They are the best things, especially the ones that haven't been discovered yet.


About Me

Let my Ph.D. be representative of my work ethic, not my ability to pontificate. 

Many people see Ph.D. and assume all I do is read and write academic papers. 

They are wrong.

Stemming from a long line of immigrant mill workers manufacturing is literally in my blood. In 2013, I began my Ph.D. studies in Automotive Engineering at Clemson University with my focus on innovating in the manufacturing sector. My dissertation, "Alternative milling path planning strategies and force modeling for nickel-based superalloys", changed the way the scientific community looked at trochoidal milling. 

I created a new approach to modeling the geometry of the tool path, surpassing conventional wisdom. From there, I developed a new way to model cutting force coefficients, allowing us to better predict cutting forces. And to cap it off, I related the cutting forces to the way the workpiece material responds to them. 

I should mention, I programmed and ran my own tests on 3 and 5-axis Okuma CNC machines, prepared the workpieces on EDM machines, created a few sensors, then gathered and processed all of the data. My hands were filthy.  

I completed my Ph.D. in December of 2018 to not only innovate but to also show that the word "impossible" has zero meaning. From academic probation and one undergraduate professor telling me that I would "never be an engineer" to having a doctoral degree in it, there is no such thing as impossible. 

Completed Projects

Industrial Cost Savings Through Academic Research

BMW Predictive Maintenance Sensor

BMW had a problem with a complex piece of manufacturing equipment in the paint shop. It kept failing and they spent a bunch of time diagnosing it. So they came to CU-ICAR and wanted to see if we could build an inexpensive sensor kit to monitor their equipment.

After a full FMEA and multiple interviews with their maintenance team and engineers, a system was developed. 

Using a basic accelerometer and a real-time clock, a Teensy gathered the data, buffered it, then sent it to a Raspberry Pi. 

Working with an alumnus in a startup, we were able to understand the health of individual components in the system and predict when they would fail. 

Within weeks this system moved from a test cell, onto the production line, and has now turned into a Ph.D. dissertation for a new student. 

Slot Milling Applications for GE Power

By applying academic principles to an industrial project, I reduced the machining time of a small slot by 89%, from 45min to 6min, resulting in $75 savings per slot. The slot? It was 0.5" wide, 0.5" deep, and 3.5" long.

Dirty hands and a mind of theory creates value. 

Replace Grinding with Milling

This project found tooling and machining parameters to eliminate the need for costly grinding operations. Through the use of solid ceramic tools for the roughing operation and solids carbides for finishing, GE Power no longer required the installation of a new grinding cell, instead, utilizing existing milling machines.

Image by Joanna Kosinska

Selected Research Publications

Analyzing the Boundaries of Manufacturing Science


 [NAMRC, 2018]

Cutting force modeling is of great importance to understanding the mechanistic behavior of the milling process. Through the understanding of cutting forces the potential for process improvement by way of tool life, surface finish, dimensional accuracy and machining time is opened. The basis for establishing cutting force models lies in the method of gathering cutting force coefficients, of which literature has two main approaches, both of which occur under full engagement: linear-direction slotting cuts. The geometry of the uncut chip has a strong dependency on the toolpath, particularly for trochoidal milling techniques, where the chips do not exhibit the repeating and somewhat invariant thickness profile that occurs in slotting. Gathering cutting force coefficients for trochoidal milling remains largely unexplored and is the focus of this paper, particularly to establish an understanding of cutting parameters and coefficient values among different toolpath techniques. It was found that matching the tool feed and speed between trochoidal and slotting toolpaths produces large differences, which are reduced when using slotting parameters based on trochoidal milling chip geometry conditions, with the closest agreement resulting from matching the maximum chip thicknesses. Furthermore, the method of gathering coefficients produces similar results between single and double flute trochoidal, which are both less than the coefficients resulting from slotting tests.


Abram Pleta, Farbod Akhavan Niaki, and Laine Mears [NAMRC, 2017]

This paper presents a novel approach to modelling the chip thickness of the process for low to medium range cutting speeds. It has been found that the tool path cannot be described as a purely circular path, instead requiring the model of a true trochoid, which is presented in this work. Utilizing efficient, numerical method, the instantaneous chip thickness is solved for and validated experimentally with cutting force measurement, using a semi- mechanistic force model, where the experimental cutting forces find good agreement with the simulated results.


Abram Pleta and Laine Mears [NAMRC, 2016]

Although the field of machining research is very well established, when it comes to nickel-based superalloys there is a large amount of that is yet to be understood. Trochoidal milling has been identified to extend tool life and reduce machining time in the milling of aluminums, however in nickel-based superalloys it remains largely unexplored. This work provided insight into the cutting force behavior of milling nickel-based superalloys using trochoidal milling.


Abram Pleta, Durul Ulutan, and Laine Mears [NAMRC, 2015]

In this study we proposed and evaluated a new tool path termed variable depth milling (VDM) which reduced and eliminate the occurrence of notch wear in nickel-based superalloys. With this method the axial depth of cut is varied across the tool in a linear fashion, progressing from a maximum depth of cut value to zero in an upward ramping fashion. This work characterizes the effects of trochoidal milling and variable depth milling techniques and compares them against a traditional milling technique, end milling. In order to compare these alternative tool path approaches directly to end milling the authors utilize relatively new metrics to provide a more representative comparison of productivity and efficiency characteristics: volumetric material removal per unit tool wear (MR/VB) and the material removal rate per unit tool wear (MRR/VB). It was found that trochoidal milling was superior to end milling in terms of productivity, where trochoidal could machine at least seven times more material than end milling with the same amount of tool wear with similar efficiency as end milling. It was demonstrated that VDM eliminated the formation of notch wear in the cutting tool at the expense of a slight decrease in productivity and efficiency.


Abram Pleta, Durul Ulutan, and Laine Mears [MSEC, 2016]

This work characterized the effects of trochoidal milling and provided a comparison of trochoidal milling with a traditional milling technique, end milling, for the machining of Inconel 738. In order to compare the trochoidal and conventional machining approaches directly, metrics of productivity normalized to tool wear are introduced. The normalized metrics introduced in this study aim to provide a more representative comparison of productivity and efficiency characteristics: volumetric material removal per unit tool wear (MR/VB) and the material removal rate per unit tool wear (MRR/VB). It was found that significantly higher volumetric material removal is possible using trochoidal milling, and fewer tools are needed; material removal rates that competitive with end milling can be achieved. When the amount of time spent on tool change for the same volume of material removal is considered, material removal rate of trochoidal milling can even be higher than end milling, indicating that better productivity and efficiency of the process is possible at reduced tooling costs.

My current and previous partnerships

Clemson University


Goodyear Tire and Rubber Co

GE Power

BMW Manufacturing

Penn State University

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©2017 by Abram Pleta | Machining Researcher