Assessment of micro turning machine stiffness response and material characteristics by fuzzy rule based pattern matching of cutting force plots

Detection of tool–workpiece contact before the start of precision machining application is essential as it prevents tool breakage and aids in maintaining the accuracy of the machined workpiece. In this research, a wireless-aided three-axis accelerometer attached to a rotating micro-milling tool is used to detect tool–workpiece contact before the start of micro-milling operations. A three-axis accelerometer (ADXL345), an X-Bee pro wireless module and ATMEL328PP-U microcontroller along with other ancillaries were housed on a printed circuit board rigidly attached to a micro-milling tool using couplings. Subsequently, the micro-milling operation was conducted on three different materials, namely, aluminum, copper and brass, for three different revolutions per minute, depth of cut and feed velocity combinations. The accelerometer signals were received wirelessly in a personal computer. Impulsive change in accelerometer signal along Z-axis during machining indicated tool–workpiece contact. The depth of cut of the machined samples was measured using a profilometer. It was found that the setup was accurate in determining tool–workpiece contact at the start of micro-milling operations.

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“…The impulsive change was detected by slope angle–based criterion. 10 An overview of the same is presented next.…”

Section: Proposed Methodologymentioning

confidence: 99%

Tool–workpiece contact detection in micro-milling using wireless-aided accelerometer sensor

Roy

Mandal

Nagahanumaiah

2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…A similar method based on HMM was used in [41] for tool wear monitoring in micromilling. A fuzzy logic based approach was used by authors in [42] where they mapped the force patterns using pattern recognition to compute machine parameters dynamically thus aiding in tool condition monitoring.…”

Section: Use Of Cutting Force Measurementmentioning

confidence: 99%

Applicability of Tool Condition Monitoring Methods Used for Conventional Milling in Micromilling: A Comparative Review

Mandal

2014

Journal of Industrial Engineering

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Micromilling is a contact based material removal process in which a rotating tool with nose radius in microns is fed over a stationary workpiece. In the process small amount of material gets chipped off from the workpiece. Due to continuous contact between tool and workpiece significant damage occurs to the cutting tools. Mitigating tool damage to make micromilling systems more reliable for batch production is the current research trend. In macroscale or conventional milling process a number of methods have been proposed for tool condition monitoring. Few of them have been applied for micromilling. This paper reviews different methods proposed and used in last two decades for monitoring the condition of micromilling tools. Applicability of tool condition monitoring methods used in conventional milling has been compared with the similar ones proposed for micromilling. Further, the challenges and opportunities on the applicability issues have been discussed.

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“…Tool wear in micro turning reduces the surface integrity of the finished products and leads to higher machining stress which causes inaccurate machining due to tool holder deflection that proves harmful for the machine. 5,6 Furthermore, unexpected tool wear requires frequent alteration of the tool which demands untimely machine shutdown, thereby affecting the supply chain for the finished product. Therefore, a process that estimates tool wear dynamically using micro turning is of utter necessity, such that the aforesaid problems are alleviated and proper efficiency of the micro turning operation is achieved.…”

Section: Introductionmentioning

confidence: 99%

Tool strain–based wear estimation in micro turning using Bayesian networks

Mandal

Sharma

Pal

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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Estimation of tool wear in micro turning is important as it enhances the process fidelity and the surface quality of the job. In this work, a simple process is demonstrated that estimates the tool wear from strain data near the cutting edge of the tool tip for micro turning operations. The tool strain for tool with six different wear lengths, collected using fiber Bragg grating sensor, was preprocessed to generate a probability distribution. The strain and tool wear data were used as the training dataset. This training dataset was subjected to maximum likelihood estimation algorithm to obtain the conditional probability distribution table required for the functioning of a suitable Bayesian network. The Bayesian network was tested for estimation of tool wear using strain data as priors for three different experiments. The maximum error in tool wear estimation using this procedure was ;6 mm.

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Assessment of micro turning machine stiffness response and material characteristics by fuzzy rule based pattern matching of cutting force plots

Cited by 10 publications

References 10 publications

Tool–workpiece contact detection in micro-milling using wireless-aided accelerometer sensor

Tool–workpiece contact detection in micro-milling using wireless-aided accelerometer sensor

Applicability of Tool Condition Monitoring Methods Used for Conventional Milling in Micromilling: A Comparative Review

Tool strain–based wear estimation in micro turning using Bayesian networks

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