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Publications

Search Publications by John L. Michaloski

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Displaying 1 - 25 of 75

Physics-based Simulation of Agile Robotic Systems

November 14, 2019
Author(s)
Pavlo Piliptchak, Murat Aksu, Frederick M. Proctor, John L. Michaloski
Development and testing of industrial robot environments is hampered by the limited availability of hardware resources. Simulations provide a more accessible and readily modifiable alternative to physical testing, but also require careful design to ensure

VIRTUAL EXPERIMENTAL INVESTIGATION FOR INDUSTRIAL ROBOTICS IN GAZEBO ENVIRONMENT

November 15, 2018
Author(s)
Murat Aksu, John L. Michaloski, Frederick M. Proctor
Measuring the agility performance of the industrial robots as they are performing in unstructured and dynamic environments is a thought-provoking research topic. This paper investigates the development of industrial robotic simulation algorithms for the

Tolerances and Uncertainty in Robotic Systems

November 9, 2017
Author(s)
Frederick M. Proctor, John L. Michaloski, Marek Franaszek
The ability to be programmed for a wide range of tasks is what differentiates robots from dedicated automation. Consequently, robots can be faced with often-changing requirements and conditions. Conventional application development based on teach

Automating Asset Knowledge with MTConnect

June 27, 2016
Author(s)
John L. Michaloski, Frederick M. Proctor, Sid Venkatesh, Sidney Ly, Martin Manning
In order to maximize assets, manufacturers should use real-time knowledge garnered from ongoing and continuous collection and evaluation of factory-floor machine status data. In discrete parts manufacturing, factory machine monitoring has been difficult

Integration of CodeSynthesis and QIF

May 30, 2016
Author(s)
John L. Michaloski
This paper describes using CodeSynthesis "XSD" tool and Xerces C++ XML tools in basic Quality Information Framework (QIF) applications. The targeted platforms are Ubuntu and Linux as well as Microsoft Windows. The emphasis of the document is on the

End-to-End Quality Information Framework (QIF) Technology Survey

May 9, 2016
Author(s)
John L. Michaloski, Thomas D. Hedberg, Hui-Min Huang, Thomas R. Kramer
The goal of this paper is to understand how quality information characterizing the manufactured parts can be reported in a XML standardized format. The Quality Information Framework (QIF) is an ANSI standard sponsored by the Dimensional Metrology Standards

End-to-end Demonstration of the Quality Information Framework (QIF) Standard at the International Manufacturing Technology Show (IMTS) 2014

March 15, 2016
Author(s)
Hui-Min Huang, John L. Michaloski, Daniel Campbell, Robert Stone, Thomas R. Kramer, Curtis Brown, Robert Brown, Gavrai Tatarliev, William Sobel, Lyle Fischer
The "Silos of Quality" in manufacturing can be described as the proliferation of customized quality languages for different stages of production. This quality "tower of babel" results in excessive translations causing loss of information, reduction of

Toward the Ideal of Automating Production Optimization

November 15, 2013
Author(s)
John L. Michaloski, Frederick M. Proctor, Jorge Arinez, Jonatan Berglund
The advent of improved factory data collection offers a prime opportunity to continuously study and optimize factory operations. Although manufacturing optimization tools can be considered mainstream technology, most U.S. manufacturers do not take full

Toward Better Integration of Vehicle Assembly Production Systems

November 15, 2012
Author(s)
Jorge Arinez, John L. Michaloski, Frederick M. Proctor, William G. Rippey, C J. Yen
In today's manufacturing world, system integration often necessitates composing systems of technology that are not designed to interoperate with each other. This inherent incompatibility results in redundant, non-value added work that is required for

Factory Equipment Network Testing Framework: Concept, Requirements, and Architecture

September 17, 2012
Author(s)
James D. Gilsinn, Kang B. Lee, John L. Michaloski, Frederick M. Proctor, Yuyin Song
This document describes the purpose, concept, requirements, and architecture for the Factory Equipment Network Testing (FENT) Framework and the software to test equipment on real-time factory networks. Other documents contain more detailed information

