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Emmanuel Stratakis - One of the best experts on this subject based on the ideXlab platform.

  • predictive modeling approaches in laser based Material Processing
    Journal of Applied Physics, 2020
    Co-Authors: Maria Christina Velli, George D Tsibidis, Alexandros Mimidis, Evangelos Skoulas, Yannis Pantazis, Emmanuel Stratakis
    Abstract:

    Predictive modeling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new Materials, processes, and structures. In the current study, previously unexplored predictive modeling in a key-enabled technology, the laser-based manufacturing, aims to automate and forecast the effect of laser Processing on Material structures. The focus is centered on the performance of representative statistical and machine learning algorithms in predicting the outcome of laser Processing on a range of Materials. Results on experimental data showed that predictive models were able to satisfactorily learn the mapping between the laser’s input variables and the observed Material structure. These results are further integrated with simulation data aiming to elucidate the multiscale physical processes upon laser–Material interaction. As a consequence, we augmented the adjusted simulated data to the experiment and substantially improved the predictive performance due to the availability of an increased number of sampling points. In parallel, an information-theoretic metric, which identifies and quantifies the regions with high predictive uncertainty, is presented, revealing that high uncertainty occurs around the transition boundaries. Our results can set the basis for a systematic methodology toward reducing Material design, testing, and production cost via the replacement of expensive trial-and-error based manufacturing procedures with a precise pre-fabrication predictive tool.

  • predictive modeling approaches in laser based Material Processing
    arXiv: Computational Physics, 2020
    Co-Authors: Maria Christina Velli, George D Tsibidis, Alexandros Mimidis, Evangelos Skoulas, Yannis Pantazis, Emmanuel Stratakis
    Abstract:

    Predictive modelling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new Materials, processes and structures. In the current study, previously-unexplored predictive modelling in a key-enabled technology, the laser-based manufacturing, aims to automate and forecast the effect of laser Processing on Material structures. The focus is centred on the performance of representative statistical and machine learning algorithms in predicting the outcome of laser Processing on a range of Materials. Results on experimental data showed that predictive models were able to satisfactorily learn the mapping between the laser input variables and the observed Material structure. These results are further integrated with simulation data aiming to elucidate the multiscale physical processes upon laser-Material interaction. As a consequence, we augmented the adjusted simulated data to the experimental and substantially improved the predictive performance, due to the availability of increased number of sampling points. In parallel, a metric to identify and quantify the regions with high predictive uncertainty, is presented, revealing that high uncertainty occurs around the transition boundaries. Our results can set the basis for a systematic methodology towards reducing Material design, testing and production cost via the replacement of expensive trial-and-error based manufacturing procedure with a precise pre-fabrication predictive tool.

Maria Christina Velli - One of the best experts on this subject based on the ideXlab platform.

  • predictive modeling approaches in laser based Material Processing
    Journal of Applied Physics, 2020
    Co-Authors: Maria Christina Velli, George D Tsibidis, Alexandros Mimidis, Evangelos Skoulas, Yannis Pantazis, Emmanuel Stratakis
    Abstract:

    Predictive modeling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new Materials, processes, and structures. In the current study, previously unexplored predictive modeling in a key-enabled technology, the laser-based manufacturing, aims to automate and forecast the effect of laser Processing on Material structures. The focus is centered on the performance of representative statistical and machine learning algorithms in predicting the outcome of laser Processing on a range of Materials. Results on experimental data showed that predictive models were able to satisfactorily learn the mapping between the laser’s input variables and the observed Material structure. These results are further integrated with simulation data aiming to elucidate the multiscale physical processes upon laser–Material interaction. As a consequence, we augmented the adjusted simulated data to the experiment and substantially improved the predictive performance due to the availability of an increased number of sampling points. In parallel, an information-theoretic metric, which identifies and quantifies the regions with high predictive uncertainty, is presented, revealing that high uncertainty occurs around the transition boundaries. Our results can set the basis for a systematic methodology toward reducing Material design, testing, and production cost via the replacement of expensive trial-and-error based manufacturing procedures with a precise pre-fabrication predictive tool.

