A multi-point flexible straightening process characterized by reciprocating bending is proposed.Specifically,the process is analyzed in terms of deformation mechanism and verified by numerical simulations and physical...A multi-point flexible straightening process characterized by reciprocating bending is proposed.Specifically,the process is analyzed in terms of deformation mechanism and verified by numerical simulations and physical experiments of the straightening of a series of metal profiles with different materials and initial shapes.Further,the relationship between the bending radius and the times of reciprocating bending required to unify the curvature is discussed,and the distribution of residual stress after straightening is analyzed.The results show that the reciprocating bending process can eliminate the difference of the initial curvature,make the curvature of each section tend to be uniform;the times of reciprocating bending to reach the uniform curvature decreases with the decrease of bending radius.The straightness of the straightened profile obtained from the experiment and simulation is less than 0.2%,demonstrating a good feasibility of this method.展开更多
The high flexibility of profile bending with hyperelastic pad enables it to be a promising method for small lot or single part production, especially for space frame and roof-rail parts in automotive and aerospace ind...The high flexibility of profile bending with hyperelastic pad enables it to be a promising method for small lot or single part production, especially for space frame and roof-rail parts in automotive and aerospace industries. Bending of two aluminum profiles with different sections was carried out to investigate the effect of main process parameters on the bending process. Results show that the shape of the cross-section and its relative thickness and section modulus in bending are the main factors that determine the bending properties of the profiles. Roller stroke, properties of polyurethane pad and constraints on profiles are key factors that determine the bending radius and section deformation of bent profiles. Failures and quality problems met in experiments were also analyzed.展开更多
Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the...Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.展开更多
基金financially supported by the National Natural Science Foundation of China(No.52005431)the National Natural Science Foundation of Hebei Province,China(No.E2020203086)the National Major Science and Technology Project of China(No.2018ZX04007002).
文摘A multi-point flexible straightening process characterized by reciprocating bending is proposed.Specifically,the process is analyzed in terms of deformation mechanism and verified by numerical simulations and physical experiments of the straightening of a series of metal profiles with different materials and initial shapes.Further,the relationship between the bending radius and the times of reciprocating bending required to unify the curvature is discussed,and the distribution of residual stress after straightening is analyzed.The results show that the reciprocating bending process can eliminate the difference of the initial curvature,make the curvature of each section tend to be uniform;the times of reciprocating bending to reach the uniform curvature decreases with the decrease of bending radius.The straightness of the straightened profile obtained from the experiment and simulation is less than 0.2%,demonstrating a good feasibility of this method.
文摘The high flexibility of profile bending with hyperelastic pad enables it to be a promising method for small lot or single part production, especially for space frame and roof-rail parts in automotive and aerospace industries. Bending of two aluminum profiles with different sections was carried out to investigate the effect of main process parameters on the bending process. Results show that the shape of the cross-section and its relative thickness and section modulus in bending are the main factors that determine the bending properties of the profiles. Roller stroke, properties of polyurethane pad and constraints on profiles are key factors that determine the bending radius and section deformation of bent profiles. Failures and quality problems met in experiments were also analyzed.
基金supported by the Spanish Ministry of Science and Innovation under Projects PID2022-137680OB-C32 and PID2022-139187OB-I00.
文摘Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.
