Hierarchical Decomposition of a Manufacturing Work Cell to Promote Monitoring, Diagnostics, and Prognostics

2017 ◽  
Author(s):  
Brian A. Weiss ◽  
Guixiu Qiao
Keyword(s):  
Machine Tools ◽  
Health Management ◽  
Control Strategies ◽  
Industrial Robot ◽  
Work Cell ◽  
Critical Elements ◽  
Functional Relationships ◽  
Manufacturing Operations ◽  
Robot Systems ◽  
And Control

Manufacturing work cell operations are typically complex, especially when considering machine tools or industrial robot systems. The execution of these manufacturing operations require the integration of layers of hardware and software. The integration of monitoring, diagnostic, and prognostic technologies (collectively known as prognostics and health management (PHM)) can aid manufacturers in maintaining the performance of machine tools and robot systems by providing intelligence to enhance maintenance and control strategies. PHM can improve asset availability, product quality, and overall productivity. It is unlikely that a manufacturer has the capability to implement PHM in every element of their system. This limitation makes it imperative that the manufacturer understand the complexity of their system. For example, a typical robot systems include a robot, end-effector(s), and any equipment, devices, or sensors required for the robot to perform its task. Each of these elements is bound, both physically and functionally, to one another and thereby holds a measure of influence. This paper focuses on research to decompose a work cell into a hierarchical structure to understand the physical and functional relationships among the system’s critical elements. These relationships will be leveraged to identify areas of risk, which would drive a manufacturer to implement PHM within specific areas.

Monitoring, Diagnostics, and Prognostics for Robot Tool Center Accuracy Degradation

2018 ◽  
Author(s):  
Guixiu Qiao ◽  
Brian A. Weiss
Keyword(s):  
Health Management ◽  
Control Strategies ◽  
Test Methods ◽  
Component Level ◽  
Data Set ◽  
Root Cause ◽  
Robot Systems ◽  
Robot Performance ◽  
And Control ◽  
Constituent Elements

Over time, robots degrade because of age and wear, leading to decreased reliability and increasing potential for faults and failures; this negatively impacts robot availability. Economic factors motivate facilities and factories to improve maintenance operations to monitor robot degradation and detect faults and failures, especially to eliminate unexpected shutdowns. Since robot systems are complex, with sub-systems and components, it is challenging to determine these constituent elements’ specific influence on the overall system performance. The development of monitoring, diagnostic, and prognostic technologies (collectively known as Prognostics and Health Management (PHM)), can aid manufacturers in maintaining the performance of robot systems by providing intelligence to enhance maintenance and control strategies. This paper presents the strategy of integrating top level and component level PHM to detect robot performance degradation (including robot tool center accuracy degradation), supported by the development of a four-layer sensing and analysis structure. The top level PHM can quickly detect robot tool center accuracy degradation through advanced sensing and test methods developed at the National Institute of Standards and Technology (NIST). The component level PHM supports deep data analysis for root cause diagnostics and prognostics. A reference data set is collected and analyzed using the integration of top level PHM and component level PHM to understand the influence of temperature, speed, and payload on robot’s accuracy degradation.

Design and control strategies for SMA actuators in a feed axis for precision machine tools

2013 ◽  
Vol 7 (5) ◽  
pp. 547-553 ◽  
Author(s):  
Horst Meier ◽  
Jan Pollmann ◽  
Alexander Czechowicz
Keyword(s):  
Machine Tools ◽  
Control Strategies ◽  
Sma Actuators ◽  
Precision Machine ◽  
Precision Machine Tools ◽  
Feed Axis ◽  
And Control

scholarly journals Bacteria-Bacteriophage Cycles Facilitate Cholera Outbreak Cycles: An Indirect Susceptible - Infected - Bacteria - Phage (Isibp) Model-Based Mathematical Study

2021 ◽  
Author(s):  
Asma Al Habees ◽  
Eman Aldabbas ◽  
Nicola Bragazzi ◽  
Jude Kong
Keyword(s):  
Bacterial Population ◽  
Compartmental Model ◽  
Health Management ◽  
Control Strategies ◽  
Equilibrium Points ◽  
Infectious Agent ◽  
Sensitivity Analyses ◽  
And Control ◽  
Disease Free ◽  
Phage Population

Cholera is an acute enteric infectious disease caused by the Gram-negative bacterium Vibrio Cholerae. Despite a huge body of research, the precise nature of its transmission dynamics has yet to be fully elucidated. Mathematical models can be useful to better understand how an infectious agent can spread and be properly controlled. We develop a compartmental model describing a Human population, a bacterial population as well as a phage population. We show that there might be eight equilibrium points; one of which is a disease free equilibrium point. We carry out numerical simulations and sensitivity analyses and we show that the presence of phage can reduce the number of infectious individuals. Moreover, we discuss the main implications in terms of public health management and control strategies.

