the achievable accuracy, experiments and testing of subsystems individually (slide system, tooling system, monitoring system, etc.). This thesis has successfully demonstrated the design and development of the control system for a 5-axis ultraprecision machine tool- Ultra-Mill, with high performance characteristics, fast, accurate, precise, etc. for motion and positioning, high dynamic stiffness, robustness and thermal stability, whereby was provided and maintained by the control system

Applications of Digital Manufacturing in Manufacturing Process Support

In this study, the authors developed three new approaches and models for improvements related to manufacturing processes. The main focus was on planning in a digital environment before the actual manufacturing process is carried out. The first approach is digital manufacturing, which gives affords the opportunity for performing an entire manufacturing process in a virtual environment. In this way, engineers virtually define, plan, create, monitor, and control all production processes. The planning phase can be done simultaneously, while other manufacturing processes are already in place. In this way, processes can continue with no interruption. Various product lifecycle management tools have databases of various programs that are used for interfacing and communication with machinery, such as CNC machines and industrial robots. Ideally, after the manufacturing process has been verified in the digital environment, control data can be uploaded to numerically controlled machinery so that the production process can start. Two special models have been developed for more detailed insight into special types of manufacturing processes. The second approach represents a model for the unique type of production that takes into account all resources as the most important factor in the manufacturing processes. The main variables that were included in this model were the availability and the presence of all required manufacturing resources needed for every single manufacturing operation. The third approach represents a model for large-scale production that includes all significant parameters of a manufacturing process, as well as all required intermediate storage. The last two models were developed as parametric, and the users in the training process can easily make tests for different types of input data