scholarly journals Robotic Surface Finishing of Curved Surfaces: Real-Time Identification of Surface Profile and Control

2018 ◽  
Author(s):  
Yalun Wen ◽  
Prabhakar R. Pagilla
Keyword(s):  
Real Time ◽  
Force Control ◽  
Surface Profile ◽  
Industrial Robots ◽  
Surface Finishing ◽  
Laser Sensor ◽  
Curved Surfaces ◽  
End Effector ◽  
Real Time Identification ◽  
Finishing Tool

An efficient strategy for robotic surface finishing of curved surfaces that includes real-time identification of the surface profile, control, and implementation is presented in this paper. Real-time identification of the surface profile in the robot base frame is accomplished by employing a proximity laser sensor mounted on the robot end-effector. This surface profile description allows us to generate trajectories for both motion and force control as it provides the surface normal at each point of the surface. Using the surface profile, a trajectory is generated that would orient the surface finishing tool to the local normal of the surface. An algorithm for simultaneous position and force control is developed for surface finishing of curved surfaces. The integrated robotic surface finishing system consists of a UR5 robot and a custom end-effector that includes a force/torque sensor, an electromechanical sander, and the proximity laser sensor. Robot Operating System (ROS) is utilized for real-time implementation, which would enable easy migration of the developed tools if other industrial robots are used. The effectiveness of the strategy is evaluated by conducting a number of experiments for flat and curved surfaces. A representative sample of results on force regulation and surface finishing are presented and discussed.

scholarly journals Conceptual Design of a High-Speed Wire EDM Robotic End-Effector Based on a Systematic Review Followed by TRIZ

Machines ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 132
Author(s):  
Sergio Tadeu Almeida ◽  
John Mo ◽  
Cees Bil ◽  
Songlin Ding ◽  
Xiangzhi Wang
Keyword(s):  
High Speed ◽  
Dimensional Accuracy ◽  
Research Field ◽  
Industrial Robots ◽  
Cnc Machining ◽  
Surface Finishing ◽  
Robot Arm ◽  
Technical Problems ◽  
End Effector ◽  
Robotic Machining

Exotic materials such as titanium offer superior characteristics that, paradoxically, make them hard-to-cut by conventional machining. As a solution, electric discharge machining (EDM) stands out as a non-conventional process able to cut complex profiles from hard-to-cut materials, delivering dimensional accuracy and a superior surface. However, EDM is embodied in CNC machines with a reduced axis and machining envelope, which constrains design freedom in terms of size and shape. To overcome these CNC constraints, traditional machining using six-axis industrial robots have become a prominent research field, and some applications have achieved cost efficiency, an improved envelope, and high flexibility. However, due to the lack of stiffness and strength of the robot arm, accuracy, material rate removal, and surface finishing are not comparable to CNC machining. Therefore, the present study investigates the design of a novel WEDM combined with six-axis robotic machining to overcome the limitations of traditional robotic machining and enhance EDM applications. This study extends the work of a conference paper to confirm potential outcomes, quantifying and ranking undesired interactions to map technical problems and applying the TRIZ approach to trigger solutions. Finally, an effective robotic end-effector design is proposed to free EDM from CNC and deliver robotic machining as a flexible and accurate machining system for exotic materials.

Development of Sensorless Force-Control-Based End-Effector for Automated Robot Polishing

2020 ◽  
Author(s):  
Takuhiro Tsukada ◽  
Shotaro Ogawa ◽  
Katsuki Koto ◽  
Yasuhiro Kakinuma
Keyword(s):  
Force Control ◽  
High Performance ◽  
Force Sensor ◽  
Pressure Control ◽  
Industrial Robots ◽  
End Effector ◽  
Finishing Process ◽  
Polishing Force ◽  
Articulated Robot ◽  
And Control

Abstract As the finishing process in manufacturing a fine mold, manual polishing is typically performed to enhance the surface quality. On the other hand, manual polishing causes increase in costs and health damage to the workers due to sucking polishing dusts. Hence, polishing automation is strongly required by utilizing industrial robots. Regarding robot polishing, highly responsive polishing pressure control is definitely needed so that macro-micro system integrating high-performance end-effector into the articulated robot could be an appropriate approach because response of the robot itself is not sufficiently high. From this viewpoint, the purpose of this study is to develop an end-effector having the ability to simultaneously control polishing force and tool spindle speed. The mechanism and control system of the end-effector are designed and experimentally evaluated. In terms of force control, observer-based force control, which does not require any additional force sensor, is implemented. The experimental results show that the developed end-effector successfully control polishing force with 0.1 N and bandwidth up to 23 Hz.

A Polishing Robot Force Control System Based on Time Series Data in Industrial Internet of Things

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Chen Zhang ◽  
Zhuo Tang ◽  
Kenli Li ◽  
Jianzhong Yang ◽  
Li Yang
Keyword(s):  
Time Series ◽  
Control System ◽  
Force Control ◽  
Time Series Data ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Series Data ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Force Control System