Accuracy Degradation Analysis for Industrial Robot Systems

2017 ◽  
Author(s):  
Guixiu Qiao ◽  
Brian A. Weiss
Keyword(s):  
Production Efficiency ◽  
Health Management ◽  
Industrial Robot ◽  
Health Assessment ◽  
Test Method ◽  
Fiber Placement ◽  
Accuracy Requirement ◽  
Measurement Science ◽  
Robot Systems ◽  
Automated Fiber Placement

As robot systems become increasingly prevalent in manufacturing environments, the need for improved accuracy continues to grow. Recent accuracy improvements have greatly enhanced automotive and aerospace manufacturing capabilities, including high-precision assembly, two-sided drilling and fastening, material removal, automated fiber placement, and in-process inspection. The accuracy requirement of those applications is primarily a function of two main criteria: (1) The pose accuracy (position and orientation accuracy) of a robot system’s tool center position (TCP), and (2) the ability of a robot system’s TCP to remain in position or on-path when loads are applied. The degradation of a robot system’s tool center accuracy can lead to a decrease in manufacturing quality and production efficiency. Given the high output rate of production lines, it is critical to develop technologies to verify and validate robot systems’ health assessment techniques, particularly the accuracy degradation. In this paper, the National Institute of Standards and Technology’s (NIST) effort to develop the measurement science to support the monitoring, diagnostics, and prognostics (collectively known as prognostics and health management (PHM)) of robot accuracy degradation is presented. This discussion includes the modeling and algorithm development for the test method, the advanced sensor development to measure 7-D information (time, X, Y, Z, roll, pitch, and yaw), and algorithms to analyze the data.

Robotic Work Cell Test Bed to Support Measurement Science for Monitoring, Diagnostics, and Prognostics

2018 ◽  
Author(s):  
Alexander Klinger ◽  
Brian A. Weiss
Keyword(s):  
Health Management ◽  
Future Research ◽  
Use Case ◽  
Test Bed ◽  
Work Cell ◽  
Functional Relationships ◽  
Measurement Science ◽  
Cell Test ◽  
Measures Of Effectiveness ◽  
Work Cells

Industrial robotics users, integrators, and manufacturers are implementing advanced monitoring, diagnostics, and prognostics (collectively known as Prognostics and Health Management (PHM)) techniques and technologies. PHM can take many different forms when implemented, and measures of effectiveness are highly dependent on the techniques implemented. A test bed has been built, and a use case designed, to represent common manufacturing tasks performed in robot work cells where PHM can provide greater equipment and process health intelligence. The physical and functional relationships within the work cell are mapped using a hierarchical deconstruction method to gain a better understanding of the propagation of effects of both equipment and process degradation. The test bed has been built so PHM techniques and technologies can be integrated and tested in a realistic scenario. Data is recorded for post processing and analysis for the verification and validation (V&V) of the implemented PHM techniques. The test bed will serve as a platform to develop, test, verify, and validate PHM techniques at the National Institute of Standards and Technology (NIST), and provide industry participants a standard platform for testing their PHM technologies. Having a common testing platform will provide industry a foundation for sets of tests to evaluate PHM. This paper presents the test bed and use case, the relationships therein, and the data management and collection approaches used to enable future research.

Using the Microcontroller and the PLC in a RPP Robot Control

2012 ◽  
Vol 463-464 ◽  
pp. 1721-1724
Author(s):  
Laurean Bogdan
Keyword(s):  
Machine Tools ◽  
Robot Control ◽  
Industrial Robot ◽  
Programmable Logic Controller ◽  
Numerical Control ◽  
Programmable Logic ◽  
Industrial Systems ◽  
Classical Systems ◽  
Control Equipment ◽  
And Control

