On the development of a collaborative robotic system for industrial coating cells

Arrais, Rafael; Costa, Carlos M.; Ribeiro, Paulo F.; Rocha, Luís F.; Silva, Manuel F.; Veiga, Germano

doi:10.1007/s00170-020-06167-z

Cited by 15 publications

(6 citation statements)

References 62 publications

(80 reference statements)

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“…Autonomy can be described as the ability to organise and manage the organisation activities and decisions. Therefore, autonomous robots can be considered intelligent devices that can perceive their environment, perform their own activities, and make decisions with a high degree of autonomy without human interaction (Alcácer and Cruz-Machado 2019 ; Arrais et al 2021 ; Dalmarco et al 2019 ). Autonomous robots will deal with highly repetitive or restricted tasks that workers cannot perform, leaving more complex tasks for workers (Dalmarco et al 2019 ; Moktadir et al 2018 ).…”

Section: Resultsmentioning

confidence: 99%

Industry 4.0: critical investigations and synthesis of key findings

Elnadi

Abdallah

2023

Manag Rev Q

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The concept of Industry 4.0 has been one of the most debated and trending topics over the last few years. Progressively, it has attracted the attention of academicians, practitioners, and policymakers worldwide. However, there needs to be more systematic review of research in the current literature that captures the current state of this new paradigm. This study aims to address this gap by conducting a comprehensive review of Industry 4.0 previous studies to identify its technological, organisational, and managerial enablers, as well as its implementation challenges and benefits. A systematic literature review was conducted, in which 244 peer-reviewed journal papers were analysed in the Scopus database until the end of May 2022. This study excluded conference papers, book chapters, and journal papers not written in English. The study indicated that industry 4.0 is still an immature topic, and applying this new paradigm is not a matter of technology only. Organisational and managerial aspects should be considered. Additionally, the transition towards Industry 4.0 is a complex task, many obstacles exist, and manufacturing companies should be aware of these challenges for successfully implementing this new paradigm. The obtained results in this study synthesise recent studies published on Industry 4.0 and provide a comprehensive picture of Industry 4.0 and potential research directions for future research. Also, this study offers significant guidelines for managers interested in implementing Industry 4.0.

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Section: Resultsmentioning

confidence: 99%

Industry 4.0: critical investigations and synthesis of key findings

Elnadi

Abdallah

2023

Manag Rev Q

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“…Human tracking is a primary input for programming by demonstration systems, using captured data from single or multiple sensors and delivering tracking data even in case of incomplete or corrupted data (Amorim et al 2021). Vision sensing and laser scanners have been combined to classify parts to be painted by robots and capture human expertise while demonstrating the painting process (Arrais et al 2021). Adhesive application can be automated using automatic dispensing machines or robotic arms which are typically manually programmed or using OLP.…”

Section: Related Workmentioning

confidence: 99%

Offline robot programming assisted by task demonstration: an AutomationML interoperable solution for glass adhesive application and welding

Babcinschi,

Cruz,

Duarte

et al. 2024

International Journal of Computer Integrated Manufacturing

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Robots have been successfully deployed in both traditional and novel manufacturing processes. However, they are still difficult to program by non-experts, which limits their accessibility to a wider range of potential users. Programming robots requires expertise in both robotics and the specific manufacturing process in which they are applied. Robot programs created offline often lack parameters that represent relevant manufacturing skills when executing a specific task. These skills encompass aspects like robot orientation and velocity. This paper introduces an intuitive robot programming system designed to capture manufacturing skills from task demonstrations performed by skilled workers. Demonstration data, including orientations and velocities of the working paths, are acquired using a magnetic tracking system fixed to the tools used by the worker. Positional data are extracted from CAD/CAM. Robot path poses are transformed into Cartesian space and validated in simulation, subsequently leading to the generation of robot programs. PathML, an AutomationML-based syntax, integrates robot and manufacturing data across the heterogeneous elements and stages of the manufacturing systems considered. Experiments conducted on the glass adhesive application and welding processes showcased the intuitive nature of the system, with path errors falling within the functional tolerance range.Video 1: glass adhesive application here Video 2: welding here

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“…To address the challenges of inefficiency, unstable quality, and health hazards associated with manual spraying, technological upgrading of the spraying process is imperative. The transformation involves elevating the manual spraying process to an automated spraying production line with robotic operation [5]. This advanced production line seamlessly integrates industrial robotics technology with emerging Internet technologies such as neural networks and fuzzy control.…”

Section: Introductionmentioning

confidence: 99%

Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network

Ling,

Zhang,

et al. 2024

Machines

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In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying processes relies entirely on manual experience. Thus, the crux of the intelligent transformation of spraying robots lies in establishing a mapping model between the spraying process and the resultant spraying quality. To address the challenge of intelligently transforming empirical spraying processes and achieving the mapping from the spraying process to spraying quality, an algorithm employing an enhanced extreme learning machine-based neural network is proposed for predicting spraying process parameters with respect to the evaluation index of spraying quality. In this approach, an algorithmic model based on the Extreme Learning Machine (ELM) neural network is initially constructed utilizing five spraying process parameters: spraying speed, spraying height, spraying width pressure, atomization pressure, and oil spraying pressure. Two spraying quality evaluation indexes, namely average film thickness at the center point and surface roughness, are also incorporated. Subsequently, the prediction neural network is optimized using the K-means improved predator optimization algorithm (KHPO) to enhance the model’s prediction accuracy. This optimization step aims to improve the efficiency of the model in predicting spraying quality based on the specified process parameters. Finally, data collection and model validation for the spraying quality prediction algorithm are conducted using a designed robotic automated waterborne paint spraying experimental system. The experimental results demonstrate a significant reduction in the prediction error of the KHPO-ELM neural network model for the average film thickness center point, showcasing a decrease of 61.95% in comparison to the traditional ELM neural network and 50.81% in comparison to the BP neural network. Likewise, the improved neural network model yields a 2.31% decrease in surface roughness prediction error compared to the traditional ELM neural network and a substantial 54.0% reduction compared to the BP neural network. Consequently, the KHPO-ELM neural network, incorporating the prediction algorithm, effectively facilitates the prediction of multi-spraying process parameters for the center point of average film thickness and surface roughness in automated robot spraying. Notably, the prediction algorithm exhibits a commendable level of accuracy in these predictions.

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On the development of a collaborative robotic system for industrial coating cells

Cited by 15 publications

References 62 publications

Industry 4.0: critical investigations and synthesis of key findings

Industry 4.0: critical investigations and synthesis of key findings

Offline robot programming assisted by task demonstration: an AutomationML interoperable solution for glass adhesive application and welding

Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network

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