The Experts below are selected from a list of 3750 Experts worldwide ranked by ideXlab platform

Thomas R. Kurfess - One of the best experts on this subject based on the ideXlab platform.

  • study of Spindle Power data with neural network for predicting real time tool wear breakage during inconel drilling
    Journal of Manufacturing Systems, 2017
    Co-Authors: Raphael Corne, Chandra Nath, Mohamed El Mansori, Thomas R. Kurfess
    Abstract:

    Abstract Digital manufacturing systems are determined to be a major key to enhance productivity and quality mainly due to real-time process monitoring and control capability with instant data processing. During machining, such systems are anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure of tool or machine, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys because catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of tool including the rake and/or flank faces and tool corner. Nowadays, Spindle Power data are easy to collect directly from modern machine tools and can be made available in production floor for such real-time data processing. This work aims to evaluate Spindle Power data for real-time tool wear/breakage prediction during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Spindle Power data were collected from the Power meter (also called load meter) to feed into the neural network (NN) for functional processing. To understand the reliability of the Spindle Power data, force data were also collected and compared. The results show that the trends of these two different types of data over cutting time are similar for any feed and speed combinations. The error in NN prediction from actual wear was found to be between 0.8–18.4% with Power data as compared to that between 0.4–17.9% with force data. Findings suggest that Spindle Power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus appreciate digital manufacturing systems.

  • Study of Spindle Power data with neural network for predicting real-time tool wear/breakage during inconel drilling
    Journal of Manufacturing Systems, 2017
    Co-Authors: Raphael Corne, Chandra Nath, Mohamed El Mansori, Thomas R. Kurfess
    Abstract:

    Abstract Digital manufacturing systems are determined to be a major key to enhance productivity and quality mainly due to real-time process monitoring and control capability with instant data processing. During machining, such systems are anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure of tool or machine, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys because catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of tool including the rake and/or flank faces and tool corner. Nowadays, Spindle Power data are easy to collect directly from modern machine tools and can be made available in production floor for such real-time data processing. This work aims to evaluate Spindle Power data for real-time tool wear/breakage prediction during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Spindle Power data were collected from the Power meter (also called load meter) to feed into the neural network (NN) for functional processing. To understand the reliability of the Spindle Power data, force data were also collected and compared. The results show that the trends of these two different types of data over cutting time are similar for any feed and speed combinations. The error in NN prediction from actual wear was found to be between 0.8–18.4% with Power data as compared to that between 0.4–17.9% with force data. Findings suggest that Spindle Power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus appreciate digital manufacturing systems.

  • Enhancing Spindle Power Data Application with Neural Network for Real-time Tool Wear/Breakage Prediction During Inconel Drilling☆
    Procedia Manufacturing, 2016
    Co-Authors: Raphael Corne, Chandra Nath, Mohamed El Mansori, Thomas R. Kurfess
    Abstract:

    Abstract Nowadays, digital manufacturing systems with real-time process monitoring and control are in high demand in industries for productivity and quality improvement. During machining, such a system is anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys as catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of the rake and flank faces. Spindle Power data are easy to collect from modern machine tools and can be made available for such real-time data processing. This work aims to evaluate and analyze Spindle Power data for real-time tool wear/breakage monitoring during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Power data were collected from the Power meter (also called load meter) of the machine Spindle to feed into the neural network (NN) for functional processing. As a counterpart, force data were also collected and processed to understand the reliability of the Spindle Power data. The results show that the trends of these two different types of data are similar for any feed and speed combinations. It is believed that such Spindle Power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus can enhance digital manufacturing systems.

