Predictive modelling for quality analysis becomes one of the most critical requirements for a continuous improvement of reliability, efficiency and safety of laser welding process. Accurate and effective model to perf...Predictive modelling for quality analysis becomes one of the most critical requirements for a continuous improvement of reliability, efficiency and safety of laser welding process. Accurate and effective model to perform non-destructive quality estimation is an essential part of this assessment. This paper presents a structured approach developed to design an effective artificial neural network based model for predicting the weld bead dimensional characteristic in laser overlap welding of low carbon galvanized steel. The modelling approach is based on the analysis of direct and interaction effects of laser welding parameters such as laser power, welding speed, laser beam diameter and gap on weld bead dimensional characteristics such as depth of penetration, width at top surface and width at interface. The data used in this analysis was derived from structured experimental investigations according to Taguchi method and exhaustive FEM based 3D modelling and simulation efforts. Using a factorial design, different neural network based prediction models were developed, implemented and evaluated. The models were trained and tested using experimental data, supported with the data generated by the 3D simulation. Hold-out test and k-fold cross validation combined to various statistical tools were used to evaluate the influence of the laser welding parameters on the performances of the models. The results demonstrated that the proposed approach resulted successfully in a consistent model providing accurate and reliable predictions of weld bead dimensional characteristics under variable welding conditions. The best model presents prediction errors lower than 7% for the three weld quality characteristics.展开更多
为解决综合能源生产单元(integrated energy production unit,IEPU)中燃煤机组碳捕集过程的高能耗问题,同时应对新能源不确定性对运行调度带来的挑战,该文提出一种考虑太阳能辅助碳捕集技术的IEPU随机低碳调度策略,旨在实现IEPU的多能...为解决综合能源生产单元(integrated energy production unit,IEPU)中燃煤机组碳捕集过程的高能耗问题,同时应对新能源不确定性对运行调度带来的挑战,该文提出一种考虑太阳能辅助碳捕集技术的IEPU随机低碳调度策略,旨在实现IEPU的多能协同与低碳运行。首先,对含太阳能辅助碳捕集热电联产单元(combined heat and power based on solar-assisted carbon capture,CHP-SACC)的能量流动与运行机理进行分析,并构建其运行模型;其次,考虑风电不确定性带来的影响,提出一种基于条件最小二乘生成对抗网络(conditional-least squares generative adversarial networks,C-LSGANs)的可再生能源场景生成方法来提高场景的生成质量;然后,考虑异质能流耦合约束、多元设备运行约束以及能量平衡约束等,以最大化系统运行收益期望为目标构建IEPU随机低碳调度模型;最后,在算例仿真中设置不同的运行策略验证所提低碳转型方案的有效性,并分析了能源价格、设备容量等因素对系统运行收益的影响。展开更多
文摘Predictive modelling for quality analysis becomes one of the most critical requirements for a continuous improvement of reliability, efficiency and safety of laser welding process. Accurate and effective model to perform non-destructive quality estimation is an essential part of this assessment. This paper presents a structured approach developed to design an effective artificial neural network based model for predicting the weld bead dimensional characteristic in laser overlap welding of low carbon galvanized steel. The modelling approach is based on the analysis of direct and interaction effects of laser welding parameters such as laser power, welding speed, laser beam diameter and gap on weld bead dimensional characteristics such as depth of penetration, width at top surface and width at interface. The data used in this analysis was derived from structured experimental investigations according to Taguchi method and exhaustive FEM based 3D modelling and simulation efforts. Using a factorial design, different neural network based prediction models were developed, implemented and evaluated. The models were trained and tested using experimental data, supported with the data generated by the 3D simulation. Hold-out test and k-fold cross validation combined to various statistical tools were used to evaluate the influence of the laser welding parameters on the performances of the models. The results demonstrated that the proposed approach resulted successfully in a consistent model providing accurate and reliable predictions of weld bead dimensional characteristics under variable welding conditions. The best model presents prediction errors lower than 7% for the three weld quality characteristics.
文摘为解决综合能源生产单元(integrated energy production unit,IEPU)中燃煤机组碳捕集过程的高能耗问题,同时应对新能源不确定性对运行调度带来的挑战,该文提出一种考虑太阳能辅助碳捕集技术的IEPU随机低碳调度策略,旨在实现IEPU的多能协同与低碳运行。首先,对含太阳能辅助碳捕集热电联产单元(combined heat and power based on solar-assisted carbon capture,CHP-SACC)的能量流动与运行机理进行分析,并构建其运行模型;其次,考虑风电不确定性带来的影响,提出一种基于条件最小二乘生成对抗网络(conditional-least squares generative adversarial networks,C-LSGANs)的可再生能源场景生成方法来提高场景的生成质量;然后,考虑异质能流耦合约束、多元设备运行约束以及能量平衡约束等,以最大化系统运行收益期望为目标构建IEPU随机低碳调度模型;最后,在算例仿真中设置不同的运行策略验证所提低碳转型方案的有效性,并分析了能源价格、设备容量等因素对系统运行收益的影响。
