R. Teti

Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application

Abstract The use of sensor systems for tool condition monitoring in machining and grinding is becoming more commonplace to enhance productivity. Many approaches have been proposed to accomplish tool condition monitoring and a number of these are successfully employed in industry. This paper reviews the motivation and basis for the utilization of these systems in industry, the sensors used in such systems including industrial application, new developments in signal and information processing, sensor based process optimization and control and directions for future developments. Main developments noted include the use of multiple sensors in systems for increased reliability, the development of intelligent sensors with improved signal processing and decisionmaking capability and the implementation of sensor systems in open architecture controllers for machine tool control.

Machining of Composite Materials

Abstract Machining of composite materials is difficult to carry out due to the anisotropic and non-homogeneous structure of composites and to the high abrasiveness of their reinforcing constituents. This typically results in damage being introduced into the workpiece and in very rapid wear development in the cutting tool. Conventional machining processes such as turning, drilling or milling can be applied to composite materials, provided proper tool design and operating conditions are adopted. An overview of the various issues involved in the conventional machining of the main types of composite materials is presented in this paper.

Intelligent Computing Methods for Manufacturing Systems

Abstract Intelligent computation is taken to include the development and application of artificial intelligence (Al) methods i.e. tools that exhibit characteristics associated with intelligence in human behaviour. Many approaches have been proposed to apply Al methods, techniques and paradigms to the solution of manufacturing problems. This paper discusses current trends in applications of intelligent computing tools to manufacturing and reviews the motivation and basis for the utilisation of these systems. The topics of the paper were confined to four main issues of manufacturing systems: design, planning, production and system level activities. A discussion about intelligent manufacturing systems from these four basic functional view points was introduced, the relevant intelligent computing methods and their use in manufacturing were surveyed, and a number of significant research issues and applications were illustrated. The main developments that were observed comprise the integration of Al methods into CAD, CAPP, etc.; the improvement of the performance of present Al techniques; the development of hybrid Al systems; the elaboration and application of new Al paradigms in manufacturing. Intelligent systems in the future are expected to be integrated, modular, and hybrid in nature, and they may well include all the techniques described in this paper and further more.

Chip Form Monitoring through Advanced Processing of Cutting Force Sensor Signals

This paper draws on the activities of the CIRP Collaborative Work on “Round Robin on Chip Form Monitoring” carried out within the Scientific-Technical Committee Cutting (STC-C). This collaborative work involved the following main round robin activities: (a) generation, detection, storage and exchange of cutting force sensor signals obtained at different Laboratories during sensor-based monitoring of machining processes with variable cutting conditions yielding diverse chip forms, and (b) cutting force signal (CFS) characterization and feature extraction through advanced processing methodologies, both aimed at comparing chip form monitoring results achieved on the basis of innovative analysis paradigms.

Tool Wear Monitoring Through Acoustic Emission

Abstract Acoustic emission (AE) generated during turning was detected and analyzed in order to study tool wear development during metal cutting and investigate the possibility of applying AE techniques for in-process sensing of tool wear. Experimental tests were carried out on carbon steel using HSS tools under realistic cutting conditions. The influence of two wear parameters (maximum wear land and crater wear width) on AE activity was examined. The results are presented in the form of graphs. The analysis of the AE - wear curves provides useful indicators of unacceptable wear development in the tool. These could be used to identify the initiation of rapid breakdown and the moment for tool change in unattended manufacturing systems. A main drawback in applying AE techniques for tool wear monitoring is represented by the dependence of the response on cutting conditions, especially in the case of realistic cutting operations.

Cutting parameters analysis for the development of a milling process monitoring system based on audible energy sound

Monitoring of machining processes is a critical requirement in the implementation of any unmanned operation in a shop floor and, particularly, in the establishment of Flexible Manufacturing Systems (FMS) and Computer Integrated Manufacturing (CIM) where most of the operations are carried out in an automated way. During the last years, notable efforts have been made to develop reliable and robust monitoring systems based on different types of sensors such as cutting force and torque, motor current and effective power, vibrations, acoustic emission or audible sound energy. This work is focused on this last sensor technology. The basic objective is to characterise the audible sound energy signals generated during different machining operations carried out on a milling machine. In order to achieve this, rotation speed, feed and depth of cut have been analysed separately. The main contributions of this work are, on the one hand, the application of a systematic methodology to set up the cutting tests and, on the other hand, the independent signal analysis of the noise generated by the milling machine used for the cutting tests in order to filter this noise out from the signals obtained during the actual material processing. The classification of audible sound signal features for process monitoring has been obtained by graphical analysis and parallel distributed data processing using a supervised neural network (NN) paradigm.

Ultrasonic identification and measurement of defects in composite material laminates

Abstract The scope of the present paper is the identification, through ultrasonic (UT) nondestructive evaluation (NDE), of the consequences of fabrication cycle variations on the integrity and mechanical properties of fiber reinforced composite laminates. A correlation between composite mechanical properties, structural characteristics, and defect nature and size, on the one hand, and UT NDE data, on the other hand, will be attempted. The UT technique utilized herein is based on the acquisition and storage of digitized waveforms obtained from computerized pulse-echo immersion scans. The eventual goal of the research effort is the establishment and optimization of an integrated UT NDE system, capable of identifying defective conditions in fiber reinforced composite components and predicting their criticality during service.

Foresight formulation in innovative production, automation and control systems

Purpose – This paper aims to define imminent and future key aspects in innovative production machines and systems but more specifically to focus on the automation and control aspects.Design/methodology/approach – The foresight analysis is based on the state‐of‐the‐art of current manufacturing technologies with a setup of key enabling features and a roadmap research.Findings – The paper finds that more integration of current and future technology development is required to build a strong platform for various applications featured with interoperability, trust, security and protection. Autonomy and close collaboration aspects in machines remain as crucial targets for the near future. An immediate action is required on smart strategies for the design patterns and agents to enable intuitive components for high quality dynamic user interfaces. This will allow rapid configuration and adaptation to new manufacturing tasks with highly improved machine learning.Originality/value – The paper describes the future of ...

Innovative signal processing for cutting force based chip form prediction

Publisher Summary This chapter discusses the activities of a joint research project work carried out by two laboratories at the Warsaw University of Technology, Poland, and the University of Naples Federico II, Italy. The main activities of the joint research work comprised: (a) generation, detection, and storage of cutting force sensor signals obtained during sensor-based monitoring of machining processes with variable cutting conditions generating different chip forms and (b) cutting force signal (CFS) characterization and feature extraction through advanced processing methodologies, aimed at comparing chip form monitoring results achieved on the basis of innovative signal analysis and processing.

An Intelligent Computation Approach to Process Planning in Multiple-Step Cold Forging

Abstract An intelligent computation approach to time and cost reduction in process planning of cold forging operations is illustrated. The problem taken into consideration is the generation of optimized working sequences in the fabrication of multi-diameter shafts through multiple-step cold forging. A supervised learning neural network paradigm was employed in order to identify the technologically feasible working sequences to be considered for process planning decision making. The process planner can then select the appropriate solution according to his experience or resort to further methods of detailed analysis (e.g. FEM analysis), with the advantage of applying time consuming numerical investigations only to a small number of cases suggested by the intelligent computing system. Neural network training and testing allowed to verify the system performance in classifying working sequence feasibility and its computational speed in providing technologically acceptable working sequences for process planner consideration.