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    Tool Wear Modeling for Application to Gear Shaping
    (University of Waterloo, 2025-12-11) Kropp, Alexander
    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.
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    Multiple Model Adaptive Control with Blending in State Space
    (University of Waterloo, 2025-12-11) Lovi, Alex
    Adaptive Control (AC) provides a systematic framework for handling uncertainty in linear and non-linear systems. Single-model adaptive schemes such as Model Reference Adaptive Control (MRAC) and Adaptive Pole Placement Control (APPC) face inherent limitations when applied to systems with large parametric uncertainty, such as slow convergence rates and limited noise robustness. This has motivated researchers to investigate multiple-model strategies that employ several candidate plants to represent different regions of the operating space. In this thesis, we develop Multiple-Model Adaptive Control (MMAC) methodologies based on the blending of signals from multiple fixed models. We consider uncertain plants with known, compact, convex polytopic uncertainty. Our starting point is the design of a Multiple-Model Parameter Identification (MMPI) scheme that quickly and robustly identifies the uncertain plant parameters. In combination with a Model Reference Control (MRC) framework, this leads to a Multiple-Model Reference Adaptive Control (MMRAC) with blending for Linear, Time-Invariant (LTI), non-square (different number of inputs and outputs), multi-input systems, with full-state feedback. Under an exact matching condition, the parameter estimates are used to design a control input such that the plant states asymptotically track the reference signal generated by a state-space reference model. A procedure is provided to select the corner models based solely on the polytopic uncertainty. The proposed MMRAC guarantees the boundedness of all closed-loop signals and the asymptotic convergence of the state-tracking error to zero. Statistical analysis demonstrate improved tracking speed and robustness to noise compared with single-model approaches. The combination of MMPI with pole-placement techniques, allowed us to develop Multiple-Model Adaptive Pole Placement Control (MMAPPC) for LTI, square (same number of inputs and outputs), multivariable systems with full-state feedback, and for with LTI, Single-Input, Single-Output (SISO) systems via an intermediate state estimation step. The resulting controller again ensures the boundedness of all closed-loop signals, while also asymptotically placing the closed-loop eigenvalues at designer-specified locations. Statistical analysis shows a clear increase in robustness to noise relative to single-model schemes. These improvements were validated in the context of motion control of lateral vehicle dynamics, where multiple-model schemes consistently outperformed single-model approaches, including cases with slowly time-varying unknown parameters.
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    Algorithmic Collective Action under Differential Privacy
    (University of Waterloo, 2025-12-11) Solanki, Rushabh
    The rapid integration of AI into everyday life has generated considerable public attention and excitement, pointing to unprecedented gains in efficiency and personalization. However, it concurrently raises concerns about the potential for automating algorithmic harms, as well as re-entrenching existing social inequities. In response, the pursuit of "trustworthy AI" has become a critical goal for researchers, corporations, and governments alike. Achieving this objective is a complex challenge with many possible ways to realize it, ranging from policy and regulation to technical solutions such as algorithmic design, systematic evaluation, and enhanced model transparency. Contemporary AI systems, particularly those leveraging large-scale models, are fundamentally trained on vast amounts of data, often sourced from social interactions and user-generated content. This dependence introduces a grassroots mechanism for autonomy: the possibility for everyday users, citizens, or workers to directly steer AI behavior. This concept, known as Algorithmic Collective Action (ACA), involves a coordinated effort where a group of individuals deliberately modifies the data they share with a platform, with the intention of driving the model’s learning process toward outcomes they regard as more favorable or equitable. We investigate the intersection between these bottom-up, user-driven efforts to influence AI and a growing class of methods that firms already implement to improve model trustworthiness, especially privacy protections. In particular, we focus on the setting in which an AI firm deploys a differentially private model, motivated by the growing regulatory focus on privacy and data protection. Differential Privacy (DP) is a formal, mathematical framework that provides provable guarantees about the privacy of individuals whose data are used in a dataset. To operationalize these privacy guarantees in deep learning settings, we employ Differentially Private Stochastic Gradient Descent (DP-SGD), which is the de facto DP mechanism for deep learning, making it a natural choice for assessing the effectiveness of ACA under realistic conditions. Our findings reveal that while differential privacy offers substantive protection for individual data, it concurrently introduces challenges for effective algorithmic collective action. Theoretically, we characterize formal lower bounds on the success of ACA when a model is trained with differential privacy. These bounds are expressed as a function of key variables: the size of the acting collective and the firm’s chosen privacy parameters, which dictate the level of privacy the firm intends to enforce. Empirically, we verify these theoretical trends through extensive experimentation by simulating collective action during the training of a deep neural network classifier across several datasets. Moreover, we offer additional insight into how ACA affects empirical privacy, and we include a socio-technical discussion of the wider implications for responsible AI.
