David Dornfeld

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.

Intelligent Manufacturing Systems

Introduction - flexible machining and assembly systems components of knowledge-based systems machine learning design of mechanical parts and mechanisms process planning. KBSES - a knowledge-based system for equipment selection group technology - models and algorithms. KBGT - a knowledge-based system for group technology models and algorithms for machine layout. KBML - knowledge-based system for machine layout aggregate scheduling of machining and assembly systems scheduling models and algorithms. KBSS - a knowledge-based system for scheduling in automated manufacturing.

Advancing Cutting Technology

This paper reviews some of the main developments in cutting technology since the foundation of CIRP over fifty years ago. Material removal processes can take place at considerably higher performance levels in the range up to Qw = 150 - 1500 cm3/min for most workpiece materials at cutting speeds up to some 8.000 m/min. Dry or near dry cutting is finding widespread application. The superhard cutting tool materi- als embody hardness levels in the range 3000 – 9000 HV with toughness levels exceeding 1000 MPa. Coated tool materials offer the opportunity to fine tune the cutting tool to the material being machined. Machining accuracies down to 10 ?m can now be achieved for conventional cutting processes with CNC machine tools, whilst ultraprecision cutting can operate in the range < 0.1?m. The main technological developments associated with the cutting tool and tool materials, the workpiece materials, the machine tool, the process conditions and the manufacturing environment which have led to this advancement are given detailed consideration in this paper. The basis for a roadmap of future development of cutting tech- nology is provided.

Present Situation and Future Trends in Modelling of Machining Operations Progress Report of the CIRP Working Group ‘Modelling of Machining Operations’

Abstract In 1995 CIRP STC “Cutting” started a working group “Modelling of Machining Operations” with the aim of stimulating the development of models capable of predicting quantitatively the performance of metal cutting operations which will be better adapted to the needs of the metal cutting industry in the future. This paper has the character of a progress report. It presents the aims of the working group and the results obtained up to now. The aim is not to review extensively what has been done in the past. It is basically a critical assessment of the present state-of-the-art of the wide and complex field of modelling and simulation of metal cutting operations based on information obtained from the members of the working group, from consultation in industry, study of relevant literature and discussions at meetings of the working group whit the aim to stimulate and pilot future developments. For this purpose much attention is given to a discussion of desirable and possible future developments and planned new activities.

Neural Network Sensor Fusion for Tool Condition Monitoring

The design and implementation of a neural network-based system combining the outputs of several sensors (acoustic emission, force and spindle motor current) for monitoring progressive tool wear in a single point turning operation is described. Multichannel autoregressive series model parameters and power spectrum amplitudes are used as inputs to the network. The objective of the system is to extend the range of machining conditions over which the system performs successfully. A basic architecture for multiple sensor systems is outlined. Results of recent research to implement a real-time monitoring system are presented.

Material Removal Mechanisms in Lapping and Polishing

Polishing processes are critical to high value production processes such as IC manufacturing. The fundamental material removal mechanisms, howeve, are poorly understood. Technological outputs (e.g., surface finish, sub-surface damage, part shape) and throughput of lapping and polishing processes are affected by a large number of variables. Individual processes are well controlled within individual enterprises, yet there appears to be little ability to predict process performance a priori. As a first step toward improving process modeling, this paper reviews the fundamental mechanisms of material removal in lapping and polishing processes and identifies key areas where further work is required.

Energy Consumption Characterization and Reduction Strategies for Milling Machine Tool Use

Since machine tools are used extensively throughout their functional life and consequently consuming valuable natural resources and emitting harmful pollutants during this time, this study reviews strategies for characterizing and reducing the energy consumption of milling machine tools during their use. The power demanded by a micromachining center while cutting low carbon steel under varied material removal rates was measured to model the specific energy of the machine tool. Thereafter the power demanded was studied for cutting aluminum and polycarbonate work pieces for the purpose of comparing the difference in cutting power demand relative to that of steel.

Learning and optimization of machining operations using computing abilities of neural networks

The authors present a scheme that uses a feedforward neural network for the learning and synthesis task. Neural networks consist of a collection of interconnected processors that compute in parallel. The parallelism allows the network to examine various constraints simultaneously during the learning phase and enables reductions in computing time that are attractive in real-time applications. The learning abilities of these networks in a tuning operation are discussed. The network learns by observing the effect of the input variables of the operation (such as feed rate, depth of cut, and cutting speed) on the output variables (such as cutting force, power, temperature, and surface finish of the workpiece). The learning phase is followed by a synthesis phase during which the network predicts the input conditions to be used by the machine tool to maximize the metal removal rate subject to appropriate operating constraints. >

Precision Manufacturing Process Monitoring with Acoustic Emission

Demands in high-technology industries such as semiconductor, optics, MEMS, etc., have predicated the need for manufacturing processes that can fabricate increasingly smaller features reliably at very high tolerances. In-situ monitoring systems that can be used to characterize, control, and improve the fabrication of these smaller features are therefore needed to meet increasing demands in precision and quality. This paper discusses the unique requirements of monitoring of precision manufacturing processes, and the suitability of acoustic emission (AE) as a monitoring technique at the precision scale. Details are then given on the use of AE sensor technology in the monitoring of precision manufacturing processes; grinding, chemical mechanical planarization (CMP) and ultraprecision diamond turning in particular.

Quantitative Relationships for Acoustic Emission from Orthogonal Metal Cutting

Theoretical relationships have been drawn between acoustic emission (AE) and the metal cutting process parameters by relating the energy content of the AE signal to the plastic work of deformation which generates the emission signals. The RMS value of the emission signal is expressed in terms of the basic cutting parameters. Results are presented for 6061-T6 aluminum and SAE 1018 steel over the range of speeds 25.2 to 372 sfm (0.128 to 1.9 m/s) and rake angles 10 to 40 deg. Good correlation has been found between predicted and experimental signal energy levels. In addition, AE generation from chip contact along the tool face is studied and the AE energy level reflects the existence of chip sticking and sliding on the tool face, and indicates the feasibility of utilizing AE in tool wear sensing.