Quality and Inspection of Machining Operations: Review of Condition Monitoring and Cmm Inspection Techniques 2000 to Present

In order to consistently produce quality parts, many aspects of the manufacturing process must be carefully monitored, controlled, and measured. The methods and techniques by which to accomplish these tasks has been the focus of numerous studies in recent years. With the rapid advances in computing technology, the complexity and overhead that can be feasibly incorporated in any developed technique has dramatically improved. Thus, techniques that would have been impractical for implementation just a few years ago can now be realistically applied. This rapid growth has resulted in a wealth of new capabilities for improving part and process quality and reliability. In this paper, overviews of recent advances that apply to machining are presented. Moreover, due to the relative significance of two particular machining aspects, this review focuses specifically on research publications pertaining to using tool condition monitoring and coordinate measurement machines to improve the machining process. Tool condition has a direct effect on part quality and is discussed first. The application of tool condition monitoring as it applies to turning, drilling, milling, and grinding is presented. The subsequent section provides recommendations for future research opportunities. The ensuing section focuses on the use of coordinate measuring machines in conjunction with machining and is subdivided with respect to integration with machining tools, inspection planning and efficiency, advanced controller feedback, machine error compensation, and on-line tool calibration, in that specific order and concludes with recommendations regarding where future needs remain. TOOL CONDITION MONITORING An effective method and implementation of tool condition monitoring for cutting processes could yield significant cost savings for manufacturers. Sensor-based approaches for tool condition monitoring provide a means to assess the underlying tool condition during the cutting process itself; thus, achieving better process control, improved tool usage, less wear intensive usage of the machine tool and, consequently, more costefficient machining. The main issues to be addressed regarding utilizing sensory information for tool condition monitoring is the low signal-to-noise ratio that necessitates integration of sensing into the tool or tool holder, and the use of advanced signal processing, feature extraction and multi-sensor pattern recognition methods to extract the relevant information [1]. Sensor-based tool condition monitoring represents a significant area of Condition-Based Monitoring (CBM), where physical phenomena related to system degradation and faults are inferred based on a set of features and indicators extracted from sensor readings. Thorough reviews of research in CBM of mechanical systems are given in [2, 3, 4]. These papers survey several hundred publications addressing achievements related to data acquisition, data processing and maintenance decisionmaking. A more focused survey can be found in [5], reviewing the area of applications of wavelet-based analysis of nonstationary signals for fault feature extraction, singularity detection, noise reduction and extraction of weak signals, signal compression, system identification and other applications. Even though no machining process monitoring application is reported in [5], potential application of nonstationary signal analysis and advanced pattern recognition methods in cutting tool condition monitoring are tremendous