To remain competitive in today’s demanding economy, there is an increasing demand for improved productivity and scrap reduction in manufacturing. Traditional manufacturing metal removal processes such as turning and boring are still one of the most used techniques for fabricating metal products. Although the essential metal removal process is the same, new advances in technology have led to improvements in the monitoring of the process allowing for reduction of power consumption, tool wear, and total cost of production. Replacing used CNC lathes from the 1980’s in a manufacturing facility may prove costly, thus finding a method to modernize the lathes is vital. This research focuses on Phase I and II of a three phase research project where the final goal is to optimize the simultaneous turning and boring operation of a CNC Lathe. From the optimization results it will be possible to build an adaptive controller that will produce parts rapidly while minimizing tool wear and machinist interaction with the lathe. Phase I of the project was geared towards selecting the sensors that were to be used to monitor the operation and designing a program with an architecture that would allow for simultaneous data collection from the selected sensors at high sampling rates. Signals monitored during the operation included force, temperature, vibration, sound, acoustic emissions, power, and metalworking fluid flow rates. Phase II of this research is focused on using the Response Surface Method to build empirical models for various responses and to optimize the simultaneous cutting process. The simultaneous turning and boring process was defined by the four factors of spindle speed, feed rate, outer diameter depth of cut, and inner diameter depth of cut. A total of four sets of experiments were performed. The first set of experiments screened the experimental region to iii determine if the cutting parameters were feasible. The next three set s of designs of experiments used Central Composite Designs to build empirical models of each desired response in terms of the four factors and to optimize the process. Each design of experiments was compared with one another to validate that the results achieved were accurate within the experimental region. By using the Response Surface Method optimal machining parameter settings were achieved. The algorithm used to search for optimal process parameter settings was the desirability function. By applying the results from this research to the manufacturing facility, they will achieve reduction in power consumption, reduction in production time, and decrease in the total cost of each part.

Machining operations in automated manufacturing centers are under-performing by 20-80%. Optimizing these machining operations requires on-line knowledge about the cutting tool's condition and the process state. Currently, this information is either not reliable or not available in a timely manner. This is due to the lack of suitable sensors, which must measure on-line directly and accurately one or more of the relevant tool and process information sources in the hostile machining environment. A direct, active, ultrasonic method for on-line sensing of the tool condition and process state in turning operations was developed. Sensing is achieved by using an ultrasonic transducer operating at 10 MHz in a pulse-echo mode to send pulses through the tool. The amplitude and propagation time of the reflected pulses are modulated by the tool nose, flank, temperature, and by the material in contact with the tool. The reflected pulses are received and processed by a high speed digital signal processing system. This method has the potential to directly and accurately measure on-line several relevant processes and cutting tool parameters through the use of a single sensor. These parameters are tool-workpiece contact, tool wear, tool chipping, temperature and chatter. / Ph. D.

The FP interferometer was found to be feasible for detecting changes in machining forces. The fiber optic sensor was able to detect increases in strain corresponding to force increases detected by a dynamometer. The FP interferometer system must progress in several ways. A better data acquisition and data analysis system must be developed. A robust sensor must be made to withstand the harsh environment of machining. Also a method for eliminating the affects of thermal strain must be created. Finally, the placement of the FP sensor must also be determined. The FP has the potential to effectively monitor machining forces without affecting the rigidity of a turning operation setup. / Master of Science

The research objectives of this thesis are as follows: (1) Investigate the effects of carbon content, alloying, and heat treatment of steels on white layer formation, (2) Prove/disprove that the temperature for phase transformation in machining is the same as the nominal phase transformation temperature of the steel, (3) Quantify the contributions of thermal and mechanical effects to white layer generation in machining, (4) Develop a semi-empirical procedure for prediction of white layer formation that accounts for both thermal and mechanical effects. These research objectives are realized through experimental and modeling efforts on steels. Depth and hardness measurements of the white layers formed in steels show the importance of heat treatment and carbon content on white layer formation. Measurements of workpiece surface temperature and X-Ray Diffraction characterization of the machined surfaces show that phase transformation occurs below the nominal As temperature suggesting that mechanical effects play an important role in white layer formation. The maximum workpiece surface temperature, the effective stress, and plastic strain on the workpiece surface are measured and/or calculated and shown to affect the white layer depth and amount of retained austenite. A semi-empirical procedure is developed by correlating the maximum workpiece temperature and the unit thrust force increase with white layer formation.

Turning is one of the most widely used processes in machining and is characterized by a cutting tool moving along the axis of a rotating workpiece as it removes material. A detrimental phenomenon to productivity in turning operations is unstable cutting or chatter. This can reduce the life of tooling, dimensional accuracy, and the quality of a part's surface finish because of severe levels of vibration. Ideally, cutting conditions are chosen such that material removal is performed in a stable manner. However, it is sometimes unavoidable because of the geometry of the cutting tool or workpiece. This work seeks to develop a dynamic cutting force model that can be used to predict both the point of chatter instability as well as its amplitude growth over time. Previous chatter models fail to capture the physics of the process from a first-principles point of view because they are oversimplified and rely on various "cutting force coefficients" that must be tuned in order to get a desired correlation with experimental results. The proposed approach models the process in a geometrically rigorous fashion, also giving treatment to the strain, strain rate, and temperature effects encountered in machining. It derives the forces encountered during a turning operation from two sources: forces due to chip formation and forces due to plowing and flank interference. This study consists of a detailed derivation of two new cutting force models. One relies on careful approximations in order to obtain a closed-form solution; the other is more explicit and obtains a solution through numerical methods. The models are validated experimentally by comparing their prediction of the point of instability, the magnitude of vibration in the time and frequency domains, as well as the machined surface topography during chatter.

Thèse (D.Ing.) -- Université du Québec à Chicoutimi, 2005. / Bibliogr.: f. 240-252. Document électronique également accessible en format PDF. CaQCU

Indiana University-Purdue University Indianapolis (IUPUI) / In this thesis, Artificial Neural Network (ANN) technique is used to model and simulate the Turning Process. Significant machining parameters (i.e. spindle speed, feed rate, and, depths of cut) and process parameters (surface roughness and cutting forces) are considered. It is shown that Multi-Layer Back Propagation Neural Network is capable to perform this particular task. Design of Experiments approach is used for efficient selection of values of parameters used during experiments to reduce cost and time for experiments. The Particle Swarm Optimization methodology is used for constrained optimization of machining parameters to minimize surface roughness as well as cutting forces. ANN and Particle Swarm Optimization, two computational intelligence techniques when combined together, provide efficient computational strategy for finding optimum solutions. The proposed method is capable of handling multiple parameter optimization problems for processes that have non-linear relationship between input and output parameters e.g. milling, drilling etc. In addition, this methodology provides reliable, fast and efficient tool that can provide suitable solution to many problems faced by manufacturing industry today.