Abderrazak El Ouafi

In this paper, an integrated geometric and thermal-induced errors prediction approach for active error compensation in machine tools is proposed and evaluated. The proposed approach is based on a hybrid of physical and neural network predictive modeling to drive an adaptive position controller for real-time error compensation including geometric and thermal-induced errors. Error components are formulated as a three-dimensional error field in the time-space domain. This approach involves four key steps for its development and implementation: (i) simplified experimental procedure combining a multicomponent laser interferometer measurement system and sixteen thermal sensors for error components measurement, (ii) artificial neural network-based predictive modeling of both position-dependent and position-independent error components, (iii) tridimensional volumetric error mapping using rigid body kinematics, and finally (iv) implementation of the real-time error compensation. Assessed on a turning center, the proposed approach conducts a significant improvement of the machine accuracy. The maximum error is reduced from 30 µm to less than 3 µm under thermally varying conditions.

This study investigates the effect of tensile test temperatures, ranging from 300 °C to 600 °C, on the microstructure, mechanical properties, and fracture behavior of AISI H13 11 tool steel manufactured by laser powder bed fusion (LPBF) under three material conditions: As-Built (AB), Direct Double-Tempered (DTT), and 13 Quenched and Double-Tempered (QTT). Optical and SEM observations show that quenching before tempering leads to a more homogeneous microstructure. Full austenitization during quenching eliminates the laser track patterns and cellular structures characteristic of the AB and DTT conditions, resulting in a microstructure like that of conventionally processed material. Tensile test results reveal that, while all material conditions (AB, DTT, and QTT) perform similarly at lower temperatures (up to 300 °C), significant differences emerge at elevated temperatures. At 300 °C, AB, DTT, and QTT maintain 87.5%, 85.8%, and 83.1% of their room-temperature yield strength, respectively. However, beyond this point, the DTT condition clearly outperforms the others. QTT shows a sharp decline above 300 °C, retaining only ~24% of its yield strength, whereas AB and DTT maintain approximately 80%. The superior performance of DTT becomes more evident at higher temperatures: it retains 25% and 20% of its yield strength at 500 °C and 600 °C, respectively, higher than both AB and QTT.

Accurate prediction of weld bead geometry is critical for optimizing laser overlap welding of low-carbon galvanized steel, as it directly affects joint quality and mechanical performance. Traditional finite element method (FEM)-based models provide reliable predictions but are computationally expensive and impractical for real-time applications. This study presents an artificial neural network (ANN)-based predictive model trained on a combination of experimental data and validated FEM simulations to estimate key weld characteristics, including depth of penetration (DOP), weld bead width at the surface (WS), and weld bead width at the interface (WI). The ANN model was evaluated using various improved statistical metrics. Results demonstrated a strong correlation between ANN predictions and experimental measurements, with R² values exceeding 95% for WS and DOP and 92% for WI, and mean errors below 7%. A comparative analysis between ANN, FEM, and experimental data confirmed the model’s reliability across different welding conditions. Additionally, ANN significantly reduced computational time compared to FEM while maintaining high accuracy, making it a practical tool for real-time process optimization. These findings highlight the potential of ANN models as efficient alternatives to conventional simulation techniques in laser overlap welding applications. Future improvements may involve integrating real-time sensor data and deep learning techniques to further enhance predictive performance.

Contingency analysis plays an important role in assessing the static security of a network. Its purpose is to check whether a system can operate safely when some elements are out of service. In a real-time application, the computational time required to perform the calculation is paramount for operators to take immediate actions to prevent cascading outages. Therefore, the numerical performance of the contingency analysis is the main focus of this current research. In power flow calculation, when solving the network equations with a sparse matrix solver, most of the time is spent factorizing the Jacobian matrix. In terms of computation time, the symbolic factorization is the costliest operation in the LU (Lower-upper) factorization process. This paper proposes a novel method to perform the calculation with only one symbolic factorization using a full Newton–Raphson-based generic formulation and modular approach (GFMA). The symbolic factorization retained can be used during the iterations of any power flow contingency scenario. A computer study demonstrates that reusing the same symbolic factorization greatly reduces computation time and improves numerical performance. Power system security assessment under N-1 and N-2 contingency conditions is performed for the IEEE standard 54-bus and 108-bus to evaluate the numerical performance of the proposed method. A comparison with the conventional power flow method shows that the time required for the analysis is shortened considerably, with a minimum gain of 228%. The comparative analysis demonstrates that the proposed solution has better numerical performance for large-scale networks.

The growing implementation of aluminum alloys in industry has focused interest on studying transformation processes such as laser welding. This process generates different kinds of signals that can be monitored and used to evaluate it and make a quality analysis of the final product. Internal defects that are difficult to detect, such as porosity, are one of the most critical irregularities in laser welding. This kind of defect may result in a critical failure of the manufactured goods, affecting the final user. In this research, a porosity prediction method using a high-speed camera monitoring system and machine learning (ML) algorithms is proposed and studied to find the most performant methodology to resolve the prediction problem. The methodology includes feature extraction by high-speed X-ray analysis, feature engineering and selection, imbalance treatment, and the evaluation of the ML algorithms by metrics such as accuracy, AUC (area under the curve), and F1. As a result, it was found that the best ML algorithm for porosity prediction in the proposed setup is Random Forest with a 0.83 AUC and 75% accuracy, 0.75 in the F1 score for no porosity, and 0.76 in the F1 score for porosity. The results of the proposed model and methodology indicate that they could be implemented in industrial applications for enhancing the final product quality for welded plates, reducing process waste and product quality analysis time, and increasing the operational performance of the process.

This paper presents an experimental investigation of laser surface transformation hardening (LSTH) of 4340 steel spur gears using regression analysis. The experimental work is focused on the effects of various LSTH parameters on the hardness profile shape and the hardened depth variation. The investigations are based on a structured design of experiments and improved statistical analysis tools. The experimentations are carried out on AISI 4340 steel spur gears using a commercial 3 kW Nd:YAG laser system. Laser power, scanning speed, and rotation speed are used as process parameters to evaluate the variation of the hardened depth and to identify the possible relationship between the process parameters and the hardened zone physical and geometrical characteristics. Based on the experimental data and analysis of variance, the direct and interactive contributions of the process parameters on the variation of the hardness profile shape and the hardened depth are analyzed. The main effects and the interaction effects are also evaluated. The results reveal that all the process parameters are relevant. The cumulative contribution of the three parameters in the hardened depth variation represents more than 80% with a clear predominance of laser power. The contribution of the interactions between the parameters represents 12% to 16%. The resulting hardness values are relatively similar for all the experimental tests with about 60 HRC. The evaluation of the produced regression models for hardened depth prediction shows limited performance suggesting that the predictive modeling process can be improved.

Maximum hardness and hardened depth are the responses of interest in relation to the laser hardening process. These values define heat treatment quality and have a direct impact on mechanical performance. This paper aims to develop models capable of predicting the shape of the hardness profile depending on laser process parameters for controlling laser hardening quality (LHQ), or rather the response values. An experimental study was conducted to highlight hardened profile sensitivity to process input parameters such as laser power (P_L), beam scanning speed (V_S) and initial hardness in the core (H_C). LHQ modeling was conducted by modeling attributes extracted from the hardness profile curve using two effective techniques based on the punctual and geometrical approaches. The process parameters with the most influence on the responses were laser power, beam scanning speed and initial hardness in the core. The obtained results demonstrate that the geometrical approach is more accurate and credible than the punctual approach according to performance assessment criteria.