This thesis investigates the current state of tool wear prediction models in machining, focusing on their limitations in accurately incorporating the complex dynamics of cutting fluids and their industrial applicability. It proposes a comprehensive evaluation framework to classify and evaluate a wide range of models, including empirical, physical, computational, and data-driven models. The study identifies the key limitations and strengths of each model category. It proposes enhancements by integrating advanced fluid dynamics and predictive modeling techniques to improve tool wear predictions' accuracy and industrial applicability. A structured literature review was conducted to investigate and evaluate existing tool wear models and their integration with cutting fluid dynamics. This review included defining search criteria, selecting relevant studies, and assessing their quality and relevance. The study uses thematic analysis and model evaluation frameworks to classify and evaluate the models, leading to the identification of critical limitations and strengths. The literature review and model evaluation findings revealed that empirical models, while simple and quick to implement, showed moderate accuracy and limited fluid dynamics integration. Physical models provided high accuracy in specific conditions but were computationally intensive. Computational models, particularly those using techniques like Finite Element Analysis (FEA) and Computational Fluid Dynamics(CFD), offered detailed insights and high accuracy but required significant computational resources. Data-driven models demonstrated exceptional predictive capabilities and comprehensive fluid dynamics integration but relied heavily on data availability and quality.  The proposed enhancements include introducing non-linear elements into empirical models, incorporating simplified fluid models or empirical correlations into physical models, exploring reduced-order models (ROMs) or surrogate models for computational models, and developing robust data preprocessing and augmentation techniques for data-driven models. These enhancements aim to improve the accuracy and applicability of tool wear models in industrial machining processes, ultimately contributing to more efficient and cost-effective machining operations. The study emphasizes the importance of a systematic and holistic approach to model evaluation and enhancement. Future research should focus on validating these proposed enhancements through empirical studies and real-world applications, ensuring their relevance and robustness in diverse industrial settings. This research offers significant potential to advance tool wear modeling, providing valuable insights for both academia and industry.

The process of taking a solid model and producing a machined part requires the time and skillset of a range of professionals, and several hours of part review, process planning, and production. Much of this time is spent creating a methodical step-by-step process plan for creating the part from stock. The work presented here is part of a software package that performs automated process planning for a solid model. This software is capable of not only greatly decreasing the planning time for part production, but also give valuable feedback about the part to the designer, as a time and cost associated with manufacturing the part. In order to generate these parameters, we must simulate all aspects of creating the part. Presented here are models that replicate these aspects. For milling, an automatic tool selection method is presented. Given this tooling, another model uses specific information about the part to generate a tool path length. A machining simulation model calculates relevant parameters, and estimates a time for machining given the tool and tool path determined previously. This time value, along with the machining parameters, is used to estimate the wear to the tooling used in the process. Using the machining time and the tool wear a cost for the process can be determined. Other models capture the time of non-machining production times, and all times are combined with billing rates of machines and operators to present an overall cost for machining a feature on a part. If several such features are required to create the part, these models are applied to each feature, until a complete process plan has been created. Further post processing of the process plan is required. Using a list of available machines, this work considers creating the part on all machines, or any combination of these machines. Candidates for creating the part on specific machines are generated and filtered based on time and cost to keep only the best candidates. These candidates can be returned to the user, who can evaluate, and choose, one candidate. Results are presented for several example parts. / text