  • predictive modeling approaches in laser based Material Processing
    arXiv: Computational Physics, 2020
    Co-Authors: Maria Christina Velli, George D Tsibidis, Alexandros Mimidis, Evangelos Skoulas, Yannis Pantazis, Emmanuel Stratakis
    Abstract:

    Predictive modelling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new Materials, processes and structures. In the current study, previously-unexplored predictive modelling in a key-enabled technology, the laser-based manufacturing, aims to automate and forecast the effect of laser Processing on Material structures. The focus is centred on the performance of representative statistical and machine learning algorithms in predicting the outcome of laser Processing on a range of Materials. Results on experimental data showed that predictive models were able to satisfactorily learn the mapping between the laser input variables and the observed Material structure. These results are further integrated with simulation data aiming to elucidate the multiscale physical processes upon laser-Material interaction. As a consequence, we augmented the adjusted simulated data to the experimental and substantially improved the predictive performance, due to the availability of increased number of sampling points. In parallel, a metric to identify and quantify the regions with high predictive uncertainty, is presented, revealing that high uncertainty occurs around the transition boundaries. Our results can set the basis for a systematic methodology towards reducing Material design, testing and production cost via the replacement of expensive trial-and-error based manufacturing procedure with a precise pre-fabrication predictive tool.

Alexandros Mimidis - One of the best experts on this subject based on the ideXlab platform.

  • predictive modeling approaches in laser based Material Processing
    Journal of Applied Physics, 2020
    Co-Authors: Maria Christina Velli, George D Tsibidis, Alexandros Mimidis, Evangelos Skoulas, Yannis Pantazis, Emmanuel Stratakis
    Abstract:

    Predictive modeling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new Materials, processes, and structures. In the current study, previously unexplored predictive modeling in a key-enabled technology, the laser-based manufacturing, aims to automate and forecast the effect of laser Processing on Material structures. The focus is centered on the performance of representative statistical and machine learning algorithms in predicting the outcome of laser Processing on a range of Materials. Results on experimental data showed that predictive models were able to satisfactorily learn the mapping between the laser’s input variables and the observed Material structure. These results are further integrated with simulation data aiming to elucidate the multiscale physical processes upon laser–Material interaction. As a consequence, we augmented the adjusted simulated data to the experiment and substantially improved the predictive performance due to the availability of an increased number of sampling points. In parallel, an information-theoretic metric, which identifies and quantifies the regions with high predictive uncertainty, is presented, revealing that high uncertainty occurs around the transition boundaries. Our results can set the basis for a systematic methodology toward reducing Material design, testing, and production cost via the replacement of expensive trial-and-error based manufacturing procedures with a precise pre-fabrication predictive tool.

  • predictive modeling approaches in laser based Material Processing
    arXiv: Computational Physics, 2020
    Co-Authors: Maria Christina Velli, George D Tsibidis, Alexandros Mimidis, Evangelos Skoulas, Yannis Pantazis, Emmanuel Stratakis
    Abstract:

    Predictive modelling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new Materials, processes and structures. In the current study, previously-unexplored predictive modelling in a key-enabled technology, the laser-based manufacturing, aims to automate and forecast the effect of laser Processing on Material structures. The focus is centred on the performance of representative statistical and machine learning algorithms in predicting the outcome of laser Processing on a range of Materials. Results on experimental data showed that predictive models were able to satisfactorily learn the mapping between the laser input variables and the observed Material structure. These results are further integrated with simulation data aiming to elucidate the multiscale physical processes upon laser-Material interaction. As a consequence, we augmented the adjusted simulated data to the experimental and substantially improved the predictive performance, due to the availability of increased number of sampling points. In parallel, a metric to identify and quantify the regions with high predictive uncertainty, is presented, revealing that high uncertainty occurs around the transition boundaries. Our results can set the basis for a systematic methodology towards reducing Material design, testing and production cost via the replacement of expensive trial-and-error based manufacturing procedure with a precise pre-fabrication predictive tool.

