Summary: The objectives of this collaborative research award are to understand the physical and thermal aspects of the material removal process in machining titanium alloys and to improve machinability using novel cutting tools with variable micro-geometry design and nano-layered self lubricating coatings. The research approach would be to establish a methodology for physics-based prediction of cutting forces, temperatures, stresses and cyclical serrated chip formation in machining titanium alloys. Friction and heat flow at tool-chip-workpiece interfaces for various tool edge micro-geometry and nano-layered coatings will be identified by conducting cutting tests. Wear rate models that relate predicted process variables and contact conditions to tool wear under realistic machining conditions will be developed. These models will be utilized in determining optimum form and thickness of coating layers that will be applied on the variable micro-geometry cutting tools. These advanced coatings will be deposited using the electron beam physical vapor deposition process. Experimental testing will be performed in industrial scale test-beds to validate improved machinability and tool life at high speed machining regimes. If successful, the benefits and broader impacts of this research will be the use of novel cutting tools with variable micro-geometry and nano-layered self lubricating coatings in high speed machining of several exotic and difficult-to-machine alloys. It is expected that this advanced cutting tool design and simulation capability would reduce the cost in product design and development and improve productivity in aerospace, automotive, military, chemical and medical device industry where titanium alloys are utilized.

Chip images collected at 

rake angle of 3 degrees 

and f=0.025mm/rev

Summary: In this project, a model-based micro-end milling process planning guideline for machining micro mold cavities is proposed. The goal is to facilitate proper selections of the process parameters. Specifically, the axial depth of cut, the feed per tooth are critical in achieving performance objectives in terms of cutting forces, surface accuracy, tool life, etc. Analytical model to provide an understanding of the micro-machining with minimum chip thickness and edge radius effects, mechanistic time-domain simulation modeling to provide predictive capability in practical machining performance, such as cutting forces, tool vibrations, surface accuracy, and surface roughness are utilized. The generalized process planning strategy consists of two steps: roughing and finishing. In roughing, the objective is to control the cutting force within a predefined threshold to prevent premature tool breakage and to maximize the material removal rate. In finishing, the primary objective is to control the form error within the tolerance and to obtain satisfactory surface roughness. The proposed process planning strategy was applied for micro-milling of array of micro-channels for micro-embossing and mold cavities for micro-molding.

Project:     Investigations on the Effects of Advanced Edge Design in PCBN cutting tools for Hard Turning
Team:       Yigit Karpat and Dr. Tugrul Ozel
Sponsor:   Conicity Technologies - Weiler Corporation (2006-2007)

Summary:  In this project, we study micro-geometry edge design effects on the Polycrystalline Cubic Boron Nitrite (PCBN) cutting tool performance in hard turning. We conducted orthogonal cutting tests using chamfered, honed, and waterfall honed various PCBN inserts in machining of AISI 4340 alloy steel. We develop slip-line field analysis based analytical models to predict friction factor at tool-chip interface using measured forces and chip dimensions. We utilized Finite Element simulations to predict field variables such as forces, temperatures, and stresses in the chip, the tool and the workpiece. We also develop tool wear rate models to predict the influence of micro-geometry  on tool wear behavior of the PCBN inserts.

Project:    Precision Micromachining using Fast Pulsed Lasers
Team:      Wen-Hui Lee and Dr. Tugrul Ozel 
Collaborators:  Dr. Zhixiong Guo, Mechanical and Aerospace Engineering, Rutgers
Sponsor:   Rutgers University Research Council Grants (2005-2006)

Summary: Laser micromachining has the capability to fabricate very small and basic 2.5 D geometric features on a range of materials in the form of laser ablation or irradiation. Short pulsed lasers that can achieve wide range of wavelengths in the form of harmonics of infrared laser beam at 1064 nm wavelength have been a very effective micro-machining tool used for hole drilling, cutting, scribing, trimming and marking. Direct laser ablation can be performed by controlling laser beam properties such as laser energy, intensity, pulse duration and wavelength in micro-machining 3-D geometric features. 

