Research Projects

Sponsored by National Science Foundation (NSF-CMMI), TTRF Research Foundation, Industry and Companies,
Rutgers Industrial & Systems Engineering, National Institute of Standards and Technology (NIST).

Predictive Modeling for Laser Based Powder Metal Additive Manufacturing of Nickel-Based Alloyed Parts
Sponsor: NIST-National Institute of Standards and Technology
The objective of this research project is to develop a predictive modeling system to effectively predict and control process performance for successful powder metal fusion so that necessary process planning and adjustment can be conducted before the actual layers are fabricated. The project will enable the current direct laser powder metal fusion systems to fabricate nickel-based (Inconel) alloyed parts with consistency and robustness in dimensional accuracy, part quality and structural integrity. The scope of this proposed project is to research and develop a cost-effective expert system for laser based powder metal fusion additive manufacturing processes, in which metal powder is fused layer-by-layer using selective laser melting to directly fabricate metal parts or components to a predetermined geometry and shape.

Predictive Modeling and Optimization of Machining Induced Surface Integrity with Applications in Titanium and Nickel-Based Alloyed End Products
Sponsor: NSF-National Science Foundation (Link)
Partners: Scientific Forming Technologies Corporation
The research objective of this award is to establish an integrated physics-based, predictive modeling approach to improve surface integrity and optimize machining operations in the manufacturing of titanium and nickel-based alloyed end products. The goal is to control surface integrity, machining-induced layer thickness, depth-of-work hardening, tensile layer thickness, residual stresses, and micro-hardness of the end product, as well as representing the cutting tool parameter (material, coating and edge geometry) effects, and the effects of cutting conditions on these results. The proposed research will be conducted in a three-pronged approach, including physics-based modeling, experimental analyses and validation, and probabilistic-predictive modeling on (1) determination of detailed friction between tool and workpiece, and tool wear, (2) physics-based finite element simulations using temperature-dependent flow softening based constitutive material models to compute process outputs, including surface properties, and validating them with experiments, and (3) probabilistic predictive modeling and multi-criteria optimization.

EAGER: Pulsed Laser Assisted Exfoliation of Single Crystalline SiC Thin Layers for Cost Effective Micro-Device Fabrication
Sponsor: NSF-National Science Foundation (Link)
This EArly-concept Grant for Exploratory Research (EAGER) grant provides funding for testing the feasibility of pulsed laser processing that would enable high rate and cost effective exfoliation of thin single crystalline silicon carbide (SiC) layers suitable for fabrication of micro-devices. The attractiveness of single crystalline SiC in a variety of device applications is counteracted by the very high cost of substrates. The main goal of this project is to exfoliate multiple thin layers from one standard thickness SiC wafer using hydrogen ion implantation and laser processing, and transferring such layers to silicon or polycrystalline SiC substrates in order to enable a broader use of this material. Hydrogen ion implantation into SiC can form a zone of voids and microcracks at a depth approximating the implantation range, and lead to exfoliation at subsequent very high temperatures. The proposed approach of laser-assisted exfoliation would utilize a lower implantation dose and lower annealing temperatures, thus reducing damage and allowing bonding of SiC to temperature-sensitive substrates. Interactions between ion implantation conditions, laser irradiation, and heating will be explored to gain preliminary understanding of the path leading to exfoliation of continuous single crystalline layers suitable for electronic devices. Feasibility studies of the proposed process will be conducted and structural and electrical properties of processed samples will be investigated.

Biomedical Devices Design, Prototyping and Manufacturing (IREBID)
Sponsor: European Commission Research Directorate-General, FP7
Partners: J. Ciurana- Univ. Girona, Spain (PI), E. Ceretti- Univ. Bresca, Italy, P. Bartolo- Inst. Polytech. Leiria, Portugal, C. Rodriguez- Tech Monterrey, Mexico, J.V. Lopes De Silva CTIR, Brazil
During the last five to ten years, the healthcare sector is increasing its research activity, not only because final terminal cases are looking for solutions, but also diagnosis, treatment and life-long care are becoming ways to reduce costs of health care. Also, doctors and patients are constantly looking for better technologies in products such as orthopedic prosthesis, diagnostics, surgery instrumentation, drug dosing and delivery systems. In the meantime other maturing manufacturing systems with high level of knowledge (e.g. automotive manufacturing field) have been decreasing its research activity and well-developed technological advancements and solutions are not exploited as much as they can be in other fields. The main objective is to create and reinforce synergies between applied investigation fields of engineering and medicine in order to develop new solutions for the healthcare sector. The project will help in creating a high level expertise and understanding on Design, Prototyping and Manufacture for Medical Devices using computing tools, biomaterials and other biocompatible engineering materials. The expected result of the project is to develop solutions for the sector of biomedicine (new medical devices) and bioengineering such as, development of medical devices which can slowly dose the medicine, improve existing developments, develop artificial joint replacements and orthopedic prosthesis, new optimized geometries and coatings for some invasive medical tools such as needles, scalpels and surgical tools and fabrication of functional graded scaffolds for tissue engineering. The second annual meetings for the European Commission IREBID project titled "Biomedical Devices Design, Prototyping and Manufacturing" was held in Rutgers Busch campus CoRE Building on July 11-29, 2011.

Mechanical Micro-machining: Modeling and Simulation of Micro-Milling Process for Fabrication of Mold Cavities, Micro-fluidic and Medical Devices
Sponsor: Industry
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.

Improving Machinability of Titanium Alloys using Physics-Based Simulation Modeling
Sponsor: NSF-National Science Foundation (Link)
Partners: TechSolve, Conicity Technologies, GE Aviation, Scientific Forming Technologies Corporation
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. Finite Element Simulation software DEFORM will be utilized in this research. 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. 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.

Investigations on Influence of Machining-Induced Strain, Stress and Temperature Fields on White-Etch Layer Formation in IN-100 Super Alloy
Sponsor: Industry
Nickel-base super alloys are typically available wrought, forged, cast and in sintered (powder metallurgy) forms and often used in mission critical components such as in aircraft/industrial gas turbine engines. Machinability of the nickel-alloys is generally rated as extremely difficult. Main difficulty in machining of nickel-alloy arises due to the high toughness and work hardening behavior of these alloys. In this project, Finite Element (FE) simulation of machining of powder-based IN100 super alloy in the presence of tool flank wear is studied by using Arbitrary Lagrangian Eulerian explicit and updated Lagrangian implicit methods. Both simulation methods are capable of simulating plastic flow around a round cutting tool edge without using chip separation criteria. Johnson-Cook work material model is used for elastic-plastic work deformations. The simulation results include predictions of chip formation, plastic strain, temperatures and tool stress distributions in cases of cutting with fresh and worn cutting tool. These results are highly essential in predicting machining induced surface alterations such as white etched layer formation that are detrimental to fatigue life of super alloy components.

Predictive Modeling and Optimization of Machining Parameters in Hard Turning
Sponsor: Industry
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.
We also 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.
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.

Development of Workpiece Material and Friction Models for Simulation of Machining of Aerospace Alloys
Sponsor: NASA-New Jersey Space Grant Consortium
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.