Utilizing drones in many applications such as video and photography, farms, search and rescue operations, construction industry for mapping and site monitoring has increased. However, their operating time is limited according to the capacity of their batteries. Therefore, many researchers attempt to enhance the operating time of drones. In this study, to the best of the authors' knowledge for the first time, the feasibility assessment of a laser-based wireless power transfer (WPT) mechanism is theoretically investigated on drone applications to increase their operating time. In this method, drones can be charged during operation wirelessly at a considerable distance with acceptable efficiency. The Analytical code is developed in Engineering Equation Solver (EES) software. The acquired results for the main components of the system have a good agreement with published papers of other researchers. In this study, three commercial photovoltaic (PV) materials were evaluated. The results show that an equipped drone with laser-based WPT system is able to approximately receive net power of 73.5, 62.6, and 33.2 W at a 500 m distance by GaAs, CdTe, and c-Si PV materials, respectively, while the laser module consumes around 600 W electrical power. Moreover, since the optical power of the laser is concentrated on PV panel, a two-phase cooling system is suggested for PV panel to reduce the temperature of the under radiate area. Results demonstrate that the temperature of GaAs PV material without a two-phase cooling system is approximately three times more than the temperature of PV panels with a two-phase cooling system at high power transfer rates.

Feasibility assessment of next-generation drones powering by laser-based wireless power transfer

Dross formation in laser cutting of electrical steel sheet in different environments, namely oil, alcohol, and air, was predicted and optimized using a deep neural network (DNN) and an improved grey wolf optimizer (I-GWO), respectively. Analyses using random forest method and response surface method showed that the cutting environment, laser power, pulse frequency, and cutting speed had a significant influence on the dross formation. In addition, cutting in oil leads to less dross formation than in alcohol and air. A stacked autoencoder method combined with a multi-objective grey wolf optimizer was employed to generate a pre-trained DNN, followed by a fine-tuning process to obtain the final DNN. The I-GWO was used to determine the optimal combination of process parameters for minimum dross formation. The DNN model proved its efficacy by showing very low values of mean absolute percentage error and very high values of absolute fraction of variation for the training, validation, and testing datasets. Moreover, the accuracy of the developed DNN model was higher than that of other artificial intelligence based methods, namely random vector functional link and support vector machine for regression, as evaluated by nine statistical criteria. The predicted optimal process parameters by the DNN and I-GWO algorithms were verified by validation experiments in which the minimum dross formation was generated. 

Prediction and optimization of dross formation in laser cutting of electrical steel sheet in different environments

Due to the variety of properties of the composites produced, determining the choice of the appropriate cutting technique is demanding. Therefore, it is necessary to know the problems associated with cutting operations, i.e., mechanical cutting (blanking), plasma cutting plasma, water jet cutting, abrasive water jet cutting, laser cutting and electrical discharge machining (EDM). The criterion for choosing the right cutting technique for a specific application depends not only on the expected cutting speed and material thickness, but it is also related to the physico-mechanical properties of the material being processed. In other words, the large variety of composite properties necessitates an individual approach determining the possibility of cutting a composite material with a specific method. This paper presents the achievements gained over the last ten years in the field of non-conventional cutting of metal-based and polymer-based composite materials. The greatest attention is paid to the methods of electrical discharge machining and ultrasonic cutting. The methods of high-energy cutting and water jet cutting are also considered and discussed. Although it is well-known that plasma cutting is not widely used in cutting composites, the authors also took into account this type of cutting treatment. The volume of each chapter depends on the dissemination of a given metal-based and polymer-based composite material cutting technique. For each cutting technique, the paper presents the phenomena that have a direct impact on the quality of the resulting surface and on the formation of the most important defects encountered. Finally, the identified current knowledge gaps are discussed. 

