Developing Measurement Science to Verify and Validate the Identification of Robot Workcell Degradation

2019 ◽  
Author(s):  
Brian A. Weiss
Keyword(s):  
Failure Modes ◽  
Performance Metrics ◽  
Health Assessment ◽  
Test Methods ◽  
Data Sets ◽  
Robot Arm ◽  
Performance Targets ◽  
Manufacturing Operations ◽  
Measurement Science ◽  
Manufacturing Competitiveness

Abstract Robot systems have become more prevalent in manufacturing operations as the technology has become more accessible to a wider range of manufacturers, especially small to medium-sized organizations. Although these robot technologies have become more affordable, easier to integrate, and greater in functional capability, these advanced systems increase workcell complexity leading to the presence of more fault and failure modes. Given increasing manufacturing competitiveness, maximizing asset availability and maintaining desired quality and productivity targets have become essential. The National Institute of Standards and Technology (NIST) is developing measurement science (e.g., test methods, performance metrics, reference data sets) to monitor the degradation within a manufacturing workcell that includes a six-degree-of-freedom robot arm. Numerous components of the workcell influence the accuracy of the robot’s tool center position. Identifying the component(s) responsible for process degradation prior to the process performing out of specification will provide manufacturers with advanced intelligence to maintain or maximize their performance targets and asset availability. NIST’s research in robot workcell health promotes workcell component health characterization and develops methods and tools to verify and validate this approach. This paper presents the overall research plan and the efforts to date in developing appropriate test methods, identifying key sources of workcell degradation, and presenting baseline performance data that is leveraged for health assessment.

scholarly journals Advancing Measurement Science to Assess Monitoring, Diagnostics, and Prognostics for Manufacturing Robotics

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Guixiu Qiao ◽  
Brian A. Weiss
Keyword(s):  
Method Development ◽  
Performance Metrics ◽  
Health Management ◽  
Test Methods ◽  
Test Method ◽  
Verification And Validation ◽  
Positional Accuracy ◽  
Measurement Science ◽  
Wide Range ◽  
Barriers And Challenges

Unexpected equipment downtime is a ‘pain point’ for manufacturers, especially in that this event usually translates to financial losses. To minimize this pain point, manufacturers are developing new health monitoring, diagnostic, prognostic, and maintenance (collectively known as prognostics and health management (PHM)) techniques to advance the state-of-the-art in their maintenance strategies. The manufacturing community has a wide-range of needs with respect to the advancement and integration of PHM technologies to enhance manufacturing robotic system capabilities. Numerous researchers, including personnel from the National Institute of Standards and Technology (NIST), have identified a broad landscape of barriers and challenges to advancing PHM technologies. One such challenge is the verification and validation of PHM technology through the development of performance metrics, test methods, reference datasets, and supporting tools. Besides documenting and presenting the research landscape, NIST personnel are actively researching PHM for robotics to promote the development of innovative sensing technology and prognostic decision algorithms and to produce a positional accuracy test method that emphasizes the identification of static and dynamic positional accuracy. The test method development will provide manufacturers with a methodology that will allow them to quickly assess the positional health of their robot systems along with supporting the verification and validation of PHM techniques for the robot system.

Implementation of an Ontology-Based Approach to Enable Agility in Kit Building Applications

2018 ◽  
Vol 12 (01) ◽  
pp. 5-24 ◽  
Author(s):  
Zeid Kootbally ◽  
Thomas R. Kramer ◽  
Craig Schlenoff ◽  
Satyandra K. Gupta
Keyword(s):  
Performance Metrics ◽  
Automatic Generation ◽  
Test Methods ◽  
Xml Schema ◽  
Class Model ◽  
Ontology Language ◽  
Information Models ◽  
Measurement Science ◽  
Robot Systems ◽  
Schema Model

The effort described in this paper attempts to integrate agility aspects in the “Agility Performance of Robotic Systems” (APRS) project, developed at the National Institute of Standards and Technology (NIST). The technical idea for the APRS project is to develop the measurement science in the form of an integrated agility framework enabling manufacturers to assess and assure the agility performance of their robot systems. This framework includes robot agility performance metrics, information models, test methods, and protocols. This paper focuses on the information models and describes how they are used to introduce robot agility within the kitting domain. The described models have been fully defined in the XML Schema Definition Language (XSDL) and in the Web Ontology Language (OWL) for kit building applications. Kit building or kitting is a process that brings parts that will be used in assembly operations together in a kit and then moves the kit to the area where the parts are used in the final assembly. Details are given on the automatic generation of OWL class model files from XML schema model files. Files of OWL instances conforming to an OWL class model are generated automatically from XML instance files by automatically-built translators. Furthermore, a design methodology for kit building is presented and shows how the different information models are used with the other components of the methodology.