  • enhancing Spindle Power data application with neural network for real time tool wear breakage prediction during inconel drilling
    Procedia Manufacturing, 2016
    Co-Authors: Raphael Corne, Chandra Nath, Mohamed El Mansori, Thomas R. Kurfess
    Abstract:

    Abstract Nowadays, digital manufacturing systems with real-time process monitoring and control are in high demand in industries for productivity and quality improvement. During machining, such a system is anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys as catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of the rake and flank faces. Spindle Power data are easy to collect from modern machine tools and can be made available for such real-time data processing. This work aims to evaluate and analyze Spindle Power data for real-time tool wear/breakage monitoring during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Power data were collected from the Power meter (also called load meter) of the machine Spindle to feed into the neural network (NN) for functional processing. As a counterpart, force data were also collected and processed to understand the reliability of the Spindle Power data. The results show that the trends of these two different types of data are similar for any feed and speed combinations. It is believed that such Spindle Power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus can enhance digital manufacturing systems.

  • Tool life predictions in milling using Spindle Power with the neural network technique
    Journal of Manufacturing Processes, 2016
    Co-Authors: Cyril Drouillet, Chandra Nath, Mohamed El Mansori, Jaydeep Karandikar, Anne-claire Journeaux, Thomas R. Kurfess
    Abstract:

    Abstract Tool wear is an important limitation to machining productivity and part quality. In this paper, remaining useful life (RUL) prediction of tools is demonstrated based on the machine Spindle Power values using the neural network (NN) technique. End milling tests were performed on a stainless steel workpiece at different Spindle speeds and Spindle Power was recorded. The NN curve fitting approach with different MATLAB™ training functions was applied to the root mean square Power (Prms) values. Sample Prms growth curves were generated to take into account uncertainty. The Prms value in the time domain was found to be sensitive to tool wear. Results show a good agreement between the predicted and true RUL of tools. The proposed method takes into account the uncertainty in tool life and the percentage increase in nominal Prms value during the RUL prediction. Using MATLAB™ on an Intel i7 processor, the computation takes 0.5 s Thus, the method is computationally inexpensive and can be incorporated for real time RUL predictions during machining.

Raphael Corne - One of the best experts on this subject based on the ideXlab platform.

  • study of Spindle Power data with neural network for predicting real time tool wear breakage during inconel drilling
    Journal of Manufacturing Systems, 2017
    Co-Authors: Raphael Corne, Chandra Nath, Mohamed El Mansori, Thomas R. Kurfess
    Abstract:

    Abstract Digital manufacturing systems are determined to be a major key to enhance productivity and quality mainly due to real-time process monitoring and control capability with instant data processing. During machining, such systems are anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure of tool or machine, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys because catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of tool including the rake and/or flank faces and tool corner. Nowadays, Spindle Power data are easy to collect directly from modern machine tools and can be made available in production floor for such real-time data processing. This work aims to evaluate Spindle Power data for real-time tool wear/breakage prediction during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Spindle Power data were collected from the Power meter (also called load meter) to feed into the neural network (NN) for functional processing. To understand the reliability of the Spindle Power data, force data were also collected and compared. The results show that the trends of these two different types of data over cutting time are similar for any feed and speed combinations. The error in NN prediction from actual wear was found to be between 0.8–18.4% with Power data as compared to that between 0.4–17.9% with force data. Findings suggest that Spindle Power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus appreciate digital manufacturing systems.

  • Study of Spindle Power data with neural network for predicting real-time tool wear/breakage during inconel drilling
    Journal of Manufacturing Systems, 2017
    Co-Authors: Raphael Corne, Chandra Nath, Mohamed El Mansori, Thomas R. Kurfess
    Abstract:

    Abstract Digital manufacturing systems are determined to be a major key to enhance productivity and quality mainly due to real-time process monitoring and control capability with instant data processing. During machining, such systems are anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure of tool or machine, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys because catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of tool including the rake and/or flank faces and tool corner. Nowadays, Spindle Power data are easy to collect directly from modern machine tools and can be made available in production floor for such real-time data processing. This work aims to evaluate Spindle Power data for real-time tool wear/breakage prediction during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Spindle Power data were collected from the Power meter (also called load meter) to feed into the neural network (NN) for functional processing. To understand the reliability of the Spindle Power data, force data were also collected and compared. The results show that the trends of these two different types of data over cutting time are similar for any feed and speed combinations. The error in NN prediction from actual wear was found to be between 0.8–18.4% with Power data as compared to that between 0.4–17.9% with force data. Findings suggest that Spindle Power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus appreciate digital manufacturing systems.

