The cutting of silicon wafers using multi-diamond wire sawing is a critical stage in solar cell manufacturing due to brittleness of silicon. Improving the cutting process output requires an in-depth understanding of phenomena associated with cutting parameters. In order to investigate the influence of diamond wire sawing on surface integrity of monocrystalline silicon, a looped diamond wire was used and cutting parameters wire cutting speed, feed rate and wire tension were varied. The surface morphology was observed by scanning electron microscopy. Surface roughness S a was measured with a non-contact profilometer. The brittle-ductile transition was identified by presence of residual phases on sawn surface. A bevel-polishing method was employed to determine the microcrack depth. The results show that with higher feed rate the surface presents deeper and wider craters because of deeper penetration of diamond grain. On increasing wire cutting speed, there were more regions formed in ductile mode. The higher S a values was observed on increasing both feed rate and wire tension, while S a decreased with an increase in wire cutting speed. The brittle mode was predominant with an increase in feed rate, resulting in Si-I phase in regions formed in fragile mode. Material removal in ductile mode led to appearance of a-Si phase at high wire cutting speed. No significant effect was observed on increasing wire tension. Subsurface microcracks mainly initiating from bottom of grooves generated by cutting mechanism. The most appropriate set of cutting parameters is the lowest feed rate and wire tension and highest wire cutting speed.
The use of machine vision systems for quality control of reflective metal surfaces has increased over the years with new systems that combine higher resolution cameras and better illumination of inspected objects. With the advances in artificial intelligence pattern recognition of images, the integration of machine vision systems in a manufacturing line for accurate automatic classification of defects would work towards the application of a smart manufacturing concept. To investigate the feasibility of such integration, a vision system that combines a 4K camera and chromatic confocal technology was employed to analyze surfaces of copper parts after the laser machining process. By the application of three machine-learning algorithms (decision trees, random forest and multi-layer perceptron) on features extracted from the Sobel edge detector, segmentation of defects has been performed using the Weka segmentation plugin. A simple convolutional neural network (CNN) was also applied for the classification of defects. Later on, using smart data rather than big data, transfer learning (TL) has been successfully performed with retraining the mobilenet-v1 model, which is based on CNN. This lean learning process can be implemented in devices that are limited by their computation resources. The maximum average of validation accuracy achieved using TL trained over 500 epochs was 90.5%. Whereas for the simple CNN classification models, the best validation accuracy was achieved by a model with a batch size equal to ten and with 40% of validation data with an average equal to 98.7% over 500 epochs.
The multi-wire sawing of silicon using a steel wire coated with diamond grits (diamond wire) is an important process in the semiconductor and photovoltaic industry. The cut is performed in the industry by pushing a silicon ingot against a diamond wire web that moves in a pilgrim-mode (forwards and backwards). As the cut direction and cutting speed of the wire change multiple times during the operation, it is very difficult to perform a proper investigation of the cutting process. In order to study the multi-wire sawing of monocrystalline silicon (mono-Si), a new experimental setup has been proposed. Based on literature research and industry know-how, the main features necessary for the proposed wafering test rig have been defined as follows: i) use of a short wire looped segment; ii) cut with controllable constant wire speed and iii) permit to track specific diamond grains after each cut for wear analysis. In order to fulfill these prerequisites, the first step is to butt-weld diamond wires into looped shape in a wire outer diameter range of 200 < OD < 500 μm. The adopted solution is a resistance butt-welding device. In this device, the wire ends are clamped with tight alignment. By the application of electric current through the wire ends interface, heat is generated by Joule effect and the incandescent heated material is pressed together with controlled force, creating the joint. After the joint is formed, a lower electric current heats the joint for a tempering procedure to increase material ductility. The conceptual design of the device is presented, followed by details of the actual device. Butt-welds of diamond wires with OD = 200 μm and 350 μm were successfully done, resulting in welded joints with good alignment, and sufficient tensile strength for the endless wire sawing application.
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