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

Rubio, Eva María; Teti, Roberto

doi:10.1007/s10845-008-0102-8

Cited by 34 publications

(20 citation statements)

References 38 publications

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“…This monitoring technology, however, has not been exhaustively investigated for process monitoring in machining, even though it is extensively used by machine tool operators for real-time decision making. In [133,[144][145][146], audible sound energy generated by milling and band sawing of Al alloy and C steel under different process and tool conditions was analysed in the frequency domain by a real-time spectrum analyser to develop an automatic process monitoring system based on inexpensive sound sensors. Signal analysis was carried out by suppressing the noise generated by the machine and the environment from the sound emitted during machining.…”

Section: Nn Applications To Sensor Monitoring Of Machiningmentioning

confidence: 99%

Advanced monitoring of machining operations

et al. 2010

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Section: Nn Applications To Sensor Monitoring Of Machiningmentioning

confidence: 99%

Advanced monitoring of machining operations

et al. 2010

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“…Paper [22] describes the application of sound signals for the monitoring of tool wear in the milling process. A sound signal has also been used by [23] in the turning process and by [24] in monitoring the milling process. The authors of [25] also use signals to evaluate cutting process stability.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the condition of the cutting tool using self-powering wireless sensor technologies

Ostaševičius

Jurenas

Augutis

et al. 2016

Int J Adv Manuf Technol

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An effective system for monitoring the wear of the machine tool inserts could significantly contribute to saving costs in manufacturing. One of the most recent and popular of effective monitoring methods revolves around the use of sensing technologies for indirect estimation of tool wear. The sensory information is difficult to collect from machine tools due to the extremely poor signal-to-noise ratio of the relevant tool wear-related information because the milling operation is of interrupted nature since the work piece is in contact with the tool edge several times per second. Another issue is the varying thickness of the chip during the penetration of the work piece. Yet, many challenges still impede the practical application of this method for industrial environment. Therefore, the paper presents a method that could solve these challenges especially in industrial environment. The proposed selfpowered wireless sensor node is integrated in the structure of the cutting tool. The voltage generated from the cutting tool vibrations of the harvester exponentially rises when the capacitor is fully charged and wireless signal sent to the receiver. As the intensity of energy accumulation depends on the state of the cutting tool wear, it indicates and detects the tool condition. The proposed technique could be useful for the identification of the cutting tool quality and the relative tool-work piece position. The information about the variation of tool wear is beneficial for helping the manufacturers to control the cutting process, to minimize the product cost as well as to improve the machining quality and efficiency.

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“…The strong international competition that exists in the field of manufacturing requires intelligent systems for providing greater value to the process and/or product (Rubio and Teti 2009). Machining processes play a crucial role in the field of manufacture and, therefore, it is an open field to deal with any kind of process improvement (Liang et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

et al. 2010

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Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.

show abstract

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

Cited by 34 publications

References 38 publications

Advanced monitoring of machining operations

Advanced monitoring of machining operations

Monitoring the condition of the cutting tool using self-powering wireless sensor technologies

Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

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