Installing a six-dimensional force/torque sensor on an industrial arm for force feedback is a common robotic force control strategy. However, because of the high price of force/torque sensors and the closedness of an industrial robot control system, this method is not convenient for industrial mass production applications. Various types of data generated by industrial robots during the polishing process can be saved, transmitted, and applied, benefiting from the growth of the industrial internet of things (IIoT). Therefore, we propose a constant force control system that combines an industrial robot control system and industrial robot offline programming software for a polishing robot based on IIoT time series data. The system mainly consists of four parts, which can achieve constant force polishing of industrial robots in mass production. (1) Data collection module. Install a six-dimensional force/torque sensor at a manipulator and collect the robot data (current series data, etc.) and sensor data (force/torque series data). (2) Data analysis module. Establish a relationship model based on variant long short-term memory which we propose between current time series data of the polishing manipulator and data of the force sensor. (3) Data prediction module. A large number of sensorless polishing robots of the same type can utilize that model to predict force time series. (4) Trajectory optimization module. The polishing trajectories can be adjusted according to the prediction sequences. The experiments verified that the relational model we proposed has an accurate prediction, small error, and a manipulator taking advantage of this method has a better polishing effect.

scholarly journals A Kinematic Error Controller for Real-Time Kinematic Error Correction of Industrial Robots

2021 ◽  
Vol 53 ◽  
pp. 705-715
Author(s):  
Mitchell R. Woodside ◽  
Joseph Fischer ◽  
Patrick Bazzoli ◽  
Douglas A. Bristow ◽  
Robert G. Landers
Keyword(s):  
Error Correction ◽  
Real Time ◽  
Industrial Robots ◽  
Kinematic Error ◽  
Real Time Kinematic

scholarly journals Real-Time Identification of Patients Included in the CMS Bundled Payment Care Improvement (BPCI) Program

2020 ◽  
Vol 41 (S1) ◽  
pp. s367-s368
Author(s):  
Michael Korvink ◽  
John Martin ◽  
Michael Long
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Care Providers ◽  
Meta Model ◽  
Bundled Payment ◽  
Clinical Cohort ◽  
Real Time Identification ◽  
Care Improvement

Background: The Bundled Payment Care Improvement Program is a CMS initiative designed to encourage greater collaboration across settings of care, especially as it relates to an initial set of targeted clinical episodes, which include sepsis and pneumonia. As with many CMS incentive programs, performance evaluation is retrospective in nature, resulting in after-the-fact changes in operational processes to improve both efficiency and quality. Although retrospective performance evaluation is informative, care providers would ideally identify a patient’s potential clinical cohort during the index stay and implement care management procedures as necessary to prevent or reduce the severity of the condition. The primary challenges for real-time identification of a patient’s clinical cohort are CMS-targeted cohorts are based on either MS-DRG (grouping of ICD-10 codes) or HCPCS coding—coding that occurs after discharge by clinical abstractors. Additionally, many informative data elements in the EHR lack standardization and no simple and reliable heuristic rules can be employed to meaningfully identify those cohorts without human review. Objective: To share the results of an ensemble statistical model to predict patient risks of sepsis and pneumonia during their hospital (ie, index) stay. Methods: The predictive model uses a combination of Bernoulli Naïve Bayes natural language processing (NLP) classifiers, to reduce text dimensionality into a single probability value, and an eXtreme Gradient Boosting (XGBoost) algorithm as a meta-model to collectively evaluate both standardized clinical elements alongside the NLP-based text probabilities. Results: Bernoulli Naïve Bayes classifiers have proven to perform well on short text strings and allow for highly explanatory unstructured or semistructured text fields (eg, reason for visit, culture results), to be used in a both comparative and generalizable way within the larger XGBoost model. Conclusions: The choice of XGBoost as the meta-model has the benefits of mitigating concerns of nonlinearity among clinical features, reducing potential of overfitting, while allowing missing values to exist within the data. Both the Bayesian classifier and meta-model were trained using a patient-level integrated dataset extracted from both a patient-billing and EHR data warehouse maintained by Premier. The data set, joined by patient admission-date, medical record number, date of birth, and hospital entity code, allows the presence of both the coded clinical cohort (derived from the MS-DRG) and the explanatory features in the EHR to exist within a single patient encounter record. The resulting model produced F1 performance scores of .65 for the sepsis population and .61 for the pneumonia population.Funding: NoneDisclosures: None

scholarly journals Behavioural Study of the Force Control Loop Used in a Collaborative Robot for Sanding Materials

Materials ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 67
Author(s):  
Rodrigo Pérez Ubeda ◽  
Santiago C. Gutiérrez Rubert ◽  
Ranko Zotovic Stanisic ◽  
Ángel Perles Ivars
Keyword(s):  
Force Control ◽  
Feed Rate ◽  
Significant Variation ◽  
Industrial Robots ◽  
Control Loop ◽  
Saturation State ◽  
Collaborative Robots ◽  
Increase Productivity ◽  
Different Characteristics ◽  
Collaborative Robot

The rise of collaborative robots urges the consideration of them for different industrial tasks such as sanding. In this context, the purpose of this article is to demonstrate the feasibility of using collaborative robots in processing operations, such as orbital sanding. For the demonstration, the tools and working conditions have been adjusted to the capacity of the robot. Materials with different characteristics have been selected, such as aluminium, steel, brass, wood, and plastic. An inner/outer control loop strategy has been used, complementing the robot’s motion control with an outer force control loop. After carrying out an explanatory design of experiments, it was observed that it is possible to perform the operation in all materials, without destabilising the control, with a mean force error of 0.32%. Compared with industrial robots, collaborative ones can perform the same sanding task with similar results. An important outcome is that unlike what might be thought, an increase in the applied force does not guarantee a better finish. In fact, an increase in the feed rate does not produce significant variation in the finish—less than 0.02 µm; therefore, the process is in a “saturation state” and it is possible to increase the feed rate to increase productivity.

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