Project:     Predictive Modeling  and Optimization of Machining Parameters in Hard Turning
Team:       Yigit Karpat and Dr. Tugrul Ozel
Sponsor:   Rutgers University, Department of Industrial and Systems Engineering (2005-2007)
Supporters: Conicity Technologies, Weiler Corporation

Summary:  In this study, we study the hard turning process that is a beneficial practice to increase quality and reduce cost and lead-time for machining steel components. We develop workpiece material model based analytical models to predict field variables such as forces, temperatures, and stresses in the chip, the tool and the workpiece. We also develop tool wear rate models based on predicted field variables. The quality of the finish-machined surfaces is highly dependent upon the tool wear. We aim to validate the tool wear models with the experiments and conduct multi-objective optimization studies using particle swarm intelligence to attain most desirable surface finishes with prolonged tool life and improved productivity in the hard turning processes.

Project:      Monitoring and Modeling of Tool Wear and Surface Finish in Hard Part Machining Using Neural Networks
Team:        Yigit Karpat and Dr. Tugrul Ozel
Sponsor:    Rutgers University, Shape-Master Tools, Timken Company (2003-2005)

Summary: In this project, we utilize neural network modeling to monitor, model and predict surface roughness and tool flank wear over the machining time for variety of cutting conditions in hard turning. Regressions models are also developed in order to capture process specific parameters. The data sets from measured surface roughness and tool flank wear were employed to train the neural network models. Trained neural network models are used in predicting surface roughness and tool flank wear for other cutting conditions. A comparison of neural network models with regression models is also carried out. Predictive neural network models are found to be capable of better predictions for surface roughness and tool flank wear within the range that they had been trained. We use evolutionary computational algorithms such as particle swarm intelligence for optimization of machining parameters by utilizing predictive neural network models developed. Currently, predictive neural network modeling is being extended to dynamically monitor and predict tool wear modes and patterns seen in finish hard turning processes.

Project: Computational Modeling of Machining Induced Stresses and Surface Properties in High Speed Machining
Team:        Erol Zeren and Dr. Tugrul Ozel 
Sponsor:   Rutgers University Research Council Grants
Supporters:  McWilliams Forge Co. (2004-2006)

Summary: The main objective of this research is to establish a computational modeling framework for high speed machining in order to be able to investigate effects of field variables on the integrity and quality of the machined workpiece. We aim to investigate the formation of residual stresses, micro-hardness and microstructure in the high-speed machined workpiece surfaces. We use commercially available Finite Element Analysis software (ABAQUS/ Explicit) to simulate machining process and apply Arbitrary Eulerian Lagrangian with dynamic explicit solutions in conjunction with adaptive meshing to simulate continuous, serrated or shear localized chip formation. The cutting tool models always have round and/or chamfered edges to represent realistic conditions. The ultimate goal is to accurately compute temperature, strain, strain rate and residual stress fields in high speed machining using computational models and to predict the resultant surface properties. We apply this modeling framework to high speed machining of alloy steels, aluminum and titanium alloys.

Project: Development of Workpiece Material and Friction Models for Simulation of Machining of Aerospace Alloys
Team:        Erol Zeren and Dr. Tugrul Ozel
Sponsor:    New Jersey Space Grant Consortium, Industry-University Research Program
Supporters: Third Wave Systems Inc. (2002-2003)

Summary: This study is an initiative to characterize workpiece material and friction properties in dry machining of aerospace alloys. Machining of difficult-to-cut aerospace alloys and materials is a challenging process and desired part quality (surface finish and tolerance) requirements are tough to achieve with conventional machinery due to process stability problems associated with rigidity, vibrations and wear behavior of cutting tools. Finite Element simulations of machining processes have the potential to reduce experimentation, optimize cutting parameters, and improve overall machinability and part quality for aerospace machining applications. One of the key elements in success of such simulation models is reliable workpiece material and friction data. This research study aims to develop novel, robust and reliable workpiece material and friction models and data for enhancing performance and reliability of Finite Element simulations.