  • Enhancing Spindle Power Data Application with Neural Network for Real-time Tool Wear/Breakage Prediction During Inconel Drilling☆
    Procedia Manufacturing, 2016
    Co-Authors: Raphael Corne, Chandra Nath, Mohamed El Mansori, Thomas R. Kurfess
    Abstract:

    Abstract Nowadays, digital manufacturing systems with real-time process monitoring and control are in high demand in industries for productivity and quality improvement. During machining, such a system is anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys as catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of the rake and flank faces. Spindle Power data are easy to collect from modern machine tools and can be made available for such real-time data processing. This work aims to evaluate and analyze Spindle Power data for real-time tool wear/breakage monitoring during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Power data were collected from the Power meter (also called load meter) of the machine Spindle to feed into the neural network (NN) for functional processing. As a counterpart, force data were also collected and processed to understand the reliability of the Spindle Power data. The results show that the trends of these two different types of data are similar for any feed and speed combinations. It is believed that such Spindle Power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus can enhance digital manufacturing systems.

  • enhancing Spindle Power data application with neural network for real time tool wear breakage prediction during inconel drilling
    Procedia Manufacturing, 2016
    Co-Authors: Raphael Corne, Chandra Nath, Mohamed El Mansori, Thomas R. Kurfess
    Abstract:

    Abstract Nowadays, digital manufacturing systems with real-time process monitoring and control are in high demand in industries for productivity and quality improvement. During machining, such a system is anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys as catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of the rake and flank faces. Spindle Power data are easy to collect from modern machine tools and can be made available for such real-time data processing. This work aims to evaluate and analyze Spindle Power data for real-time tool wear/breakage monitoring during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Power data were collected from the Power meter (also called load meter) of the machine Spindle to feed into the neural network (NN) for functional processing. As a counterpart, force data were also collected and processed to understand the reliability of the Spindle Power data. The results show that the trends of these two different types of data are similar for any feed and speed combinations. It is believed that such Spindle Power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus can enhance digital manufacturing systems.

Eder S.n. Lopes - One of the best experts on this subject based on the ideXlab platform.

  • Nanocrystalline structural layer acts as interfacial bond in Ti/Al dissimilar joints produced by friction stir welding in Power control mode
    Scripta Materialia, 2020
    Co-Authors: Victor Ferrinho Pereira, Eduardo Bertoni Da Fonseca, A. M. S. Costa, Jefferson Bettini, Eder S.n. Lopes
    Abstract:

    Abstract One of the main drawbacks of welding dissimilar metal alloys is the potential massive precipitation of deleterious intermetallic compounds that occurs at the interface joint. Solid-state welding processes are therefore attractive, given the lower temperatures involved. In this work, dissimilar high strength aluminum and titanium alloys were friction-stir-welded using the Spindle Power control mode to reduce the heat input at the joint as much as possible. A microstructural characterization indicated that the chosen parameters (heat input of ~0.5 kJ/mm) produced welded joints without spatially continuous layers of deleterious intermetallic compounds.

T Debroy - One of the best experts on this subject based on the ideXlab platform.

