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Monocrystalline silicon wafers are widely used as the primary material for solar cell production in the photovoltaic industry, owing to their high efficiency and sleeker aesthetic. Wafering is the primary process towards wafer production. The existing wafering processes have limitations of cracking, chipping, and high kerf loss, arising the need for cost-efficient and precise methods to produce wafers. In the present experimental work, an attempt has been made to explore the potential of Wire Electro-discharge Machining (WEDM) for wafer production and to gain an understanding of the effects caused by pulse on time (Ton), pulse off time (Toff), peak current (Ip), wire tension (WT), and wire feed rate (WFR) during the wafering process to achieve the optimal parametric setting for attaining maximum wafering speed and minimum surface roughness. The work material used was P-type monocrystalline silicon. Wafers thickness of 250 μm was produced, avoiding edge chipping. The experimentation was planned according to the face-centered central composite design. The obtained results were statistically analyzed, and response surface methodology (RSM) was used to model the wafering speed and surface roughness. From the analysis of experimental results, it was noticed that at the higher level of Ton and Ip, a maximum wafering speed of 2.574 mm/min was attained. In contrast, the minimum surface roughness of 1.57 μm was achieved at the lower level of Ton and Ip. The most significant process variables that influence the wafering speed and surface roughness are the Ton, Toff, and Ip. Micrographs of the wafer surface reveal the presence of micro craters, but there was no evidence of micro-cracks. The multi-objective optimization of the responses was done using the desirability approach, and the optimized values of wafering speed and surface roughness attained were 0.989 mm/min and 1.57 μm, respectively.

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To keep up with the rising demand for silicon solar cells in the photovoltaic sector, an alternative slicing method that can achieve high throughput with minimal waste is required. In recent research efforts, Wire electro-discharge machining (WEDM) has become the possible alternative method for slicing. The experimental investigation focuses on slicing monocrystalline silicon using the WEDM process with a brass wire electrode of 250 μm in diameter. The face-centered central composite design was employed for planning and conducting experiments. The investigational experiments were conducted with five different process parameters serving as inputs: peak current, wire tension, wire feed rate, pulse on and off time. The response parameter measured was the slicing speed and the surface roughness. Further, comparisons were made between different kernel functions in support vector regression (SVR) for the prediction modelling of slicing speed and surface roughness. The difficulty in prediction modelling can be attributed to the complexity of the WEDM process, which is caused by the involvement of various process parameters. The primary purpose of this work is to determine the best predictive kernel among the linear, polynomial, radial basis function (Rbf), and sigmoid kernel functions based on the experimental data. The predictive performance of different kernel functions was evaluated and compared. Grid search was used for the hyper tuning of the kernel parameters. The radial basis function produces R2 of 99.751 % and 97.552 %, MSE values of 0.00046 and 0.00079, RSME values of 0.0215 and 0.02814, MAE values of 0.01645 and 0.01894, and MAPE values of 1.2 % and 0.9 % for slicing speed and surface roughness. Support vector regression with radial basis function gives better results in comparison to other kernel functions, which concludes that support vector regression with radial basis function is well suited for the prediction of slicing speed and surface roughness.

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