This paper presents some aspects regarding the possibility of combining microcontroller and programmable logic controller (PLC) for robot control. The RPP robot is designed and manufactured within the Faculty of Engineering from “Lucian Blaga” University of Sibiu, as a patent: BRAŢ TELESCOPIC, Nr. 112418 B1, Int.CI.6 B 25 J 18/02, from 17.02.1997. Classical systems for programming and control of industrial robot are based on numerical control equipment, developed around a PC. Programmable Logic Controllers (PLC) haves proven to be viable alternatives to driving machine tools, industrial systems and Robots. Microcontrollers as computers offer advantage due to programmable inputs and outputs ports. The application of a microcontroller and PLC in control of a robot is presented in this paper.

scholarly journals Smart Manufacturing PHM

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Brian A. Weiss ◽  
Philip Freeman ◽  
Jay Lee ◽  
Radu Pavel
Keyword(s):  
Health Monitoring ◽  
Machine Tools ◽  
Manufacturing System ◽  
New Technologies ◽  
Health Management ◽  
Smart Manufacturing ◽  
Prognostics And Health Management ◽  
Monitoring And Control ◽  
Supply Chain Disruptions ◽  
And Control

The age of Smart Manufacturing has arrived where more and more organizations are embracing it to innovate and maintain their competitiveness. Smart Manufacturing blends information technology (IT) with operations technology (OT) to enable greater productivity, efficiency, quality, and customization within factory operations. More specifically, emerging and existing factory-floor level technologies (including robotics, machine tools, additive processes, automation, and sensors) are being fused with networking (both wired and wireless) and analysis technologies to generate more timely, accurate, and appropriate communication. This communication directly enables more intelligent sensing, monitoring, and control of the overall manufacturing system, including its constituent processes and sub-systems. Organizations that are adopting a smart manufacturing approach have become more flexible and adaptive to address changing customer demands, integrate new technologies, mitigate supply chain disruptions, and better utilize their human workforce. Prognostics and Health Management (PHM), in the context of Smart Manufacturing, focuses on the technologies and capabilities that enable health monitoring, diagnostics, and prognostics to promote greater intelligence in maintenance and control activities.

scholarly journals EXAMINATION OF COOLING SYSTEMS IN MACHINE TOOLS REGARDING SYSTEM STRUCTURE AND CONTROL STRATEGIES

2021 ◽  
Vol 2021 (3) ◽  
pp. 4563-4568
Author(s):  
C. Steiert ◽  
◽  
Ju. Weber ◽  
J. Weber ◽  
◽  
...  
Keyword(s):  
Energy Consumption ◽  
Heat Input ◽  
Machine Tools ◽  
Control Strategies ◽  
Cooling Capacity ◽  
System Structure ◽  
Cooling Systems ◽  
System Configurations ◽  
And Control ◽  
Machine Structure

Abstract When analyzing machine tools it is observable that despite sufficient cooling capacity thermo-elastic deformation of the machine structure is badly compensated due to heat input. The reason is the missing adaption of coolant and heat input into the system structure during the process, resulting in insufficient productivity and quality. In this paper, various system configurations are shown that can be used to achieve both adequate thermal performance and a reduction in energy consumption.

scholarly journals The Current State of Sensing, Health Management, and Control for Small-to-Medium-Sized Manufacturers

2016 ◽  
Author(s):  
Moneer Helu ◽  
Brian Weiss
Keyword(s):  
Case Studies ◽  
New Technologies ◽  
Health Management ◽  
Shop Floor ◽  
Manufacturing Operations ◽  
Diagnosis And Prognosis ◽  
Industrial Sectors ◽  
Current State ◽  
Medium Enterprises ◽  
And Control

The development of digital technologies for manufacturing has been challenged by the difficulty of navigating the breadth of new technologies available to industry. This difficulty is compounded by technologies developed without a good understanding of the capabilities and limitations of the manufacturing environment, especially within small-to-medium enterprises (SMEs). This paper describes industrial case studies conducted to identify the needs, priorities, and constraints of manufacturing SMEs in the areas of performance measurement, condition monitoring, diagnosis, and prognosis. These case studies focused on contract and original equipment manufacturers with less than 500 employees from several industrial sectors. Solution and equipment providers and National Institute of Standards and Technology (NIST) Hollings Manufacturing Extension Partnership (MEP) centers were also included. Each case study involved discussions with key shop-floor personnel as well as site visits with some participants. The case studies highlight SME’s strong need for access to appropriate data to better understand and plan manufacturing operations. They also help define industrially-relevant use cases in several areas of manufacturing operations, including scheduling support, maintenance planning, resource budgeting, and workforce augmentation.

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