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  • 1.
    Beno, Tomas
    et al.
    University West, Department of Engineering Science, Division of Manufacturing Processes. University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing.
    Repo, Jari
    University West, Department of Engineering Science, Division of Mechanical Engineering and Natural Sciences.
    Pejryd, Lars
    Örebro Universitet.
    The Use of Machine Tool Internal Encoders as Sensors in a Process Monitoring System2013In: International Journal of Automation Technology, ISSN 1881-7629, E-ISSN 1883-8022, Vol. 7, no 4, p. 410-417Article in journal (Refereed)
    Abstract [en]

    Tool wear in machining changes the geometry of the cutting edges, which affects the direction and amplitudes of the cutting force components and the dynamics in the machining process. These changes in the forces and dynamics are picked up by the internal encoders and thus can be used for monitoring of changes in process conditions. This paper presents an approach for the monitoring of a multi-tooth milling process. The method is based on the direct measurement of the output from the position encoders available in the machine tool and the application of advanced signal analysis methods.

    The paper investigates repeatability of the developed method and discusses how to implement this in a process monitoring and control system. The results of this work show that various signal features which are correlated with tool wear can be extracted from the first few oscillating components, representing the low-frequency components, of the machine axes velocity signatures. The responses from the position encoders exhibit good repeatability, especially short term repeatability while the long-term repeatability is more unreliable.

  • 2.
    Bonilla Hernández, Ana Esther
    et al.
    University West, Department of Engineering Science, Research Enviroment Production Technology West.
    Beno, Tomas
    University West, Department of Engineering Science, Division of Production Engineering. University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing.
    Repo, Jari
    University West, Department of Engineering Science, Division of Manufacturing Processes.
    Wretland, Anders
    GKN Aerospace Engine Systems AB, Trollhättan, Sweden.
    Analysis of Tool Utilization from Material Removal Rate Perspective2015In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 29, p. 109-113Article in journal (Refereed)
    Abstract [en]

    An end of life strategy algorithm has been used to study a CNC program to evaluate how the cutting inserts are used in terms of their full utilization. Utilized tool life (UTL) and remaining tool life (RTL) were used to evaluate if the insert has been used to its limits of expected tool life, or contributing to an accumulated tool waste. It is demonstrated that possible means to improvement exists to increase the material removal rate (MRR), thereby using the insert until its remaining tool life is as close to zero as possible. It was frequently found that inserts were used well below their maximum performance with respect to cutting velocity.

  • 3.
    Bonilla Hernández, Ana Esther
    et al.
    University West, Department of Engineering Science, Research Enviroment Production Technology West.
    Beno, Tomas
    University West, Department of Engineering Science, Division of Manufacturing Processes. University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing.
    Repo, Jari
    University West, Department of Engineering Science, Division of Manufacturing Processes.
    Wretland, Anders
    GKN Aerospace Engine Systems AB, Trollhättan, Sweden.
    Integrated optimization model for cutting data selection based on maximal MRR and tool utilization in continuous machining operations2016In: CIRP - Journal of Manufacturing Science and Technology, ISSN 1755-5817, E-ISSN 1878-0016, Vol. 13, p. 46-50Article in journal (Refereed)
    Abstract [en]

    The search for increased productivity can be interpreted as the increase of material removal rate (MRR). Namely, increase of feed, depth of cut and/or cutting speed. The increase of any of these three variables, will increase the tool wear rate; therefore decreasing its tool life according to the same tool life criteria. This paper proposes an integrated model for efficient selection of cutting data for maximal MRR and maximal tool utilization. The results show that, it is possible to obtain a limited range of cutting parameters from where the CAM Programmer can select the cutting data assuring both objectives.

  • 4.
    Bonilla Hernández, Ana Esther
    et al.
    University West, Department of Engineering Science, Research Enviroment Production Technology West.
    Beno, Tomas
    University West, Department of Engineering Science, Division of Manufacturing Processes.
    Repo, Jari
    University West, Department of Engineering Science, Division of Manufacturing Processes.
    Wretland, Anders
    GKN Aerospace Engine Systems AB, Trollhättan, Sweden.
    Streamlining the CAM programming process by Lean Principles within the aerospace industryManuscript (preprint) (Other academic)
  • 5.
    Pejryd, Lars
    et al.
    University West, Department of Engineering Science.
    Repo, Jari
    University West, Department of Engineering Science.
    Beno, Tomas
    University West, Department of Engineering Science, Division of Production Engineering. University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing.
    Machine Tool Internal Encoders as Sensors for the Detection of Tool Wear2012In: Procedia CIRP: 3rd CIRP Conference on Process Machine Interactions, 29-30 October 2012, Nagoya, Japan., 2012, p. 46-51Conference paper (Refereed)
    Abstract [en]

    Tool wear in machining changes the geometry of the cutting edges, which effect the direction and amplitudes of the cutting forcecomponents and the dynamics in the machining process. These changes in the forces and dynamics are picked up by the internalencoders and thus can be used for monitoring of changes in process conditions. This paper presents an approach for the monitoringof a multi-tooth milling process. The method is based on the direct measurement of the output from the position encoders availablein the machine tool and the application of advanced signal analysis methods.