scholarly journals AA-HMM: An Anti-Adversarial Hidden Markov Model for Network-Based Intrusion Detection

2018 ◽  
Vol 8 (12) ◽  
pp. 2421 ◽  
Author(s):  
Chongya Song ◽  
Alexander Pons ◽  
Kang Yen
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Performance Metrics ◽  
Hidden Markov ◽  
Data Sets ◽  
Learning Abilities ◽  
Benchmark Data ◽  
Malicious Behavior ◽  
Network Intrusion ◽  
The Common

In the field of network intrusion, malware usually evades anomaly detection by disguising malicious behavior as legitimate access. Therefore, detecting these attacks from network traffic has become a challenge in this an adversarial setting. In this paper, an enhanced Hidden Markov Model, called the Anti-Adversarial Hidden Markov Model (AA-HMM), is proposed to effectively detect evasion pattern, using the Dynamic Window and Threshold techniques to achieve adaptive, anti-adversarial, and online-learning abilities. In addition, a concept called Pattern Entropy is defined and acts as the foundation of AA-HMM. We evaluate the effectiveness of our approach employing two well-known benchmark data sets, NSL-KDD and CTU-13, in terms of the common performance metrics and the algorithm’s adaptation and anti-adversary abilities.

Evaluation of Localization Algorithms

2009 ◽  
pp. 348-379 ◽  
Author(s):  
Michael Allen ◽  
Sebnem Baydere ◽  
Elena Gaura ◽  
Gurhan Kucuk
Keyword(s):  
Performance Metrics ◽  
Methodological Approach ◽  
Evaluation Criteria ◽  
Simulation Models ◽  
Data Sets ◽  
Localization Algorithm ◽  
Localization Algorithms ◽  
Development Cycle ◽  
Complete Test ◽  
And Performance

This chapter introduces a methodological approach to the evaluation of localization algorithms. The chapter contains a discussion of evaluation criteria and performance metrics followed by statistical/ empirical simulation models and parameters that affect the performance of the algorithms and hence their assessment. Two contrasting localization studies are presented and compared with reference to the evaluation criteria discussed throughout the chapter. The chapter concludes with a localization algorithm development cycle overview: from simulation to real deployment. The authors argue that algorithms should be simulated, emulated (on test beds or with empirical data sets) and subsequently implemented in hardware, in a realistic Wireless Sensor Network (WSN) deployment environment, as a complete test of their performance. It is hypothesised that establishing a common development and evaluation cycle for localization algorithms among researchers will lead to more realistic results and viable comparisons.

Review of material test standardization status for the material qualification of laminated thermosetting composite structures

2020 ◽  
pp. 073168442095810
Author(s):  
Sang Yoon Park ◽  
Won Jong Choi
Keyword(s):  
Composite Materials ◽  
Material Properties ◽  
Composite Structures ◽  
Failure Modes ◽  
Laminated Composite ◽  
Test Methods ◽  
Test Method ◽  
Experimental Comparison ◽  
Laminated Composite Materials ◽  
Design Values

This paper presents a review of recent literature related to the static mechanical testing of thermoset-based carbon fiber reinforced composites and introduces a material qualification methodology to generate statistically-based allowable design values for aerospace application. Although most test methods have been found to be effective in determining the specific material properties by incorporating them into the material qualification and quality control provisions, a full validation to clarify the behavior of thermoset-based laminated composite materials is currently lacking, particularly with regard to the characterization of compressive, in-plane, interlaminar shear, and damage tolerance properties. The present study obtains information on the different types of test method that can be employed within the same material properties, and makes an in-depth experimental comparison based on the past literatures. A discussion on the scope of theoretical analysis involves a description of how the proposed test method can be adequate for obtaining more accurate material properties. This discussion is directly applicable to the assessment of material nonlinearity and the geometrical effect of specimens. Finally, the resulting failure modes and the effect of each material property are studied to aid the understanding of the load distribution and behavior of laminated composite materials.

