Tool Wear Modeling for Application to Gear Shaping

Abstract

Tool wear has a major impact on the productivity, economics, and sustainability of metal machining operations. While the topic of tool wear has been studied extensively for conventional metal cutting processes, like milling, turning, and drilling, there is a scarcity of studies in literature on modeling and predicting tool wear in gear machining operations. Gears, on the other hand, are essential components for a vast array of engineered systems, like automotive, aerospace, and various transportation vehicles, robotics, automation, and general machinery. This thesis has targeted developing a framework for the study and prediction of tool wear in the gear shaping operation. Gear shaping is among the most versatile methods of cutting gears. In-house experiments were designed and performed to replicate the gear shaping process on a 5-axis milling machine. The kinematics were modeled, and custom NC code was generated and validated using polygon subtractions, to ensure that a gear shaping cutter would accurately produce a gear workpiece. The testing conditions were designed considering the physical capabilities of the machine tool, and the utilization of digital simulations of the gear cutting operation via ShapePro software (previously developed at the University of Waterloo), that predicts the kinematics, chip geometry, and cutting forces. As such, specimen gears were produced from AISI 1215 mild steel using HSS (high-speed steel) cutter material. The cutting edges and flanks of the tool were imaged throughout the tests, to monitor the development of wear. In the envelope of cutting speeds and feed rates that could be tested, only minimal wear was observed, and this was concentrated at the cutting tooth tips and corners, consistent with the prediction from ShapePro that these regions are subject to the largest chip thickness and longest distance of workpiece material cut. Nevertheless, these tests have demonstrated the proof-of-concept for performing shaping tests and progressively imaging the tool edges for wear. Future tests should focus on performing the cuts at more aggressive speeds and feeds to induce discernable wear. To facilitate rapid characterization of tool wear for different workpiece and tool material pairs, an analogy testing method was also developed as an interrupted cutting operation on a lathe, designed to mimic cutting conditions similar to those in shaping. With this approach, a broader set of cutting speeds and feed rates could be explored in the machining of a similar kind of material (cold rolled 1020 steel). In this case, using HSS tooling brought practical limitations (e.g., built-up edge), thus the tooling material was changed to carbide. Nevertheless, a reasonable variation of cutting conditions could be implemented and tool wear progression data, as a function of cutting distance (and time) be documented. This data has informed the development of a progression-based tool wear model for predicting flank wear in interrupted cutting, which is presented as a new contribution in this thesis. Established tool life models, such as Taylor and Colding, can predict the cutting time required for reaching only a single value of tool flank wear, whereas the proposed model can be used to predict when different values of tool wear would be reached without requiring re-calibration. More importantly, the proposed model can be integrated inside a time-domain simulation of an interrupted cutting operation, like gear shaping, in order to predict and update the wear state along each node on the tool edge, which classical tool life models cannot achieve, as they are ‘tuned’ to predict the cutting time until a preset wear is reached. The proposed model, as well as Taylor and Colding models, were benchmarked with respect to the experimental wear data collected across 12 different cutting conditions, and they all performed comparably in predicting tool life, with average prediction errors of roughly 21%-24%, thus indicating some confidence in the proposed new model. Future research should focus on collecting further data, both in gear shaping and analogy orthogonal testing experiments, to ensure further repeatability of the data, as well as validating that a tool wear model calibrated using the lower cost and faster analogy experiments can indeed predict the distribution of tool wear progression in the much more complex gear shaping operation. Furthermore, it is also advised to expand the wear model to predict uncertainty bounds along with the tool wear values themselves, and to expand the study into the machining of different kinds of metals, with different tools substrates and coatings.