    The paper investigates repeatability of the method developed and how to detect wear in an individual tooth in a milling cutter. Theresults of this work show that various signal features which correlate with tool wear can be extracted from the first few oscillatingcomponents, representing the low-frequency components, of the machine axes velocities. The responses from the position encodersexhibit good repeatability, especially short term repeatability while the long-term repeatability is more unreliable. A worn toothincreases the irregularity in the encoder responses and can be identified at an early stage of the cut.

  • 6.
    Repo, Jari
    University West, Department of Engineering Science, Division of Mechanical Engineering.
    Condition Monitoring in Machining Using Internal Sensor Signals2012Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Condition monitoring of critical machine tool components and machining processes is a key factor to increase the availability of the machine tool and to achieve a more robust machining process. Any failure in the machining process and machine tool components may have negative effects on the final produced part. Instabilities in machining processes also shortens the life time of the cutting edges and the machine tool. The condition monitoring system may utilise information from several sources to facilitate the detection of disturbances in the machining process. To minimise additional complexity to the machining system, internal sensor signals for condition monitoring are used.

    The main contribution from this work is a further understanding of the measured responses from linear and angular position encoders during excitation of the machine tool structure. It is shown that the measured encoder responses contain the operational frequencies and this applies to both active and passive machine axes. The response from an active machine axis however, involves a more complex analysis. The fundamental principles on the extraction of the generated micro-vibrations (translational and torsional vibrations) from the linear and rotary encoders are presented. Various methods for their analysis in time domain, frequency domain and phase space domain are also presented. New extentions to the nonlinear numerical methods in order to facilitate the extraction of features from Poincaré sections are introduced.

    The experimental work shows that encoders are sensitive to small disturbances in the machining process. The possibility to use the proposed measurement method and numerical methods for tool wear detection in a milling operation has therefore been investigated. It is shown that tool wear can be detected and quantified by utilising the signals already available in machine tools.

  • 7.
    Repo, Jari
    University West, Department of Engineering Science, Division of Production Engineering.
    Condition monitoring of machine tools and machining processes using internal sensor signals2010Licentiate thesis, comprehensive summary (Other academic)
  • 8.
    Repo, Jari
    et al.
    University West, Department of Engineering Science, Division of Production Engineering.
    Beno, Tomas
    University West, Department of Engineering Science, Division of Production Engineering.
    Pejryd, Lars
    University West, Department of Engineering Science, Division of Production Engineering.
    Machine tool and process condition monitoring using Poincaré maps2010In: COMA'10, International Conference on Competitive Manufacturing: Stellenbosch, South Africa, 3-5 February 2010, 2010Conference paper (Other academic)
  • 9.
    Repo, Jari
    et al.
    University West, Department of Engineering Science, Division of Production Engineering.
    Beno, Tomas
    University West, Department of Engineering Science, Division of Production Engineering.
    Pejryd, Lars
    University West, Department of Engineering Science, Division of Production Engineering.
    New Aspects on Condition Monitoring of Machine Tools and Machining Processes2009In: The Swedish Production Symposium (SPS'09): 2-3 dec,  Göteborg, 2009, p. 27-33Conference paper (Other academic)
    Abstract [en]

    A general trend within the manufacturing community and in particular the aerospace industry is that the requirements on the manufacturing of components are subjected to ever tighter tolerances and surface integrity restrictions. In order to fulfil these requirements a lot of effort has been carried out to non destructive testing of the produced part. However, the final objective in the first place must be to assure a robust process. One way to achieve a robust process is to continuously monitor it. This may require additional sensors which increase complexity and adds cost. This paper presents suggestions and experimentally achieved results into the extended use of already existing sensors in the machine tool. The basic idea is to show how these sensors can provide additional information about the machine tool itself and how they can be used for monitoring the process as well as to support maintenance activities. The Poincaré analysis method is applied to the position encoder signals to reveal the underlying dynamics when the machine tool structure is excited with a periodically varying load.

