Artificial intelligence in monitoring and the mechanics of machining

Hermann, Gyula

doi:10.1016/0166-3615(90)90113-4

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…. number of extracted features in the monitoring system n general variable, representing the number of samples (determined according to the sampling frequency and the cycle of revolution of every axis) or the number of possible faults or abnormalities P power P…k † covariance matrix for parameter estimation P c , P r , P f pressure of the pneumatic supply for clamping devices, the hydraulic oil for rotating devices and the hydraulic oil for feed drives r ij fuzzy probability or possibility that the ith fault or abnormality will occur when the jth feature is abnormal, or the fuzzy GoM R fuzzy matrix ‰r ij Š n £ m , 0 4 r ij 4 1 S j grade of interrelationship (GoI) of the jth feature in an unknown state t 0 , t 1 constants for limit values…”

Section: Notation a Imentioning

confidence: 99%

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A multisensor-based system for manufacturing process monitoring

Starr

Leung

2001

Proceedings of the Institution of Mechanical Engineers, Part B:

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Abstract:In order to improve the availability and productivity of manufacturing systems, process monitoring has become an important matter in modern automated plants. The traditional monitoring technologies are based on single sensors and are insufficient, especially for manufacturing processes of highly complicated and automated systems. Instead, multi-sensor based technologies are developing rapidly and are widely used for such monitoring tasks. This paper introduces a multi-sensor based process monitoring system for an existing machining centre. The system can monitor the major manufacturing process faults or abnormalities of the machining centre and provide maintenance planning through measuring and analysing multiple sensor parameters such as power, vibration, temperature and pressure of spindle, feed axes, and hydraulic and pneumatic systems of the machining centre. The general structure of the monitoring system and the implementation of the main steps of the monitoring system development are presented in detail.

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Section: Notation a Imentioning

confidence: 99%

“…metal cutting, have been used as the starting point. Early monitoring of manufacturing processes relied on the sensing and processing of a single parameter based on a single sensor [1]. Cutting force and other related parameters like spindle torque or main drive current are very popular [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

A multisensor-based system for manufacturing process monitoring

Starr

Leung

2001

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Early condition monitoring systems relied on the sensing and processing of a single parameter by a single sensor [16]. This kind of monitoring strategy is simple and has poor usability and often brings about false or incomplete diagnosis.…”

Section: Condition Monitoringmentioning

confidence: 99%

A systematic approach to integrated fault diagnosis of flexible manufacturing systems

Starr

Zhou

et al. 2000

International Journal of Machine Tools and Manufacture

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A Flexible Manufacturing System (FMS) is an application of modern manufacturing techniques. Like other manufacturing equipment, the success of a FMS is very much dependent upon its trouble-free operation. It is crucial to monitor all the possible faults or abnormalities in real time and when a fault is detected, to react quickly in order to maintain the productivity of the FMS. Because of the FMS's complexity, the functionally complete diagnosis of a FMS should be based on all the available information and various advanced diagnostic techniques so as to get a satisfactory result. This paper proposes a systematic approach to fault diagnosis of FMS's integrating condition monitoring, fault diagnosis and maintenance planning. An intelligent integrated fault diagnosis system is designed with a modular and reconfigurable structure. The implementation of the integrated diagnosis system is presented in detail. The system can monitor the major conditions and diagnose the major faults of a FMS, and give corresponding maintenance planning as well. The developed system has been applied to an existing FFS-1500-2 FMS in Zhengzhou Textile Machinery Plant and has achieved good results.

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“…FEM is used for simulating the process of machining. The main advantage of the simulation is that different cutting variables such as stress, temperature strain rate, etc., that are difficult to measure through experimentation, can be easily measured by simulation (Ulutan 2009, Grzesik 2004, Balazinski 2002, Hermann 1990.…”

Section: Direct Tool Wear Measurementmentioning

confidence: 99%

Tool Condition Monitoring-A Review

Proceedings of the International Conference on Industrial Engineering and Operations Management

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The condition of the tool is the most significant factor in the manufacture of a quality product. Manufacturing a product involves different cutting tools, and the tool will wear out during product manufacturing. Using a worn-out tool would result in the formation of the defective product. Thus monitoring of cutting tools assumed special significance. Many researchers have been contributing techniques for tool wear measurement. An attempt has been made in this paper (i) for exploring the literature for the different tool wear monitoring techniques, (ii) advantages and limitations of the different techniques, (iii) research gaps, and to provide directions for future research. Finally, the paper concludes that much research is required in the design and development of methods using the application of machine learning and artificial intelligence for achieving accuracy, robustness, and economy in tool condition monitoring. This would help improve the reliability of the machine tool and help deliver quality products to the customer.

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Artificial intelligence in monitoring and the mechanics of machining

Cited by 11 publications

References 7 publications

A multisensor-based system for manufacturing process monitoring

A multisensor-based system for manufacturing process monitoring

A systematic approach to integrated fault diagnosis of flexible manufacturing systems

Tool Condition Monitoring-A Review

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