This master’s thesis focuses on analysis of time consumption of machining process, the basic units of operating costs of production,optimization and subsequen tapplication to specific part. It is a turning shaft in two operations, named roughing and finishing turning. The aim is to find the optimum combination of life time of cutting tool and cutting speed, which is the main criterion for optimization in terms of minimum production cost or a maximum of productivity.

This work deals with operating production costs in machining process and further optimalization. In the first chapter, individual costs items which are related to machining process are specified. These cost items are quantified and total production costs are acquired. Optimalization of operation production costs are described for criterion of minimal production costs and criterion of maximal productivity are described in next chapter. Practical part of this work is focused to apply each individual criterion in practical example. There is CD with programs attached to this work. The goal is to find optimal combination of cutting speed, sufficient life time of cutting tool and suitable cutting technology so that chosen criterion can be acceptable.

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 v 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 vi 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.

The precision of machining systems is ever increasing in order to keep up with components’ accuracy requirements. At the same time product variants areincreasing and order quantities are decreasing, which introduces high demands on the capability of machining systems. The machining system is an interaction between the machine tool structure, the process and the control system and is defined in terms of capability by the positional, static, dynamic and thermal accuracy. So far, the control of the machining system, in terms of static and dynamic stability is process based which is often translated into sub-optimum process parameters and therefore low productivity.This thesis proposes a new approach for control of the machining systemwhich is based on the capability to control the structural properties of themachine tool and as a result, controlling the outcome of the machining process.The control of the structural properties is realized by carefully designed Joint Interface Modules (JIMS). These modules allow for control of the stiffness and damping of the structure, as a result of tuning the contact conditions on the interface of the JIM; this is performed by control of the pre-load on the interface,by treatment of the interface with damping enhancing materials, or both. The thesis consists of a presentation of the motivation behind this work, the theoretical basis on which the proposed concept is based and a part describing the experimental investigations carried out. Two prototype JIMs, one for a milling process and one for a turning process were used in the experimental investigations that constitute the case studies for examining the validity of the proposed concept and demonstrating its applicability in a real production environment. / <p>QC 20140611</p> / EU FP7 POPJIM

<p>Carbon fiber bending during Carbon Fiber Reinforced Plastic (CFRP) milling is an important factor on the quality of the machined surface. When the milling tool rotates, the fiber first contacts the rake face instead of the tool edge at a certain cutting angle, then the fiber is bent instead of being cut by the tool. It causes the matrix and the fiber to fall out, and the fiber is broken from deep inside the machined surface. The broken fibers are pulled out as the tool rotates, which is known as pull-out fibers. The machining defect is the main cause of deteriorating the quality of the machined surface. To reduce such machining defects, it is important to predict the carbon fiber bending during CFRP milling. However, it is difficult to determine a point where fiber bending occurs because the fiber cutting angle changes every moment as the tool rotates. Therefore, in this study, CFRP milling simulation was performed to numerically analyze the machining parameters such as fiber cutting angle, fiber length, and the magnitude of fiber bending according to the different milling conditions. In addition, the deformation of the matrix existing between carbon fibers is predicted based on the fiber bending information obtained through simulation, and matrix shear strain energy model is developed. Also, the relationship between the matrix shear strain energy and machining quality is analyzed. Through verification experiments under various machining conditions, it is confirmed that the quality of the machined surface deteriorated as the matrix shear strain energy increased. Moreover, this study analyzed the fiber cutting mechanism considering bent fibers during CFRP milling and proposed a method to identify the type of machining mechanism through machining sound analysis. Through experiments, it was verified that fiber bending or defects can be identified through machining sound analysis in the high-frequency range between 7,500 Hz and 14,800 Hz. From the analysis, the effect of different chip thickness in up-milling and down-milling on fiber bending was investigated by analyzing simulation and sound signal. From machining experiments, the effect of this difference on cutting force and machining quality was verified. Lastly, we developed a minimum chip thickness and fiber fracture model in CFRP milling and analyzed the effect of fractured fibers on the machining sound. Carbon fibers located below the minimum chip thickness do not contact the tool edge and are compressed by the bottom face of the tool, and these fibers are excessively bent and broken. As these broken fibers are discharged while scratching the flank face of the tool, a loud machining sound is generated. Moreover, through the verification experiment, it was confirmed that the number of broken fibers is proportional to the loudness of the sound, and calculated number of broken fibers for one second using the fiber fracture model coincides with the high-frequency machining sound range of 7,500 Hz to 14,800 Hz.</p>