Modeling for a Small-hole Drilling Process of Engineering Plastic PEEK by Taguchi-based Neural Network Method

Engineering plastics have specific properties in the strength, hardness, impact resistance, and aging persistence, often used for structural plates and electronic components. However, the holes made by the drilling process always shrink after the cutting heat dispersion due to their high thermal expansion coefficient. Especially for small-hole fabrication, drilling parameters must be discussed thoughtfully to acquire a stable hole quality. This study developed parameter models by the Taguchi-based neural network method to save the experimental resources on drilling of engineering plastic, polyetheretherketone (PEEK). A three-level full-factorial orthogonal array experiment, L27, was first conducted for minimizing the thrust force, hole shrinkage in diameter, and roundness error. The experiments were operated by a peck-drilling process with cyclic lubricant, and the diameter was 1 mm. In terms of the network modeling, four variables were designated to the input layer neurons including the spindle speed, depth of peck-drilling, feed rate, and thrust force detected; and that of the output layer were the diameter shrinkage and roundness of the hole drilled. The models were trained by a stepped-learning procedure to expand the network’s field information stage by stage. After three stages of training, the models developed can provide precise simulations for the network’s training sets and accurately predict the hole’s characteristics for the non-trained cases.

Iterative Learning Method for Drilling Depth Optimization in Peck Deep-Hole Drilling

Due to the enclosed chip evacuation space in deep hole drilling process, chips are accumulated in drill flutes as drilling depth increases, resulting in the increase of drilling torque and lead to drill breakage. Peck drilling is a widely used method to periodically alleviate the drilling torque caused by chip evacuation; the drilling depth in each step directly determines both drill life and machining efficiency. The existing drilling depth optimization methods face problems including low accuracy of the prediction model, the hysteresis of signal diagnosis, and onerous experiments. To overcome these problems, a novel drilling depth optimization method for peck drilling based on the iterative learning optimization is proposed. First, the chip evacuation torque coefficients (CETCs) are introduced into the chip evacuation torque model to simplify the model for learning. Then, the effect of chip removal process in peck drilling on drilling depth is analyzed. The extended depth coefficient by chip removal (EDCbCR) is introduced to develop the relationship between the extended depth in each drilling step and drilling depth. On the foundation of the modeling above, an iterative learning method for drilling depth optimization in peck drilling is developed, in which a modified Newton's method is proposed to maximize machining efficiency and avoid drill breakage. In experiments with different cutting parameters, the effectiveness of the proposed method is validated by comparing the optimized and measured results. The results show that the presented learning method is able to obtain the maximum drilling depth accurately with the error less than 10%.

Effect of Process Parameters on Hole Diameter Accuracy in High Pressure Through Coolant Peck Drilling Using Taguchi Technique

Machining with pressurized coolant is nowadays widely accepted technique in the manufacturing industry, especially in high performance machining conditions. The data on the effects of variation of high coolant pressure in drilling operation is limited. This paper presents the effect of high coolant pressures along with spindle speed, feed rate and peck depth on hole diameter accuracy. Experiments were performed on EN9 steel with TiAIN coated through coolant drill on CNC vertical machining center. Taguchi technique was employed for design of experiments and analysis of results. Results showed that the higher values of optimal coolant pressure and spindle speed were demanded for drilling at bottom of hole as compared to that for drilling at top of hole. The optimal values of feed rate and peck depth were same for both the cases of drilling at top and bottom of hole. Use of high coolant pressure in drilling permits higher peck depth for better hole diameter control which results in reduced cycle time and hence production cost.