Automatic Multiple Sensor Data Acquisition System in a Real-time Production Environment

Downey, Jonathan; Bombiński, Sebastian; Nejman, Mirosław; Jemielniak, Krzysztof

doi:10.1016/j.procir.2015.06.039

Cited by 28 publications

(11 citation statements)

References 22 publications

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“…Downey et al proposed a multiple sensor automatic data acquisition system to acquire vibration, cutting force, and acoustic emission signals, and the system is applicable to the computer numerical control (CNC) turning center of a real-time production environment. To ensure the quality of acquired signals, a cutting force sensor is mounted on the lower part of the tool holder of a tool condition monitoring system, and an accelerometer and acoustic emission sensors are mounted on its upper part [ 24 ]. González-Laguna et al analyzed vibration signals and found that their root mean square and the amplitude of a fast Fourier transform were correlated to the condition of the cutting tools in steel turning operations.…”

Section: Introductionmentioning

confidence: 99%

Development of Insert Condition Classification System for CNC Lathes Using Power Spectral Density Distribution of Accelerometer Vibration Signals

Huang

Yeh

2020

Sensors

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Insert conditions significantly influence the product quality and manufacturing efficiency of lathe machining. This study used the power spectral density distribution of the vibration signals of a lathe machining accelerometer to design an insert condition classification system applicable to different machining conditions. For four common lathe machining insert conditions (i.e., built-up edge, flank wear, normal, and fracture), herein, the insert condition classification system was established with two stages—insert condition modeling and machining model fusion. In the insert condition modeling stage, the magnitude features of the segmented frequencies were captured according to the power spectral density distributions of the accelerometer vibration signals. Principal component analysis and backpropagation neural networks were used to develop insert condition models for different machining conditions. In the machining model fusion stage, a backpropagation neural network was employed to establish the weight function between the machining conditions and insert condition models. Subsequently, the insert conditions were classified based on the calculated weight values of all the insert condition models. Cutting tests were performed on a computer numerical control (CNC) lathe and utilized to validate the feasibility of the designed insert condition classification system. The results of the cutting tests showed that the designed system could perform insert condition classification under different machining conditions, with a classification rate exceeding 80%. Using a triaxial accelerometer, the designed insert condition classification system could perform identification and classification online for four common insert conditions under different machining conditions, ensuring that CNC lathes could further improve manufacturing quality and efficiency in practice.

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Section: Introductionmentioning

confidence: 99%

Development of Insert Condition Classification System for CNC Lathes Using Power Spectral Density Distribution of Accelerometer Vibration Signals

Huang

Yeh

2020

Sensors

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“…According to [12], elements of the characteristic equation are searched, on the basis of which it is possible by analytical or numerical methods to determine the bag curves defining the stability limit:…”

Section: Determination Of the Stability Boundarymentioning

confidence: 99%

Active systems for monitoring the cutting process in the Industry 4.0 concept

2018

Self Cite

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The SBS ExactControl Card has been developed to meet today's requirements for comprehensive process monitoring. Six acoustic emission sensors can be connected, two can be operated simultaneously. It can be plugged into the SB-5500 or SB-5575 controllers, which are also capable of automatic and manual balancing, hydro-balancing and cards for basic acoustic emission monitoring.The ExactControl Card is multi-channel capable and has Profibus and Ethernet interfaces, and analogue and digital inputs/outputs. With this card, power, spindle current, vibration, temperature, torque and speed, as well as information from the controller, e.g. alarms and positions can be used individually or in parallel for process monitoring.

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“…Post-real-time monitoring of tool wear. 25 Drake et al introduced some machine condition monitoring signals recorded by the data acquisition system, emphasizing the causal relationship between machine components and signal characteristics, and demonstrating the applicability of vibration signal kurtosis as a measure of the spindle bearing state. 26 Zhang designed and developed a multimedia sensor network intelligent warehouse monitoring system based on multisensor set, distributed processing, and event trigger monitoring.…”

Section: Introductionmentioning

confidence: 99%

Research and development of monitoring system and data monitoring system and data acquisition of CNC machine tool in intelligent manufacturing

Guo

Sun

2020

International Journal of Advanced Robotic Systems

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Intelligent manufacturing as the development direction of the new generation manufacturing system has become a hot research topic. Computer numerical control (CNC) machine tools are the core manufacturing equipment in discrete manufacturing enterprises, collecting and monitoring the data is an important part of intelligent manufacturing workshops. It has a great significance to improve the production efficiency of enterprises and eliminate information islands. The purpose of this article is to solve the problems of data acquisition and monitoring of CNC machine tools in the manufacturing workshop of enterprises. This article uses FOCAS data acquisition method to research and develop the data acquisition and monitoring system of CNC machine tools in intelligent manufacturing workshop. The research results show that the equipment information model based on MTConnect protocol and FOCAS can solve the data acquisition and storage functions of CNC machine tools well. Using the object-oriented Petri net model, it can solve various uncertain factors in numerical control (NC) machining tasks and realize the monitoring function of CNC machining tasks in the workshop. Based on the NC program analysis, the calculation method of machining time in the NC program can determine the preventive maintenance cycle of the machine based on the machine fault information. Based on VS2013 development environment, Qt application framework and SQL Server 2012 database, the numerical control machine tool data acquisition and monitoring prototype system was developed, and the system was verified in the workshop to prove the effectiveness of the system.

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Automatic Multiple Sensor Data Acquisition System in a Real-time Production Environment

Cited by 28 publications

References 22 publications

Development of Insert Condition Classification System for CNC Lathes Using Power Spectral Density Distribution of Accelerometer Vibration Signals

Development of Insert Condition Classification System for CNC Lathes Using Power Spectral Density Distribution of Accelerometer Vibration Signals

Active systems for monitoring the cutting process in the Industry 4.0 concept

Research and development of monitoring system and data monitoring system and data acquisition of CNC machine tool in intelligent manufacturing

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