2023

Jaydeep Mohan Karandikar, PhD, Oak Ridge National Laboratory

Unlocking the potential of machining processes has never been easier. Dr. Karandikar, a visionary in the field, presented two groundbreaking papers at NAMRC that revolutionize machining parameter selection for total cost optimization, with a specific focus on tool life. This cutting-edge method allows for in-process optimization of machining operations in real-world industrial environments, eliminating the need for costly and time-consuming laboratory experiments.

With Dr. Karandikar's innovative approach, industrial manufacturers can now achieve unprecedented levels of efficiency and cost reduction. By leveraging this groundbreaking technique, GE implemented the methodology and experienced an impressive 30% average cost reduction across more than 200 operations. The result? A staggering annualized savings of approximately $20 million for GE Aviation.

Gone are the days of relying solely on trial-and-error methods and excessive expenditure on experimental setups. Dr. Karandikar's groundbreaking work empowers manufacturers to optimize machining parameters with precision, thereby minimizing costs and maximizing productivity. This game-changing approach enables businesses to streamline their operations, achieve remarkable cost savings, and gain a competitive edge in the industry.

Don't miss out on the opportunity to implement Dr. Karandikar's transformative method for machining parameter selection. Join the ranks of forward-thinking manufacturers who are already reaping the rewards of this revolutionary approach. Embrace the future of machining optimization and witness firsthand the immense benefits it brings to your operations.

2022

Jay Lee, PhD, FSME​, University of Cincinnati

Dr. Lee has frequently contributed high-impact research works to SME NAMRC Conferences and technical journals for publication. The four selected NAMRC papers are listed below. In the 2003 NAMRC paper, Dr. Lee and his collaborators proposed a novel algorithm for generic process/machine performance assessment based on merged multiple sensor readings. This algorithm is built on the extraction of generic signal features and generic methods of signature matching, and thus the methodology is application-independent, and can be applied in a wide range of applications. The concept was quickly adopted in many applications by researchers in the United States, Japan, Spain, New Zealand, and China. In the three NAMRC papers published in 2019-202, Dr. Lee and his collaborators developed advanced machine learning techniques such as deep learning and domain adaptation for prognostics and diagnosis in industrial applications.

• “Multisensor process performance assessment through the use of autoregressive modeling and feature maps”, Transactions of XXXI SME/NAMRI, 2003, 31.483-490.
• “Detection and diagnosis of bottle capping failures based on motor current signature analysis,” Procedia Manufacturing 2019 34, 840-846
• “Deep learning-based intelligent process monitoring of directed energy deposition in additive manufacturing with thermal images,” Procedia Manufacturing 2020, 48, 643-649
• “Enhancing intelligent cross-domain fault diagnosis performance on rotating machines with noisy health labels,” Procedia Manufacturing 2020, 48, 940-946

2021

Jianjun (Jan) Shi, PhD, Carolyn J. Stewart Chair and Professor H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

For 2021, Jianjun Shi from the Georgia Institute of Technology is receiving this award for his novel method for integrating quality/defect and process data for quality improvement into hot steel manufacturing processes. The method reduces defects in hot-rolling steel manufacturing processes by integrating in-situ imaging and process data monitoring and has been used to date by more than 40 steel mills globally.

Dr. Shi’s team built an innovative modeling system that utilized data pre-processing, data-analytics-driven dichotimization of product quality levels (good or bad components), process-quality modeling of process variables and defect levels, and active quality control. This integration of inline sensing information, the production database, focused data mining, and optimization provided a quality improvement solution for the hot rolling process.

The immediate result: the reduction of defects with hot-rolled wire rod products leading to significant waste reductions. The logistic regression model also has been used by others to model degenerative biological systems as well as for improving nursing care quality.

2020

Sheri K. Kurgin, PhD, Senior Manufacturing Engineer, General Motors, Macomb, Michigan

Jie Gu, PhD, Senior Project Engineer, General Motors, Pontiac, Michigan

Sheri Kurgin, PhD, and Jie Gu, PhD, are recognized with the 2020 NAMRI/SME S.M. Wu Research Implementation Award for the GM COMP system and method that was invented and developed with the sole purpose of performing global offset compensation in the multiaxis machine tools. Sheri Kurgin, PhD, and Jie Gu, PhD, are the leaders in the development and implementation of the CNC offset method. Three technical papers were presented at the 2015, 2016 and 2017 NAMRC events to describe the concept and highlight some of the major technical challenges. The COMP system is integrated in all the new machine tools in production for more than eight years.

The patented and commercialized innovative COMP system and method enables calculating and implementing global and local compensation for multiaxis computer- controlled manufacturing systems as opposed to traditional methods. Benefits are better part quality, quality consistency, (>3%) improved productivity and enhanced product/process flexibility.