  • tool geometry for friction stir welding optimum shoulder diameter
    Metallurgical and Materials Transactions A-physical Metallurgy and Materials Science, 2011
    Co-Authors: M Mehta, Amit Arora, T Debroy
    Abstract:

    The most important geometric parameter in the friction stir welding (FSW) tool design is the shoulder diameter, which is currently estimated by trial and error. Here, we report a combined experimental and theoretical investigation on the influence of shoulder diameter on thermal cycles, peak temperatures, Power requirements, and torque during FSW of AA7075-T6. An optimum tool shoulder diameter is identified using a three-dimensional, heat transfer and materials flow model. First, the predictive capability of the model is tested by comparing the computed values of peak temperature, Spindle Power, and torque requirements for various shoulder diameters against the corresponding experimental data. The change in the values of these variables with shoulder diameter is correctly predicted by the model. The model is then used to identify the optimum tool shoulder diameter that facilitates maximal use of the supplied torque in overcoming interfacial sticking. The tool with optimum shoulder diameter is shown to result in acceptable yield strength (YS) and ductility.

  • Tool Geometry for Friction Stir Welding—Optimum Shoulder Diameter
    Metallurgical and Materials Transactions A, 2011
    Co-Authors: M Mehta, Amit Arora, T Debroy
    Abstract:

    The most important geometric parameter in the friction stir welding (FSW) tool design is the shoulder diameter, which is currently estimated by trial and error. Here, we report a combined experimental and theoretical investigation on the influence of shoulder diameter on thermal cycles, peak temperatures, Power requirements, and torque during FSW of AA7075-T6. An optimum tool shoulder diameter is identified using a three-dimensional, heat transfer and materials flow model. First, the predictive capability of the model is tested by comparing the computed values of peak temperature, Spindle Power, and torque requirements for various shoulder diameters against the corresponding experimental data. The change in the values of these variables with shoulder diameter is correctly predicted by the model. The model is then used to identify the optimum tool shoulder diameter that facilitates maximal use of the supplied torque in overcoming interfacial sticking. The tool with optimum shoulder diameter is shown to result in acceptable yield strength (YS) and ductility.

Chandra Nath - One of the best experts on this subject based on the ideXlab platform.

  • study of Spindle Power data with neural network for predicting real time tool wear breakage during inconel drilling
    Journal of Manufacturing Systems, 2017
    Co-Authors: Raphael Corne, Chandra Nath, Mohamed El Mansori, Thomas R. Kurfess
    Abstract:

    Abstract Digital manufacturing systems are determined to be a major key to enhance productivity and quality mainly due to real-time process monitoring and control capability with instant data processing. During machining, such systems are anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure of tool or machine, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys because catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of tool including the rake and/or flank faces and tool corner. Nowadays, Spindle Power data are easy to collect directly from modern machine tools and can be made available in production floor for such real-time data processing. This work aims to evaluate Spindle Power data for real-time tool wear/breakage prediction during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Spindle Power data were collected from the Power meter (also called load meter) to feed into the neural network (NN) for functional processing. To understand the reliability of the Spindle Power data, force data were also collected and compared. The results show that the trends of these two different types of data over cutting time are similar for any feed and speed combinations. The error in NN prediction from actual wear was found to be between 0.8–18.4% with Power data as compared to that between 0.4–17.9% with force data. Findings suggest that Spindle Power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus appreciate digital manufacturing systems.

  • Study of Spindle Power data with neural network for predicting real-time tool wear/breakage during inconel drilling
    Journal of Manufacturing Systems, 2017
    Co-Authors: Raphael Corne, Chandra Nath, Mohamed El Mansori, Thomas R. Kurfess
    Abstract:

    Abstract Digital manufacturing systems are determined to be a major key to enhance productivity and quality mainly due to real-time process monitoring and control capability with instant data processing. During machining, such systems are anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure of tool or machine, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys because catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of tool including the rake and/or flank faces and tool corner. Nowadays, Spindle Power data are easy to collect directly from modern machine tools and can be made available in production floor for such real-time data processing. This work aims to evaluate Spindle Power data for real-time tool wear/breakage prediction during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Spindle Power data were collected from the Power meter (also called load meter) to feed into the neural network (NN) for functional processing. To understand the reliability of the Spindle Power data, force data were also collected and compared. The results show that the trends of these two different types of data over cutting time are similar for any feed and speed combinations. The error in NN prediction from actual wear was found to be between 0.8–18.4% with Power data as compared to that between 0.4–17.9% with force data. Findings suggest that Spindle Power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus appreciate digital manufacturing systems.

