Determination of efficiency indicators of the stand for intelligent
control of manual operations in industrial production
- URL: http://arxiv.org/abs/2401.10777v1
- Date: Fri, 19 Jan 2024 15:51:34 GMT
- Title: Determination of efficiency indicators of the stand for intelligent
control of manual operations in industrial production
- Authors: Anton Sergeev, Victor Minchenkov, Aleksei Soldatov
- Abstract summary: Systems of intelligent control of manual operations in industrial production are being implemented in many industries nowadays.
This paper proposes the methodology for calculating the efficiency indicators.
The results show high precision in tracking the validity of manual assembly and do not depend on the duration of the assembly process.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Systems of intelligent control of manual operations in industrial production
are being implemented in many industries nowadays. Such systems use
high-resolution cameras and computer vision algorithms to automatically track
the operator's manipulations and prevent technological errors in the assembly
process. At the same time compliance with safety regulations in the workspace
is monitored. As a result, the defect rate of manufactured products and the
number of accidents during the manual assembly of any device are decreased.
Before implementing an intelligent control system into a real production it is
necessary to calculate its efficiency. In order to do it experiments on the
stand for manual operations control systems were carried out. This paper
proposes the methodology for calculating the efficiency indicators. This
mathematical approach is based on the IoU calculation of real- and
predicted-time intervals between assembly stages. The results show high
precision in tracking the validity of manual assembly and do not depend on the
duration of the assembly process.
Related papers
- Root Cause Analysis Of Productivity Losses In Manufacturing Systems Utilizing Ensemble Machine Learning [0.0]
This study introduces a data-driven ensemble approach to analyze productivity losses per cycle.
The ensemble approach integrates various methods, including information theory and machine learning behavior models.
The method is validated using a semi-automated welding manufacturing system, an injection molding automation system, and a synthetically generated test PLC dataset.
arXiv Detail & Related papers (2024-07-31T10:21:20Z) - Control and Automation for Industrial Production Storage Zone: Generation of Optimal Route Using Image Processing [49.1574468325115]
This article focuses on developing an industrial automation method for a zone of a production line model using the DIP.
The neo-cascade methodology employed allowed for defining each of the stages in an adequate way, ensuring the inclusion of the relevant methods for its development.
The system was based on the OpenCV library; tool focused on artificial vision, which was implemented on an object-oriented programming (OOP) platform based on Java language.
arXiv Detail & Related papers (2024-03-15T06:50:19Z) - DVQI: A Multi-task, Hardware-integrated Artificial Intelligence System
for Automated Visual Inspection in Electronics Manufacturing [57.33324493991657]
We present the DarwinAI Visual Quality Inspection (DVQI) system for the automated inspection of printed circuit board assembly defects.
The DVQI system enables multi-task inspection via minimal programming and setup for manufacturing engineers.
We also present a case study of the deployed DVQI system's performance and impact for a top electronics manufacturer.
arXiv Detail & Related papers (2023-12-14T18:56:54Z) - Machine Learning Meets Advanced Robotic Manipulation [48.6221343014126]
The paper reviews cutting edge technologies and recent trends on machine learning methods applied to real-world manipulation tasks.
The rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue.
arXiv Detail & Related papers (2023-09-22T01:06:32Z) - Neuro-Symbolic AI for Compliance Checking of Electrical Control Panels [47.187609203210705]
We propose a Neuro-Symbolic approach for automating the compliance verification of the electrical control panels.
Our approach is based on the combination of Deep Learning techniques with Answer Set Programming (ASP) and allows for identifying possible anomalies and errors in the final product.
The experiments conducted on a real test case provided by an Italian Company demonstrate the effectiveness of the proposed approach.
arXiv Detail & Related papers (2023-05-17T10:29:02Z) - An Automated Robotic Arm: A Machine Learning Approach [0.0]
The modern industry is rapidly shifting from manual control of systems to automation.
Computer-based systems, though feasible for improving quality and productivity, are inflexible to work with.
One such task of industrial significance is of picking and placing objects from one place to another.
arXiv Detail & Related papers (2022-01-07T10:33:01Z) - Machine Learning based Indicators to Enhance Process Monitoring by
Pattern Recognition [0.4893345190925177]
We propose a novel framework for machine learning based indicators combining pattern type and intensity.
In a case-study from semiconductor industry, our framework goes beyond conventional process control and achieves high quality experimental results.
arXiv Detail & Related papers (2021-03-24T10:13:20Z) - Runtime Safety Assurance Using Reinforcement Learning [37.61747231296097]
This paper aims to design a meta-controller capable of identifying unsafe situations with high accuracy.
We frame the design of RTSA with the Markov decision process (MDP) and use reinforcement learning (RL) to solve it.
arXiv Detail & Related papers (2020-10-20T20:54:46Z) - Evaluating the Safety of Deep Reinforcement Learning Models using
Semi-Formal Verification [81.32981236437395]
We present a semi-formal verification approach for decision-making tasks based on interval analysis.
Our method obtains comparable results over standard benchmarks with respect to formal verifiers.
Our approach allows to efficiently evaluate safety properties for decision-making models in practical applications.
arXiv Detail & Related papers (2020-10-19T11:18:06Z) - Detection and Classification of Industrial Signal Lights for Factory
Floors [63.48764893706088]
The goal is to develop a solution which can measure the operational state using the input from a video camera capturing a factory floor.
Using methods commonly employed for traffic light recognition in autonomous cars, a system with an accuracy of over 99% in the specified conditions is presented.
arXiv Detail & Related papers (2020-04-23T14:26:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.