A Machine Vision-based Cyber-Physical Production System for Energy Efficiency and Enhanced Teaching-Learning Using a Learning Factory

Kumar, Rishi; Patil, Omkar; S, Karthik Nath; Sangwan, Kuldip Singh; Kumar, Rajneesh

doi:10.1016/j.procir.2021.01.128

Cited by 19 publications

(4 citation statements)

References 14 publications

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“…Kumar et al deeply studied the kinematics and trajectory interpolation of palletizer, discussed the application of the scattered node interpolation algorithm in robot trajectory planning, and used this algorithm to interpolate the robot path trajectory to make the robot motion trajectory smoother. Trough the simulation and comparison of joint space trajectory planning of the industrial robot through cubic polynomial interpolation and quintic polynomial interpolation, the advantages and disadvantages of the two trajectory planning methods are obtained, which has good reference signifcance for the trajectory planning of the robot [5]. Zhang et al proposed a smooth curve transition algorithm for continuous trajectory planning of industrial robot operation space to improve the motion speed of industrial robots during continuous trajectory transition.…”

Section: Literature Reviewmentioning

confidence: 99%

Industrial Robot Assembly Line Design Using Machine Vision

Xie¹

2023

Journal of Robotics

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In order to further improve the functional requirements and performance indicators of the industrial robot assembly system and more accurately realize the measurement and recognition of the target position of the assembly line by the vision system, this article constructs a robot assembly line system based on obstacle detection and robot arm obstacle path planning based on machine vision technology and further improves the intelligence and accuracy of the assembly line system through the design and optimization of the system software module. Through the experimental verification of the positioning error based on the eye-to-hand binocular vision system and eye-in-hand monocular vision system, the system proposed in this article meets the design accuracy requirements of less than 0.1 mm in x/y direction and less than 1 mm in depth direction and verifies the feasibility and high accuracy of the system.

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Section: Literature Reviewmentioning

confidence: 99%

Industrial Robot Assembly Line Design Using Machine Vision

Xie¹

2023

Journal of Robotics

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“…It can automate VQC and ensure that only defect-free products are delivered. Classification can help in identifying areas for improvement by categorizing defects, reducing waste, and saving energy by identifying defects in early process stages [22]. By inspecting images of machine spare parts, wear can be identified and the spare parts can be replaced to prevent the quality losses of the manufactured products [23].…”

Section: Artificial Intelligence Facilitates Visual Quality Assurancementioning

confidence: 99%

“…The machine vision system proposed in [22] was implemented on the learning factory to monitor the quality of workpieces for defects. This study also offers a solution for enhancing energy and resource losses caused by the scrapping and rework of defective workpieces.…”

Section: Visual Quality Controlmentioning

confidence: 99%

A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing

Hoffmann,

Reich

2023

Electronics

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Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of interest in visual inspection. Next to AI methods, the explainable artificial intelligence (XAI) systems, which achieve transparency and interpretability by providing insights into the decision-making process of the AI, are interesting methods for achieveing quality inspections in manufacturing processes. In this study, we conducted a systematic literature review (SLR) to explore AI and XAI approaches for visual QA (VQA) in manufacturing. Our objective was to assess the current state of the art and identify research gaps in this context. Our findings revealed that AI-based systems predominantly focused on visual quality control (VQC) for defect detection. Research addressing VQA practices, like process optimization, predictive maintenance, or root cause analysis, are more rare. Least often cited are papers that utilize XAI methods. In conclusion, this survey emphasizes the importance and potential of AI and XAI in VQA across various industries. By integrating XAI, organizations can enhance model transparency, interpretability, and trust in AI systems. Overall, leveraging AI and XAI improves VQA practices and decision-making in industries.

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“…A machine vision system in CPS can improve the reliability and robustness of the system. 10 To increase the intelligence and autonomy in the shop floor, some industrial tasks were identified as cost and time consuming and require frequent human interventions. First is the task of checking the status, availability, compliance, and readiness of the production lines prior to the start of production, which is commonly known as the industrial inspection process.…”

Section: Introductionmentioning

confidence: 99%

A Modular Smart Vision System for Industrial Inspection and Control of Conformity

Helou

Benfriha

Al‐Ahmari

et al. 2022

Smart and Sustainable Manufacturing Systems

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In manufacturing systems, there are several essential tasks to perform before and after the production process. In traditional systems, these tasks were done manually, which can lead to more resource consumption and the risk of human error; however, advanced manufacturing systems and Industry 4.0 tend toward a more autonomous manner. To ensure the compliance of the machining process and the safety of the personnel as well as the machines at the shop floor, the inspection of the overall factory prior to any machining process and the control of conformity of the manufactured parts are necessary in order to know the status of the manufacturing line. This paper proposes a novel modular smart vision system for machine inspection and conformity control of machined parts. Our system uses smart vision technologies embedded in industrial robots and enhanced with image processing and analysis capabilities.The solution also integrates a user interface for human-machine interactions that has been developed with a modular approach, and is designed, launched, and controlled by the manufacturing execution system, allowing agile and customized configuration. By this new approach, the robot inspects all the machines in the factory to check the status before launching the production plan. After the machining process, the system interprets the in situ dimensional analysis for the machined parts and makes decisions about whether the parts are acceptable or require additional machining.

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A Machine Vision-based Cyber-Physical Production System for Energy Efficiency and Enhanced Teaching-Learning Using a Learning Factory

Cited by 19 publications

References 14 publications

Industrial Robot Assembly Line Design Using Machine Vision

Industrial Robot Assembly Line Design Using Machine Vision

A Systematic Literature Review on Artificial Intelligence and Explainable Artificial Intelligence for Visual Quality Assurance in Manufacturing

A Modular Smart Vision System for Industrial Inspection and Control of Conformity

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