Catálogo de publicaciones - libros

This chapter reviews major research developments over the past few decades in the monitoring and control of machining processes ( turning, milling, drilling, and grinding). The major research accomplishments are reviewed from the perspective of a hierarchical monitoring and control system structure, which considers servo, process, and supervisory control levels. The use and benefits of advanced signal processing and control methods ( Kalman filtering, optimal control, adaptive control) are highlighted, and illustrated with examples from research work conducted by the author and his co-workers. Also included are observations on how significant the research to date has been in terms of industrial impact, and thoughts on how this research area might develop in the future.

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.

Condition monitoring and diagnosis systems which are capable of identifying machining system defects and their location are essential for unmanned machining. Unattended (or minimally manned) machining would result in increased capital equipment utilization, thus substantially reducing manufacturing costs. A review of tool monitoring systems and techniques and their components is presented. The proposed algorithm for MPC fuzzy neural networks is a fast, effective, and simple method for dealing with multi-sensor, multi-class, overlapped classification problems. Two case studies are presented on Multiple Principle Component fuzzy neural networks for tool condition monitoring in turning and drilling. Experimental application of this method yielded a success rate up to 97%. A case study for online detection of drill chipping is also included. The online detection methodology employs vibration signals to detect tool chipping, based on the particle frequency being excited. The Continuous Wavelet Transformation method has proven effective, however, it is not able to generate an online monitoring classification script capable of analyzing its output map.

This chapter is dedicated to the description of monitoring systems for grinding processes. Grinding is by far the most important abrasive process with geometrically non-defined cutting edges and plays a prominent role to generate the final surface quality of machined parts. The monitoring systems will be discussed in terms of their ability to measure process quantities during manufacturing, or on the grinding wheel or the workpiece. Monitoring of peripheral units like dressing systems will also be discussed. After the description of different technical solutions an outline of adaptive control and intelligent grinding systems is provided.

Condition monitoring of machines provides knowledge about the condition of machines. Any deterioration in machine condition can be detected and preventive measures taken at an appropriate time to avoid catastrophic failures This is achieved by monitoring such parameters as vibration, wear debris in oil, acoustic emission etc. The changes in these parameters help in the detection of the development of faults, diagnosis of causes of problem and anticipation of failure. Maintenance/corrective actions can be planned accordingly. The application of condition monitoring in plants results in savings in maintenance costs, and improved availability and safety. The techniques covered in this chapter are performance, vibration, motor stator current, shock pulse, acoustic emission, thermography and wear debris monitoring. The instrumentation required, method of analysis and applications with some examples are explained. Signal processing techniques to gain more benefits of vibration monitoring are covered. Wear debris monitoring methods include magnetic plugs, ferrography, particle counter and spectrographic oil analysis.

Bearing failure is one of the foremost causes of breakdown in rotating machine, resulting in costly systems downtime. This chapter presents an overview of current state-of-the-art monitoring approaches for rolling element bearings. Issues related to sensors, signal processing as well as diagnostics and prognostics are discussed. This chapter also presents a brief discussion related to the typical failure modes of bearings. Such failures are more and more common on advanced, high speed, ultra precision production systems as higher spindle speeds are employed for increased accuracy, productivity and machining satiability. Models for rolling element bearing behavior are presented as well as mechanistic models for damage propagation. Examples are also presented from test systems to demonstrate the various approaches discussed in this chapter.

The effectiveness and reliability of measurement techniques for bearing condition monitoring are affected by both the locations of the sensors and the signal processing algorithms selected for defect feature extraction. This chapter describes a structural dynamics-based sensor placement strategy by investigating the mechanisms of signal propagation from the source of its generation to the sensor location. Numerical simulation of a group of sensors for measuring vibration measurement on two custom-designed bearing test beds is presented, and an approach to optimizing the sensor placement based on the Effective Independence () method is introduced. The chapter then comparatively investigates several commonly employed signal processing techniques for feature extraction, such as wavelet transform-based signal enveloping, the Wigner-Ville distribution, and the wavelet packet transform, and evaluates performance using vibration signals measured from the bearing test beds.

Sheet metal stamping is one of the most commonly used manufacturing processes. Every day, millions of parts are made by stamping, ranging from small battery caps to large automobile body panels. Yet, it is a difficult process involving the press, the dies (including the binder), the material (the blank) and the forming process with very large forces. Even with advanced technologies today, such as finite element modeling (FEM) and computer control, malfunctions occur from time to time. As a result, condition monitoring and fault diagnosis are important. This chapter presents research on monitoring and diagnosis of sheet metal stamping processes. It consists of five sections. Section 8.1 introduces some of the authors’ research on the sheet metal stamping process. Section 8.2 is a brief description of the sheet metal stamping process. Understanding this section is essential to the rest of the chapter. Section 8.3 presents an effective online monitoring method based on support vector regression (SVR). Section 8.4 gives a new diagnosis method based on infarred thermal imaging. Finally, Section 8.5 contains conclusions.

This chapter presents an in-depth study on the condition monitoring of rotating machinery using adaptive parametric modelling, focusing on the development of robust state indicators of gearboxes running from a brand new to breakdown state in a natural course, under varying load conditions. Three independent robust state indicators based on state-space representation of a time-varying autoregressive model and noise-adaptive Kalman filtering are proposed and compared with other state indicators considered in previous studies. The experimental validations make use of full lifetime vibration monitoring data of gearboxes under varying load conditions and analyze some critical properties of gear state indicators in real applications over the full lifetime horizon of gearboxes. The results show that the proposed three gear state indicators possess a highly effective and robust property in the state detection of a gearbox, which is independent of variable load conditions, as well as remarkable stability, early alarm for incipient fault and significant presence of fault effects. The proposed three gear state indicators can be directly employed by an online maintenance program as reliable quantitative condition covariates to make optimal maintenance decisions for rotating machinery.

This chapter outlines three generic types of signatures encountered in monitoring of manufacturing process and equipment, periodic type including modulation, transient type, and dynamics changing type, and then summarizes signal processing algorithms and their suitability for various types of signatures under four categories, time domain, frequency domain, time-frequency distribution and model based methods. Additionally, decision-making strategies are discussed