LabVIEW based tool condition monitoring and control for CNC lathe based on parameter analysis

Bhagat, Sanket N.; Nalbalwar, Sanjay L.

doi:10.1109/rteict.2016.7808058

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…Nine outputs are selected and given to the raspberry pi via the relay. Pins 17,18,23,16,25,12,22,27, 24, 13 are provided as input for manual mode, auto mode, the part program running, cycle start, rejected part count, federate override greater than 100, spindle output, machine output, alarm status, and machine ready respectively. The raspberry pi is programmed using python to collect and analyze the data.…”

Section: Methodsmentioning

confidence: 99%

“…In our work, we will be monitoring the Sinumerik CNC with the help of a raspberry pi and thereby showing the machine's utilization patterns. The process of monitoring is initiated by selecting the list of parameters to be monitored [16]. The raspberry pi is programmed with the help of the programming language Python to meet the requirements.…”

Section: Introductionmentioning

confidence: 99%

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Cost Effective Digitalization Solution for Sinumerik CNC System To Increase The Transparency and Utilization of The Machine

Anand¹,

Philip²,

Eswaran³

2019

IJRTE

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In this work, we fetch the current trends in industrial automation and data exchange technology adopted in Computer Numerical Control (CNC) machine and mitigate the features in in a cost-effective manner. The current trend is Industry 4.0, uses cloud-based systems for information and data exchanges in machine to machine communication. This methodology is reliable, but expensive and can be afforded only by large scale companies. In order to provide the data transparencies at low cost, we utilize a low-cost computing system using Python language for small scale industry. This technique was implemented in the existing CNC machine and the machine parameters such as Machine Operating Mode, Cycle Time, Part Count, Feed rate, Spindle Running Hours, Machine Running Hours, and Machine Utilization Hours are monitored. Graphical user interface (GUI) screens are developed to help human machine interface. Acquired real-time machine data will help boost transparency and help the operator/ user for smart decision making. The IIoT (Industrial Internet of Things) technology helps to connect more numbers of such machines, results in increased machine utilization and productivity through continuously monitoring and analyzes.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cost Effective Digitalization Solution for Sinumerik CNC System To Increase The Transparency and Utilization of The Machine

Anand¹,

Philip²,

Eswaran³

2019

IJRTE

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“…More and more papers about cutting tools have been published [3], [4], meaning that this subject is receiving more and more attention. Bhagat and Nalbalwar [5] use three types of sensors (temperature sensor, accelerometer and current sensor) and Labview software in the experiment. Three tool wear conditions, i.e.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Application of Synchronized Chaotic Systems of Different Fractional Orders for Cutting Tool Wear Diagnosis and Identification

et al. 2019

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Machining refers to a variety of processes, in which a cutting tool is used to remove the unwanted material from a workpiece. Tool wear, advertently or inadvertently, occurs after long-time use. It is crucial to monitor the tool wear so that the cutting tool can deliver the best performance and meet the technological challenges nowadays. In this paper, through different fractional-order chaotic systems, i.e., Chen-Lee, Lorenz, and Sprott, extension theory is proposed to predict the tool life. The results of the three chaotic systems are compared. The centroid of the 2-D plane of dynamic errors is used as the characteristics. Four wear states are defined in accordance with different levels of surface roughness, i.e., normal, slight, moderate, and severe. The boundaries of the four states are identified according to the locations of the centroid generated with the systems of different fraction orders. The boundaries are then fed into the extension model, and the relational function calculation is performed. In this way, the identification of tool state can be easily achieved. The experiment results indicate that Chen-Lee system and the Lorenz systems exhibit the same diagnosis rate (97.375%), higher than that of the Sprott system (35.75%). It is demonstrated that the two chaotic systems are fit for use with the method proposed in this paper. It is also proven that Chen-Lee and Lorenz fractional-order master-slave chaotic systems are very effective for tool life monitoring. The robustness of diagnosis is also greatly improved. INDEX TERMS Fractional order chaotic system, cutting, wear, machine tool.

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“…The actual condition of a cutting tool can be described as normal, worn, or damaged. 1,2 Normal processing wears a tool, and to clearly describe its state, we used the following descriptions in this study: normal, slight wear, medium wear, and severe wear, which more accurately indicates tool state.…”

Section: Introductionmentioning

confidence: 99%

Intelligent real-time monitoring of Computer Numerical Control tool wear based on a fractional-order chaotic self-synchronization system

Chen

2018

Journal of Low Frequency Noise, Vibration and Active Control

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Tool wear and damage are unavoidable in machines which operate for long periods. The main objective of this study was the prediction and understanding of tool wear ahead of time. To achieve this a real-time monitoring system was needed which incidentally also improves product yield and efficiency. Traditionally, tool wear and working life have been estimated by past experience. To monitor the state of the tool, we used a method employing fractional-order chaotic selfsynchronization system matched with extension theory to analyze, extract, and measure tool vibration signals. The fractional-order Chen-Lee chaotic system was used to detect differences in micro vibrations, resulting from different degrees of tool wear, and these were introduced into the master and slave systems. The extreme changes produced by micro differences in the chaotic system, and matter element module design based on the extension theory, were used to accurately determine tool status. Chaos theory, wavelet packet analysis, and Fast Fourier transform were then used to compare the results. This method can reduce the number of sensors and the time required for real-time monitoring of expected life compared to previous determination methods where temperature and current diagnostics must be included.

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LabVIEW based tool condition monitoring and control for CNC lathe based on parameter analysis

Cited by 9 publications

References 5 publications

Cost Effective Digitalization Solution for Sinumerik CNC System To Increase The Transparency and Utilization of The Machine

Cost Effective Digitalization Solution for Sinumerik CNC System To Increase The Transparency and Utilization of The Machine

A Study on the Application of Synchronized Chaotic Systems of Different Fractional Orders for Cutting Tool Wear Diagnosis and Identification

Intelligent real-time monitoring of Computer Numerical Control tool wear based on a fractional-order chaotic self-synchronization system

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