2018

Joseph J. Beaman, SCD, FSME, The University of Texas at Austin

Beaman was recognized with the 2018 NAMRI/SME S.M. Wu Research Implementation Award for his groundbreaking work on selective laser sintering. His co-authored paper, “Solid Freeform Fabrication and Selective Powder Sintering,” was originally published in the Transactions of NAMRC-XV, May 27-29, 1987. Beaman’s vision on SLS was 15 years ahead of the general research community, and it has revolutionized the manufacturing industry by offering incredible freedom in creating complex geometry. He was later one of the founders of DTM Corp. (now merged with 3D Systems), which markets SLS.

2017

Ajay Malshe, PhD, NanoMech Inc. 

Malshe is recognized with the 2017 NAMRI/SME S.M. Wu Research Implementation Award for his patented and patent-pending nano-engineered-based multicomponent chemistry lubricants for advanced mechanical applications. This innovative platform is based upon an “open-architecture and multicomponent design approach applying intercalation at subnanometric scales to deliver multifunctional macromolecules-on-demand.” The related contact mechanics application-specific chemical components are selected from an array of organic and inorganic atomic and molecular chemistries. NAMRC Paper Citations: “Tribological Study of Nanolubricant Integrated Soybean Oil for Minimum Quantity Lubrication (MQL) Grinding,” Transactions of NAMRI/SME, Volume 38, 2010; “Performance of Novel MoS2 Nanoparticles Based Grinding Fluids in Minimum Quantity Lubrication (MQL) Grinding,” Transactions of NAMRI/SME, Volume 36, 2008.

2016

Yung C. Shin, PhD, FSME, Purdue University

Shin is receiving the 2016 NAMRI/SME S.M. Wu Research Implementation Award for pioneering the scientific development and industrial implementation of laser-assisted machining (LAM) technology for difficult-to-machine materials, leading to significant improvements in productivity and sizeable reductions in machining costs. In collaboration with Nanohmics and Progressive Manufacturing Technology, this technology was successfully commercialized as a LAM-enabled CNC machine tool for cost-effective machining of structural ceramic components. This technology has revolutionized the production of parts fabricated from advanced ceramics and high-temperature alloys. The NAMRC paper referenced with the award is: "Comparative Assessment of Laser-assisted Machining for Various Ceramics," Transactions of NAMRI/SME, Volume 30, 2002.

2015

John S. Agapiou, PhD, FSME, General Motors Corp.
 
Agapiou is recognized for his innovative design of four-flute and modified three-flute, solid-carbide drills to produce top-quality bores in one or two passes as opposed to the traditional three to six passes, with sizable savings in cycle time, investment cost, tooling, and maintenance. This technology was disseminated throughout the holemaking processes in GM Powertrain with thousands of such tools used in production and adapted by most of the automotive industries today. The multiflute point designs have spread to different types of holemaking tools and subsequently commercialized by many tooling companies to achieve complex part features or precision bores. The NAMRC paper referenced with the award is: "An Evaluation of Advanced Drill Body and Point Geometries in Drilling Cast Iron." Transactions of NAMRI/SME, Volume 19, 1991

2014

S. Jack Hu, PhD, University of Michigan 

Hu is recognized for the innovative method he developed “for systematically identifying sources of quality variation in assembly systems. This method has been implemented in the Chrysler Corporation and the General Motors Co., resulting in significant quality improvements and economic benefits.” The NAMRC paper referenced with the award is Hu, S.J. and Wu, S.M., “Identifying Sources of Variation in Automobile Assembly Using Principal Component Analysis,” Transactions of NAMRI/SME, Volume 20, 1992.

2013

Jun Ni, University of Michigan 

Ni is recognized for his contribution to the establishment of the machine tool error modeling and compensation method that enables precision machining and advances the fundamental knowledge of the machine tool thermal error mode and analysis approach. The method developed has been successfully implemented in industry, including at General Motors, Chrysler, Saginaw Machine Systems and Boeing. Ni's research was originally presented in the following paper: “Error Link Metrology and Flexible Error Synthesis Model for Correcting Quasi-Static Machine Errors,” Transactions of NAMRI/SME, Volume XXII, 1994.

