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.

Industrial Robot Accuracy Degradation Monitoring and Quick Health Assessment

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Guixiu Qiao ◽  
Brian A. Weiss
Keyword(s):  
Production Efficiency ◽  
Health Management ◽  
Research Effort ◽  
Industrial Robot ◽  
Health Assessment ◽  
Test Method ◽  
Health State ◽  
Future Health ◽  
Robot Accuracy ◽  
Degradation Monitoring

Robot accuracy degradation sensing, monitoring, and assessment are critical activities in many industrial robot applications, especially when it comes to the high accuracy operations which may include welding, material removal, robotic drilling, and robot riveting. The degradation of robot tool center accuracy can increase the likelihood of unexpected shutdowns and decrease manufacturing quality and production efficiency. The development of monitoring, diagnostic and prognostic (collectively known as prognostics and health management (PHM)) technologies can aid manufacturers in maintaining the performance of robot systems. PHM can provide the techniques and tools to support the specification of a robot’s present and future health state and optimization of maintenance strategies. This paper presents the robotic PHM research and the development of a quick health assessment at the U.S. National Institute of Standards and Technology (NIST). The research effort includes the advanced sensing development to measure the robot tool center position and orientation; a test method to generate a robot motion plan; an advanced robot error model that handles the geometric/nongeometric errors and the uncertainties of the measurement system, and algorithms to process measured data to assess the robot’s accuracy degradation. The algorithm has no concept of the traditional derivative or gradient for algorithm converging. A use case is presented to demonstrate the feasibility of the methodology.

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.

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.

Automated Fiber Placement of Composite Wind Tunnel Blades: Process Planning and Manufacturing

2019 ◽  
Author(s):  
Ramy Harik ◽  
Joshua Halbritter ◽  
Dawn Jegley ◽  
Ray Grenoble ◽  
Brian Mason
Keyword(s):  
Wind Tunnel ◽  
Process Planning ◽  
Fiber Placement ◽  
Automated Fiber Placement

scholarly journals Review: Filament Winding and Automated Fiber Placement with In Situ Consolidation for Fiber Reinforced Thermoplastic Polymer Composites

Polymers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1951
Author(s):  
Yi Di Boon ◽  
Sunil Chandrakant Joshi ◽  
Somen Kumar Bhudolia
Keyword(s):  
Processing Parameters ◽  
Filament Winding ◽  
Reference Points ◽  
Thermoplastic Composites ◽  
Manufacturing Processes ◽  
Fiber Reinforced ◽  
Consolidation Process ◽  
Fiber Placement ◽  
Automated Fiber Placement

Fiber reinforced thermoplastic composites are gaining popularity in many industries due to their short consolidation cycles, among other advantages over thermoset-based composites. Computer aided manufacturing processes, such as filament winding and automated fiber placement, have been used conventionally for thermoset-based composites. The automated processes can be adapted to include in situ consolidation for the fabrication of thermoplastic-based composites. In this paper, a detailed literature review on the factors affecting the in situ consolidation process is presented. The models used to study the various aspects of the in situ consolidation process are discussed. The processing parameters that gave good consolidation results in past studies are compiled and highlighted. The parameters can be used as reference points for future studies to further improve the automated manufacturing processes.

Effect of gap on strengths of automated fiber placement manufactured laminates

2021 ◽  
Vol 263 ◽  
pp. 113677
Author(s):  
Hiroshi Suemasu ◽  
Yuichiro Aoki ◽  
Sunao Sugimoto ◽  
Toshiya Nakamura
Keyword(s):  
Fiber Placement ◽  
Automated Fiber Placement

scholarly journals Experimental Investigation on the Influence of Fiber Path Curvature on the Mechanical Properties of Composites

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2602
Author(s):  
Huaqiao Wang ◽  
Jihong Chen ◽  
Zhichao Fan ◽  
Jun Xiao ◽  
Xianfeng Wang
Keyword(s):  
Tensile Strength ◽  
Path Planning ◽  
Composite Laminates ◽  
Bending Strength ◽  
High Strength ◽  
Bending Test ◽  
Fiber Placement ◽  
Automated Fiber Placement ◽  
Fiber Path ◽  
Path Curvature

Automated fiber placement (AFP) has been widely used as an advanced manufacturing technology for large and complex composite parts and the trajectory planning of the laying path is the primary task of AFP technology. Proposed in this paper is an experimental study on the effect of several different path planning placements on the mechanical behavior of laminated materials. The prepreg selected for the experiment was high-strength toughened epoxy resin T300 carbon fiber prepreg UH3033-150. The composite laminates with variable angles were prepared by an eight-tow seven-axis linkage laying machine. After the curing process, the composite laminates were conducted by tensile and bending test separately. The test results show that there exists an optimal planning path among these for which the tensile strength of the laminated specimens decreases slightly by only 3.889%, while the bending strength increases greatly by 16.68%. It can be found that for the specific planning path placement, the bending strength of the composite laminates is significantly improved regardless of the little difference in tensile strength, which shows the importance of path planning and this may be used as a guideline for future AFP process.

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

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