  • 10.
    Repo, Jari
    et al.
    University West, Department of Engineering Science, Division of Mechanical Engineering.
    Pejryd, Lars
    University West, Department of Engineering Science, Division of Mechanical Engineering.
    Beno, Tomas
    University West, Department of Engineering Science, Division of Mechanical Engineering. University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing.
    Measurement method for the identification of individual teeth in milling operations2012In: CIRP - Journal of Manufacturing Science and Technology, ISSN 1755-5817, E-ISSN 1878-0016, Vol. 5, no 1, p. 26-32Article in journal (Refereed)
    Abstract [en]

    Internal sensors already available in the machine tools may prove to be an interesting approach to monitor the machining process. Accurate determination of the position of the individual tooth on a milling cutter is important to be able to correlate the measured responses from the machine tool position encoders to the tooth or teeth that may be the cause of the response.

    The aim of the work presented in this paper is to develop a measurement method to identify the individual tooth on a milling cutter by their angular position relative to a specified 0-degree direction. If the lower and upper bounds of the cutting zone are known, together with the actual spindle position and the starting time of the cut, it will be possible to track and identify which teeth are within the cutting zone at a given time in the following off-line analysis of the responses. This may simplify the task of finding potential correlations between the state of individual teeth on the milling cutter with measured responses from various sensors during the milling process. The proposed method is based on a reflectance detector and uses accurate position information provided by the position encoders.

    A validation of the measurement method is also presented which shows that the error of the estimated angular position is approximately +/- 0.15 degrees for the validation setup used in this case.

  • 11.
    Repo, Jari
    et al.
    University West, Department of Engineering Science, Division of Mechanical Engineering and Natural Sciences.
    Wretland, Anders
    GKN Aerospace Engine Systems AB, Trollhättan, Sweden.
    Beno, Tomas
    University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing.
    Tu, Juei-feng
    University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing. North Carolina State University, Department of Mechanical and Aerospace Engineering, Raleigh, North Carolina, United States.
    Detectability of various machining conditions by using internal encoder signals2016In: The 7th International Swedish Production Symposium, SPS16, Conference Proceedings: 25th – 27th of October 2016, Lund: Swedish Production Academy , 2016, p. 1-7Conference paper (Refereed)
    Abstract [en]

    Automated Tool Condition Monitoring (TCM) often relies on additional sensors sensitive to tool wear to achieve robust machining processes. The need of additional sensors could impede the implementation of tool monitoring systems in industry due to the cost and retrofitting difficulties. This paper has investigated the use of existing position encoder signals to monitor a special face turning process with constant feed per revolution and machining speed. A signal processing method by converting encoder signals into a complex-valued form and a new vibration signature extraction method based on phase function were developed to analyze the encoder signals in the frequency domain. The cumulative spectrum indicated that the spectral energy would shift from the lower to the higher frequency band with increasing cutting load. The embedded vibration signatures extracted from the encoder signals provided additional detectability of the machining condition with distinguishable spectral modes. This paper confirms the sensitivity of the encoder signals and more signatures could be extracted for tool wear detection in the future work.

  • 12.
    Repo, Jari
    et al.
    University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing.
    Wretland, Anders
    GKN Aerospace Engine Systems AB, Dept. 9634 – TL-3, SE-46181 Trollhättan, Sweden.
    Beno, Tomas
    University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing.
    Tu, Juei-feng
    University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing. North Carolina State University, Dept. of Mechanical and Aerospace Engineering, Raleigh, USA.
    In-Process Tool Wear Detection Using Internal Encoder Signals for Unmanned Robust Machining2016In: High Speed Machining, Vol. 2, no 1, p. 37-50Article in journal (Refereed)
    Abstract [en]

    Automated Tool Condition Monitoring (TCM) often relies on additional sensors sensitive to tool wear to achieve robust machining processes. The need of additional sensors could impede the implementation of tool monitoring systems in industry due to the cost and retrofitting difficulties. This paper has investigated the use of existing position encoder signals to monitor a special face turning process with constant feed per revolution and machining speed. A signal processing method by converting encoder signals into a complex-valued form and a new vibration signature extraction method based on phase function were developed to analyze the encoder signals in the frequency domain. The cumulative spectrum indicated that the spectral energy would shift from the lower to the higher frequency band with increasing cutting load. The embedded vibration signatures extracted from the encoder signals provided real-time detectability of the machining condition with distinguishable spectral modes. The embedded vibration signatures extracted from the encoder signals provided additional detectability of the machining condition with distinguishable spectral modes. In particular, tool chipping manifested itself as significant amplitude changes at a specific frequency band 20-30 Hz in the extracted vibration signatures. A new monitoring metric based on the XY-plane modulations combined with statistical process control charts was proposed and shown to be a robust tool wear and tool wear rate indicator. The results show that when tool chipping occurred, it could be detected in real-time when this this tool wear rate value jumped in combination with breach of the control limits. This confirms that internal encoder signals, together with the proposed metric, could be a robust in-process tool wear monitor.

1 - 12 of 12
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