  • Enhancing Spindle Power Data Application with Neural Network for Real-time Tool Wear/Breakage Prediction During Inconel Drilling☆
    Procedia Manufacturing, 2016
    Co-Authors: Raphael Corne, Chandra Nath, Mohamed El Mansori, Thomas R. Kurfess
    Abstract:

    Abstract Nowadays, digital manufacturing systems with real-time process monitoring and control are in high demand in industries for productivity and quality improvement. During machining, such a system is anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys as catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of the rake and flank faces. Spindle Power data are easy to collect from modern machine tools and can be made available for such real-time data processing. This work aims to evaluate and analyze Spindle Power data for real-time tool wear/breakage monitoring during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Power data were collected from the Power meter (also called load meter) of the machine Spindle to feed into the neural network (NN) for functional processing. As a counterpart, force data were also collected and processed to understand the reliability of the Spindle Power data. The results show that the trends of these two different types of data are similar for any feed and speed combinations. It is believed that such Spindle Power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus can enhance digital manufacturing systems.

  • enhancing Spindle Power data application with neural network for real time tool wear breakage prediction during inconel drilling
    Procedia Manufacturing, 2016
    Co-Authors: Raphael Corne, Chandra Nath, Mohamed El Mansori, Thomas R. Kurfess
    Abstract:

    Abstract Nowadays, digital manufacturing systems with real-time process monitoring and control are in high demand in industries for productivity and quality improvement. During machining, such a system is anticipated to excerpt reliable data within a short time-lapse, monitor tool wear progress, anticipate its wear and breakage, alert the machinist in real time to avoid unexpected failure, and help obtaining quality products. This is vital, especially, when drilling Ni-/Ti-based superalloys as catastrophic failure and premature breakage of tools occur in random manner due to aggressive welding and chipping of the rake and flank faces. Spindle Power data are easy to collect from modern machine tools and can be made available for such real-time data processing. This work aims to evaluate and analyze Spindle Power data for real-time tool wear/breakage monitoring during drilling of a Ni-based superalloy, Inconel 625. Experiments were performed by varying speed and feed. Power data were collected from the Power meter (also called load meter) of the machine Spindle to feed into the neural network (NN) for functional processing. As a counterpart, force data were also collected and processed to understand the reliability of the Spindle Power data. The results show that the trends of these two different types of data are similar for any feed and speed combinations. It is believed that such Spindle Power data integrated with the artificial intelligence (NN) system can be used for real-time tool wear/breakage monitoring and process control, thus can enhance digital manufacturing systems.

  • Tool life predictions in milling using Spindle Power with the neural network technique
    Journal of Manufacturing Processes, 2016
    Co-Authors: Cyril Drouillet, Chandra Nath, Mohamed El Mansori, Jaydeep Karandikar, Anne-claire Journeaux, Thomas R. Kurfess
    Abstract:

    Abstract Tool wear is an important limitation to machining productivity and part quality. In this paper, remaining useful life (RUL) prediction of tools is demonstrated based on the machine Spindle Power values using the neural network (NN) technique. End milling tests were performed on a stainless steel workpiece at different Spindle speeds and Spindle Power was recorded. The NN curve fitting approach with different MATLAB™ training functions was applied to the root mean square Power (Prms) values. Sample Prms growth curves were generated to take into account uncertainty. The Prms value in the time domain was found to be sensitive to tool wear. Results show a good agreement between the predicted and true RUL of tools. The proposed method takes into account the uncertainty in tool life and the percentage increase in nominal Prms value during the RUL prediction. Using MATLAB™ on an Intel i7 processor, the computation takes 0.5 s Thus, the method is computationally inexpensive and can be incorporated for real time RUL predictions during machining.