Robotic Instrumented Complaint Wrist

1992 ◽  
Vol 114 (1) ◽  
pp. 120-123 ◽  
Author(s):  
Yangsheng Xu ◽  
R. P. Paul
Keyword(s):  
Force Control ◽  
Position Control ◽  
Contact Forces ◽  
Displacement Sensor ◽  
Robot Arm ◽  
Sensing Mechanism ◽  
Manufacturing Operations ◽  
Positioning Errors ◽  
Control Scheme ◽  
Passive Compliance

A robotic complaint wrist which combines a passive compliance device and a displacement sensor has been developed and tested. The device provides the necessary flexibility to accommodate transitions between the position control and force control modes, and avoid large impact forces as a robot makes contact with parts, as well as correct positioning errors and allow the relaxation of tolerances in assembly and manufacturing operations. The device installed between a robot arm and end-effector is composed of two parts: a passive compliance device and a sensing mechanism. The passive compliance is provided by a rubber structure; its configuration can be arranged to yield the desired stiffness ratio along and about each axis. The sensing mechanism consists of a six-joint serial linkage with a transducer at each point. The measured deflection is used to actively control the contact forces and compensate for the positioning error during motion and contact. In this paper, the design features of two prototypes of the device are described. A systematic hybrid position/force control scheme incorporating the device is presented.

CMIP model evaluation with the ESMValTool v2.0

2020 ◽  
Author(s):  
Axel Lauer ◽  
Fernando Iglesias-Suarez ◽  
Veronika Eyring ◽  
the ESMValTool development team
Keyword(s):  
Model Evaluation ◽  
Large Scale ◽  
Performance Metrics ◽  
Comprehensive Evaluation ◽  
Coupled Model ◽  
Data Sets ◽  
Evaluation Tool ◽  
Emergent Constraints ◽  
Data Volume ◽  
And Performance

<p>The Earth System Model Evaluation Tool (ESMValTool) has been developed with the aim of taking model evaluation to the next level by facilitating analysis of many different ESM components, providing well-documented source code and scientific background of implemented diagnostics and metrics and allowing for traceability and reproducibility of results (provenance). This has been made possible by a lively and growing development community continuously improving the tool supported by multiple national and European projects. The latest version (2.0) of the ESMValTool has been developed as a large community effort to specifically target the increased data volume of the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the related challenges posed by analysis and evaluation of output from multiple high-resolution and complex ESMs. For this, the core functionalities have been completely rewritten in order to take advantage of state-of-the-art computational libraries and methods to allow for efficient and user-friendly data processing. Common operations on the input data such as regridding or computation of multi-model statistics are now centralized in a highly optimized preprocessor written in Python. The diagnostic part of the ESMValTool includes a large collection of standard recipes for reproducing peer-reviewed analyses of many variables across atmosphere, ocean, and land domains, with diagnostics and performance metrics focusing on the mean-state, trends, variability and important processes, phenomena, as well as emergent constraints. While most of the diagnostics use observational data sets (in particular satellite and ground-based observations) or reanalysis products for model evaluation some are also based on model-to-model comparisons. This presentation introduces the diagnostics newly implemented into ESMValTool v2.0 including an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of ESMs, new diagnostics for extreme events, regional model and impact evaluation and analysis of ESMs, as well as diagnostics for emergent constraints and analysis of future projections from ESMs. The new diagnostics are illustrated with examples using results from the well-established CMIP5 and the newly available CMIP6 data sets.</p>

Experiment on the Bonding Performance of BFRP Bars Reinforced Concrete

2012 ◽  
Vol 174-177 ◽  
pp. 993-998 ◽  
Author(s):  
Shi Yong Jiang ◽  
Yong Ye ◽  
Wei Fei
Keyword(s):  
Reinforced Concrete ◽  
Failure Modes ◽  
Concrete Strength ◽  
Basalt Fiber ◽  
Test Methods ◽  
Load Level ◽  
Reinforced Plastics ◽  
Bonding Performance ◽  
Pull Out ◽  
Bonding Properties

Through the pull-out test methods, the concrete strength、reinforcement diameter Basalt Fiber Reinforced Plastics Bars、the anchorage length、 stirrup rate and other factors on the bonding properties of the BFRP reinforced concrete is analyzed. The BFRP bars and reinforcing steel bars bonding properties is compared. BFRP reinforced concrete bond failure mode has two types .As the concrete strength increases, the bond strength of the BFRP reinforced concrete increased. With the increase BFRP bars diameter and shear lag relationship, the cohesive force of the BFRP reinforced concrete decrease accordingly. And the failure modes of the shape of the BFRP reinforcement concrete in BFRP bonding properties with a big impact for the specimens’ configuration stirrups on the ductility. When BFRP bars loading under the same load level, the end of the slip is greater than the free end slip.

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