Web-enabled Real-time Quality Feedback for Factory Systems using MTConnect

August 15, 2012
Author(s)
John L. Michaloski, Byeong Eon Lee, Frederick M. Proctor, Sid Venkatesh
Quality is a key element to success for any manufacturer, and the fundamental prerequisite for quality is measurement. In the discrete parts industry, quality is attained through inspection of parts but typically there is a long latency between machining

Synthesis of Manufacturing and Facility Data for Sustainability Analysis

June 8, 2012
Author(s)
John L. Michaloski, Guodong Shao, Swee K. Leong, Frank H. Riddick, Jonatan Berglund, Jorge Arinez, Stephan Biller
This paper discusses data synthesis of production and facility knowledge for sustainability analysis by applying the ISA 95 "Activity Models of Manufacturing Operations Management" (MOM) model. Presently, production and facility management are "silo"

Web-enabled, Real-time, Quality Assurance for Machining Production Systems

April 19, 2012
Author(s)
John L. Michaloski
In order to maintain the close control of production quality, frequent measurement and process parameter adjustments are desirable. In the discrete parts industry, part inspection is intended to be a metric for the process quality but quality control is

Energy Efficiency Analysis for a Casting Production System

December 14, 2011
Author(s)
Jonatan K. Berglund, John L. Michaloski, Swee K. Leong, Guodong Shao, Frank H. Riddick, Jorge Arinez, Stephan Biller
A growing number of manufacturing industries are initiating efforts to address sustainability issues. A study by the National Association of Manufacturers indicated that the manufacturing sector currently accounts for over a third of all energy consumed in

PUSH-BUTTON DISCRETE EVENT SIMULATION FOR ANALYSIS OF FACTORY FLOOR OPERATIONS

November 18, 2010
Author(s)
John L. Michaloski, Byeong Eon Lee, Frederick M. Proctor, Sid Venkatesh, Nils Bengtsson, Anders Skoogh, Benjamin Raverdy
In manufacturing, Discrete Event Simulation (DES) can be effectively used to model production and provide sustainability analysis of equipment and system operation by measuring throughput, capacity, and bottlenecks. DES allows analysis under different

MTCONNECT-BASED KAIZEN FOR MACHINE TOOL PROCESSES

August 18, 2010
Author(s)
Byeong Eon Lee, John L. Michaloski, Frederick M. Proctor, Sid Venkatesh, Nils Bengtsson
Kaizen is a part of Lean Manufacturing that focuses on the concept of continuous improvement to reduce waste. For implementing Kaizen on the factory floor, comprehensive and efficient tools for data acquisition, process measurement and analysis are

STEPNC++ - An Effective Tool for Feature-based CAM/CNC

December 1, 2009
Author(s)
John L. Michaloski, Thomas R. Kramer, Frederick M. Proctor, Xun Xu, Sid Venkatesh, David Odendahl
This chapter discusses the realization of direct translation of feature-based CAM files into feature-based CNC part program files. The information infrastructure that allows this to happen is STEP-NC, as described by ISO 14649 Parts 10 and 11. Among the

Quantifying the Performance of MT-Connect in a Distributed Manufacturing Environment

October 8, 2009
Author(s)
John L. Michaloski, Byeong Eon Lee, Frederick M. Proctor, Sid Venkatesh, Sidney Ly
In the CNC manufacturing world, the continuing pressure to reduce costs and improve time to market places a premium on smarter ways of manufacturing and intensifies the need to integrate feedback from the shop floor into the enterprise business systems

Standardization of Auxiliary Equipment for Next Generation CNC Machining

October 17, 2008
Author(s)
David Odendahl, Sid Venkatesh, John L. Michaloski, Frederick M. Proctor
This paper presents the recent work of the Open Modular Architecture Control (OMAC) Machine Tool Working Group to support STEP-NC, which is a new standard for the exchange of comprehensive Computer Numerical Control (CNC) manufacturing data. Because of the