2012

Kevin Scott Smith, University of North Carolina at Charlotte; Jerry Halley, Tech Manufacturing; and Robert G. Wilhelm, University of North Carolina at Charlotte 

Smith, Halley and Wilhelm’s research applied high-speed machining technology and novel tool path planning approaches to machine solid billets of material to typical sheet metal thicknesses, enabling cost-effective machining of complex monolithic structures with extremely thin walls and unsupported floors. This technology directly led to the widespread replacement of aircraft structural elements previously fabricated by riveting of many complex sheet metal stampings with lighter, stronger, more robust and less-expensive monolithic components. Smith et al.'s research was originally presented in the following papers: "Forced Vibration, Chatter, Accuracy, in High Speed Milling," Transactions of NAMRI, Vol. 13; "NC Programming for Quality in Milling," Transactions of NAMRI, Vol. 16; "Sensor-Based Supervision of CNC Machining," Transactions of NAMRI, Vol. 20; and "Beyond Geometry: Process Planning for High Speed Machining of Monolithic Structures," Transactions of NAMRI, Vol. 34.

2011

C. Richard Liu, Purdue University

The 2011 award recipient's research has created "single step finish/superfinish hard machining" for performing roughing, finishing and superfinishing processes within a single setup, and has developed a new market and a science base for engineering a new generation of processes, machine tools, cutting tools and manufacturing systems. This research has been successfully applied in manufacturing load-carrying components due to its significant benefits over competing technologies. Liu's research was originally presented in the following papers: "An Error Correction Method for CNC Machine Tools Using Reference Parts," introduced at the 22nd North American Manufacturing Research Conference and published in the Transactions of NAMRI/SME 1994, Vol. 22 and "Residual Stress Formation Mechanism and Its Control by Sequential Cuts," introduced at the 28th North American Manufacturing Research Conference and published in the Transactions of NAMRI/SME 2000, Vol. 28. 

2010

Richard E. DeVor and S.G. Kapoor, University of Illinois at Urbana Champaign

The 2010 award recipients are recognized for their innovative research beginning in the early 1980s on the development of mechanistic simulation models for machining processes. These simulation models have commercially benefited industry by providing guidance in terms of product design and process planning, and in supporting the design of machine tool systems at both the conventional and microscales. This work was originally outlined in the paper "Analysis of Cutting Forces in Face Milling of High Silicon Casting Aluminum Alloys," which was introduced at the 10th North American Manufacturing Research Conference and published in the Transactions of NAMRI/SME 1982, Vol. 11.

2009

William R.D. Wilson, Northwestern University

The 2009 award recipient is recognized for the establishment of fundamental contact mechanics in analyzing surface asperities and lubrication film thickness to enable the development of more accurate friction models in metalforming operations, especially the optimization of metal rolling processes. Wilson's research was originally outlined in "Flattening of Workpiece Surface Asperities in Metal Forming Processes," which was introduced at the 11th North American Manufacturing Research Conference and published in the Transactions of NAMRI/SME 1983, Vol. 11, and "Strategy for Friction Modeling in Computer Simulations of Metalforming Processes," which was presented at the 16th North American Manufacturing Research Conference and published in the Transactions of NAMRI/SME 1988, Vol. 16. 

2008

Jae M. Lee, Chi-Hung Shen and Ernie Wasserbaech, General Motors Corp.

The 2008 award recipients were recognized for having patented and commercialized the innovative abrasive belt camlobe grinder, which enabled simultaneous grinding of all of the camlobes as opposed to traditional sequential grinding. This advance set a significant new standard in the high-value/low-cost manufacture of this critical automotive component. Lee, Shen, and Wasserbaech's research was originally introduced in the paper, "Camshaft Grinding Using Coated Abrasive Belts," which was presented at the 21st North American Manufacturing Research Conference and published in the Transactions of NAMRI/SME 1993, Vol. 21. 

2007

Paul K. Wright, University of California, Berkeley

The 2007 award recipient is recognized for the creation of the "open architecture control" that expanded machine tool control and enabled both better connections to high-end CAD images so that more complex surfaces could be machined, and the use of more sensors for in-process inspection and real-time adjustments. This work was presented work in the paper, "Self-Sustaining, Open-System Machine Tools," by Israel Greenfeld, Fred B. Hansen and Paul K. Wright, which was presented at the 17th North American Manufacturing Research Conference and published in the Transactions of NAMRI/SME 1989, Vol. 17. 

2006

Taylan Altan, The Ohio State University; Goverdhan D. Lahoti, The Timken Co.; and Soo Ik Oh, Seoul National University

The three recipients of the 2006 NAMRI/SME S.M. Wu Research Implementation Award were recognized for developing concepts and methods related to the finite element modeling (FEM) of forming problems, which was later commercialized into a FEM package known as DEFORM. Altan, Oh and Lahoti originally presented their work in a 1981 NAMRC paper, "ALPID - A General Purpose FEM Program for Metal Forming," which was originally presented at the 1981 North American Metalworking Research Conference and published in the Transactions of NAMRI/SME 1981, Vol. 9.