Computer numerical control (CNC) machining is an integral element to the
manufacturing industry for production of components with requirements to meet several
outcome conditions. The surface roughness (Ra) of a workpiece is one of the most
important outcomes in finish machining processes due to it’s direct impact on the
functionality and lifespan of components in their intended applications. Several factors
contribute to the creation of Ra in machining including, but not limited to, the machining
parameters, properties of the workpiece, tool geometry and wear. Alternative to traditional
selection of machining parameters using existing standards and/or expert knowledge,
current studies in literature have examined methods to consider these factors for prediction
and optimization of machining parameters to minimize Ra. These methods span many
approaches including theoretical modelling and simulation, design of experiments,
statistical and machine learning methods. Despite the abundance of research in this area,
challenges remain regarding the generalizability of models for multiple machining
conditions, and lengthy training requirements of methods based solely on machine learning
methods. Furthermore, many machine learning methods focus on static cutting parameters
rather than consideration of properties of the tool and workpiece, and dynamic factors such
as tool wear.
The main contribution of this research was to develop a prediction and optimization
model framework to minimize Ra for finish turning that combines theoretical and machine
learning methods, and can be practically utilized by CNC machine operators for parameter
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decision making. The presented research work was divided into four distinct objectives.
The first objective of this research focused on analyzing the relationship between the
machining parameters and Ra for three different materials with varying properties (AISI
4340, AISI 316, and CGI 450). This was followed by the second objective that targeted the
development of an Ra prediction framework that utilized a kinematics-based prediction
model with an ensemble gradient boosted regression tree (GBRT) to create a multi-material
model with justified results, while strengthening accuracy with the machine learning
component. The results demonstrated the multi-material model was able to provide
predictions with a root-mean-square error (RMSE) of 0.166 μm and attained 70% of testing
predictions to fall within limits set by the ASME B46.1-2019 standard. This standard was
utilized as an efficient evaluation tool for determining if the prediction accuracy was within
an acceptable range.
The remaining objectives of this research focused on investigating the relationship
between tool wear and Ra through a focused study on AISI 316, followed by application
of the prediction model framework as the fitness function for testing of three different
metaheuristic optimization algorithms to minimize Ra. The results revealed a significant
relationship between tool wear and Ra, which enabled improvement in the prediction
framework through the use of the tool’s total cutting distance for an indicator of tool wear
as an input into the prediction model. Significant prediction improvement was achieved,
demonstrated by metrics including RMSE of 0.108 μm and 87% of predictions were within
the ASME B46.1-2019 limits. The improved prediction model was used as the fitness
function for comparison performance of genetic algorithm (GA), particle swarm
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optimization (PSO), and simulated annealing (SA), under constrained and unconstrained
conditions. SA demonstrated superior performance with less than 5% error between the
optimal and experimental Ra when constrained to the experimental data set during
validation testing. The overall results of this research establish the feasibility of a
framework that could be applied in an industrial setting for both prediction of Ra for
multiple materials, and supports the determination of parameters for minimizing Ra
considering the dynamic nature of tool wear. / Thesis / Master of Applied Science (MASc) / The surface quality produced on a workpiece via computer numerical control
(CNC) machining is influenced by many factors, including the machining parameters,
characteristics of the workpiece, and the cutting tool’s geometry and wear. When the
optimal machining parameters are not used, manufacturing companies may incur
unexpected costs associated with scrapped components, as well as time and materials
required for re-machining the component. This research focuses on developing a model to
indirectly predict surface roughness (Ra) in CNC turning, and to provide operators
guidance regarding the optimal machining parameters to ensure the machined surface is
within specifications. A multi-material Ra prediction model was produced to allow for use
under multiple machining conditions. This was enhanced by comparing three different
optimization algorithms to evaluate their suitability with the prediction framework for
providing recommendation on the optimal machining parameters, considering an indicator
for tool wear as an input factor.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/30184 |
| Date | January 2024 |
| Creators | Bennett, Kristin S. |
| Contributors | Veldhuis, Stephen C., Mechanical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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