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Current Concepts for Cutting Metal-Based and Polymer-Based Composite Materials

• Cutting of electrical steel sheet is performed using pulsed fiber laser. • Laser power, pulse frequency, and cutting speed are important process parameters. • DNN models are developed for predicting laser cutting quality of electrical steel. • DNN and GA models are reliable and effective for predicting laser cutting quality. • Optimal combination of process parameters generates the best cutting quality. Prediction and optimization of geometrical qualities, namely roundness of circular cut and kerf width of square cut, for pulsed laser cutting of non-oriented electrical steel are performed using deep neural network (DNN) and genetic algorithm (GA). Analyses using random forest method and response surface method show that laser power, pulse frequency, and cutting speed significantly affect the cutting qualities and are properly used as input variables in the prediction models. A real-coded GA is employed to determine the optimal DNN architecture, and the final DNN models are obtained through pre-training and fine-tuning processes. A binary-coded GA is utilized to determine the optimal combination of process parameters for generating the optimum geometrical qualities. The developed DNN-GA models show great ability in prediction of the roundness and the kerf width, as demonstrated by a very low mean absolute percentage error (≤2.60%) and a very high absolute fraction of variation (≥0.9972) for training, validation, and testing datasets. In addition, the performance of the DNN-GA models is evaluated by means of nine statistical criteria in comparison with other artificial intelligence based models, namely random vector functional link network (RVFL) and support vector machine for regression (SVR) integrated with equilibrium optimizer (EO) and grey wolf optimizer (GWO). The results indicate that the performance of the DNN-GA models is better than that of the RVFL-EO, SVR-EO, RVFL-GWO, and SVR-GWO models. The predicted optimal geometrical qualities of the DNN-GA models are verified by validation experiments in which a combination of the smallest roundness and kerf width is generated. 

Prediction and optimization of geometrical quality for pulsed laser cutting of non-oriented electrical steel sheet

We present an in situ process monitoring approach for remote fiber laser cutting, which is based on evaluating images from a high-speed camera. A specifically designed image processing algorithm allows the distinction between complete and incomplete cuts by analyzing spectral and geometric information of the melt pool from the captured images of the high-speed camera. The camera-based monitoring system itself is fit to a conventional laser deflection unit for use with high-power fiber lasers, with the optical detection path being coaxially aligned to the incident laser. Without external illumination, the radiation of the melt from the process zone is recorded in the visible spectral range from the top view and spatially and temporally resolved. The melt pool size and emitted sparks are evaluated in dependence of machining parameters such as feed rate, cycles, and focus position during cutting electrical sheets.

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Image Processing Algorithm for In Situ Monitoring Fiber Laser Remote Cutting by a High-Speed Camera

An improved method based on fuzzy gain scheduling (FGS) control is developed in this study to tune the PID parameters and precisely control the working temperature in situ during laser cutting of non-oriented electrical steel sheets. Experiments on laser cutting of thin electrical steel sheets are performed to compare the cutting quality of kerf features using an open loop controller with a constant power, a conventional PID controller, and the proposed FGS controller. An unstable and inconsistent cutting quality of kerf with a larger heat affected zone (HAZ) and dross attachment is observed under open loop control due to a significant variation of working temperature and kerf width along the cutting path. The FGS controller shows better cutting performance and tracking response than the conventional PID controller by reducing the settling time as well as enhancing the control stability in the steady period. A better quality of finer and uniform kerf width with parallel kerf edges of a smaller HAZ and dross attachment are thus produced by the improved FGS control through real-time tuning of control parameters. Finally, the average kerf width is well correlated with the set-point temperature in FGS control by a linear equation.

An improved real-time temperature control for pulsed laser cutting of non-oriented electrical steel

Kerf width is one of the most important quality items in cutting of thin metallic sheets. The aim of this study was to develop a convolutional neural network (CNN) model for analysis and prediction of kerf width in laser cutting of thin non-oriented electrical steel sheets. Three input process parameters were considered, namely, laser power, cutting speed, and pulse frequency, while one output parameter, kerf width, was evaluated. In total, 40 sets of experimental data were obtained for development of the CNN model, including 36 sets for training with k-fold cross-validation and four sets for testing. Compared with a deep neural network (DNN) model and an extreme learning machine (ELM) model, the developed CNN model had the lowest mean absolute percentage error (MAPE) of 4.76% for the final test dataset in predicting kerf width. This indicates that the proposed CNN model is an appropriate model for kerf width prediction in laser cutting of thin non-oriented electrical steel sheets.

Dinh Tu Nguyen

Papers

An improved real-time temperature control for pulsed laser cutting of non-oriented electrical steel

Prediction of Kerf Width in Laser Cutting of Thin Non-Oriented Electrical Steel Sheets Using Convolutional Neural Network