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    Deterministic and Probabilistic Bijective Combinatorics for Macdonald Polynomials
    (University of Waterloo, 2025-12-11) Dantas e Moura, Guilherme Zeus
    Permuted-basement Macdonald polynomials 𝐸^𝜎_𝛼(𝐱; 𝑞, 𝑡) are nonsymmetric generalizations of symmetric Macdonald polynomials indexed by a composition 𝛼 and a permutation 𝜎. They form a basis for the polynomial ring ℚ(𝑞, 𝑡)[𝐱] for each fixed permutation 𝜎. They can be described combinatorially as generating functions over augmented fillings of composition shape 𝛼 with a basement permutation 𝜎. We construct deterministic bijections and probabilistic bijections on fillings that prove identities relating 𝐸^𝜎_𝛼, 𝐸^{𝜎𝑠ᵢ}_𝛼, 𝐸^𝜎_{𝑠ᵢ𝛼}, and 𝐸^{𝜎𝑠ᵢ}_{𝑠ᵢ𝛼}. These identities correspond to two combinatorial operations on the shape and basement of the fillings: swapping adjacent parts in the shape, which expands 𝐸^𝜎_𝛼 in terms of 𝐸^𝜎_{𝑠ᵢ𝛼} and 𝐸^{𝜎𝑠ᵢ}_{𝑠ᵢ𝛼}; and swapping adjacent entries in the basement, which gives 𝐸^𝜎_𝛼 = 𝐸^{𝜎𝑠ᵢ}_𝛼 when 𝛼ᵢ = 𝛼ᵢ₊₁.
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    Selbstbestimmt: The Woman in Contemporary German Women’s Filmmaking
    (University of Waterloo, 2025-12-11) Provida, Myrto
    Women’s representation in film has become an increasingly relevant topic in gender and film discourse, challenging the postfeminist claim that gender equality has been achieved and feminist critique is no longer necessary. German cinema in particular has recently experienced renewed scrutiny for its evident gender inequality, with scholarship pointing to the underrepresentation of women both on and off the screen, a pattern that reflects enduring structural barriers within the industry. Despite graduating in equal numbers as men from film schools, women direct fewer than 25% of the feature films released in Germany, receive less than 20% of all available funding, and are significantly underrepresented as directors, writers, and producers. At the same time, female figures in German films are “visible but not diverse:” the average German cinematic woman is slim, white, heterosexual, in her teens, twenties, or thirties, and more likely to be shown in the context of relationships or partnerships than in work environments. Yet when women occupy key filmmaking positions, not only do they work with more women behind the screen, but they also construct more complex female characters, foregrounding female subjectivity in diverse contexts. This dissertation attaches itself to questions of women’s representation in German cinema by examining three contemporary films directed by women in order to to explore how they engage with womanhood through their female protagonists. The first, both chronologically and analytically, is Maren Ade’s Toni Erdmann (2016), which centres on the relationship between business consultant Ines and her father, who unexpectedly visits her. An international critical and commercial success, Toni Erdmann has exerted wide influence on the German cinematic landscape, drawing significant attention on its portrayal of gendered subjectivity. The second is Annika Pinske’s Alle reden übers Wetter (2022), which follows doctoral candidate Clara’s return to her hometown to celebrate her mother’s birthday. Despite being the most recent of the three films analyzed, Alle reden übers Wetter is placed after Toni Erdmann due to its contextual and aesthetic parallels to Ade’s film, as Pinske—the only director among the three who hails from the former East, a rarity among German women filmmakers—worked closely with Ade and adopts a similarly realist aesthetic in her debut feature, which presents a range of multidimensional female characters. Finally, the dissertation examines Sherry Hormann’s Nur eine Frau (2019), which dramatizes the life of Hatun “Aynur” Sürücü, a German woman of Kurdish background who was murdered by her brother at a Berlin bus stop in 2005. The film is discussed last not only because it diverges from Ade’s and Pinske’s works through its highly stylized form, but also because it engages with a distinct subject matter—migration and Islam—through a real-life case that remains central to German debates on cultural difference and integration. A close reading of the formal and narrative strategies in these films reveals a multifaceted engagement with gendered subjectivity by contemporary German women directors, whose protagonists are constructed as complex characters navigating intersecting pressures and gendered demands. Central to these trajectories is the pursuit of self-determination, which emerges across all three works as an ongoing process marked by the continuous negotiation of often conflicting roles—as daughters, mothers, partners, and friends. While these films stop short of articulating a collective dimension of self-determination, instead adopting an individualized lens reflecting postfeminist discourse, they nonetheless articulate a shared feminist consciousness by exposing and challenging gendered scripts and essentialist notions of womanhood. By foregrounding self-determination as an analytical lens, this dissertation demonstrates how these films articulate feminist modes of resistance on screen while their production contexts reveal parallel struggles for autonomy off screen. Moreover, by bringing two largely unexamined films into scholarly conversation—Alle reden übers Wetter and Nur eine Frau—the project expands the representational and analytical space of German women’s filmmaking and illuminates the diverse ways women directors intervene in, reshape, and critically reimagine dominant cultural narratives about womanhood. Ultimately, the analysis shows that contemporary German women filmmakers and their protagonists enact a mode of agency and resistance that foregrounds their struggle to live and create selbstbestimmt.