Evangelos Skoulas - One of the best experts on this subject based on the ideXlab platform.

  • predictive modeling approaches in laser based Material Processing
    Journal of Applied Physics, 2020
    Co-Authors: Maria Christina Velli, George D Tsibidis, Alexandros Mimidis, Evangelos Skoulas, Yannis Pantazis, Emmanuel Stratakis
    Abstract:

    Predictive modeling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new Materials, processes, and structures. In the current study, previously unexplored predictive modeling in a key-enabled technology, the laser-based manufacturing, aims to automate and forecast the effect of laser Processing on Material structures. The focus is centered on the performance of representative statistical and machine learning algorithms in predicting the outcome of laser Processing on a range of Materials. Results on experimental data showed that predictive models were able to satisfactorily learn the mapping between the laser’s input variables and the observed Material structure. These results are further integrated with simulation data aiming to elucidate the multiscale physical processes upon laser–Material interaction. As a consequence, we augmented the adjusted simulated data to the experiment and substantially improved the predictive performance due to the availability of an increased number of sampling points. In parallel, an information-theoretic metric, which identifies and quantifies the regions with high predictive uncertainty, is presented, revealing that high uncertainty occurs around the transition boundaries. Our results can set the basis for a systematic methodology toward reducing Material design, testing, and production cost via the replacement of expensive trial-and-error based manufacturing procedures with a precise pre-fabrication predictive tool.

  • predictive modeling approaches in laser based Material Processing
    arXiv: Computational Physics, 2020
    Co-Authors: Maria Christina Velli, George D Tsibidis, Alexandros Mimidis, Evangelos Skoulas, Yannis Pantazis, Emmanuel Stratakis
    Abstract:

    Predictive modelling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new Materials, processes and structures. In the current study, previously-unexplored predictive modelling in a key-enabled technology, the laser-based manufacturing, aims to automate and forecast the effect of laser Processing on Material structures. The focus is centred on the performance of representative statistical and machine learning algorithms in predicting the outcome of laser Processing on a range of Materials. Results on experimental data showed that predictive models were able to satisfactorily learn the mapping between the laser input variables and the observed Material structure. These results are further integrated with simulation data aiming to elucidate the multiscale physical processes upon laser-Material interaction. As a consequence, we augmented the adjusted simulated data to the experimental and substantially improved the predictive performance, due to the availability of increased number of sampling points. In parallel, a metric to identify and quantify the regions with high predictive uncertainty, is presented, revealing that high uncertainty occurs around the transition boundaries. Our results can set the basis for a systematic methodology towards reducing Material design, testing and production cost via the replacement of expensive trial-and-error based manufacturing procedure with a precise pre-fabrication predictive tool.

Jyoti Mazumder - One of the best experts on this subject based on the ideXlab platform.

  • role of zinc coating at liquid vapor interface during laser Material Processing of zinc coated steel
    Journal of Applied Physics, 2013
    Co-Authors: Jyoti Mazumder
    Abstract:

    In laser Material Processing, one of the major interests is characterizing interfacial phenomena induced by thermal phase changes of Materials. The interfacial characteristics in the laser Processing of multi-coated Materials show different behaviors compared to those of single Material Processing. The difference in thermo-physical properties of the coated and primary Materials induces the contrasting characteristics of multiple interfacial phenomena including temperature, recoil pressure, capillary force, and thermo capillary force. The influence of coating layer to the interfacial physics evolutions is difficult to be modeled mathematically when the laser beam penetrates the multi-coated Material layer by layer. This paper addresses the role of the zinc coating at the liquid-vapor interface during the laser Processing of zinc coated steel, as a representative case of multi-coated Materials. Computational modules incorporating the zinc layers were established and selectively applied at the locations wher...