Industry 4.0 in Welding

Mishra, Debasish; Pal, Surjya K.; Chakravarty, Debashish

doi:10.1007/978-3-030-63986-0_8

Cited by 12 publications

(6 citation statements)

References 90 publications

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“…Experts write down each one of their trials into a procedure qualification record (PQR). Once satisfactory and meeting the quality criteria, the PQR can be used to generate a welding procedure specification (WPS) which summarize how to replicate the expert's weld [1]. Therefore, information such as materials' references, physical treatments, temperature bounds or the width of the weld are written on it.…”

Section: Background On Gas Metal Arc Welding and Welding Procedures C...mentioning

confidence: 99%

“…There exists a known relationship between welding settings and the weld bead shape or quality which is now widely used in industry [1]. Based on this relationship, the welding parameters can be identified with regression models [2] and neural networks [3] or optimized for an expected weld bead shape [4].…”

Section: Introductionmentioning

confidence: 99%

“…We propose a new method using supervised learning to recommend (1) the number of passes to complete the weld and (2) the values for the seven parameters (layer, amperage, voltage, wire feed rate, welding speed, frequency offset and trimming) for each pass. We created a dataset by extracting data from 630 WPS filled by experts, then we compared 11 classification algorithms to train models to identify the number of passes, and 11 regression algorithms to train models to recommend the parameters for each pass.…”

Section: Introductionmentioning

confidence: 99%

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Supervised recommendations of gas metal arc welding parameters

Picherit,

Lampron,

Morin

et al. 2023

Proceedings of the Canadian Conference on Artificial Intelligence

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In gas metal arc welding, a weld quality and performance depends on many parameters. Selecting the right ones can be complex, even for an expert. One generally proceeds through trial and error to find a good set of parameters. Therefore, the current experts' method is not optimized and can require a lot of time and materials. We propose using supervised learning techniques to help experts in their decision-making. To that extent, a two-part recommendation system is proposed. The first step is dedicated to identify, through classification, the number of weld passes. The second one suggests the seven remaining parameter values for each pass: layer, amperage, voltage, wire feed rate, frequency offset, trimming and welding speed. After extracting data from historical Welding Procedure Specification forms, we tested 11 different supervised learning algorithms. The recommendation system is able to provide good results for all the different settings mentioned above even if the data is noisy due to the heuristic nature of the experts' process. The best classification model is CatBoost with 82.22% average F1-Weighted-Score and the best regression models are Extra Trees or a boosting algorithm with a reduced mean absolute percentage error compared to our baseline.

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Section: Background On Gas Metal Arc Welding and Welding Procedures C...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Supervised recommendations of gas metal arc welding parameters

Picherit,

Lampron,

Morin

et al. 2023

Proceedings of the Canadian Conference on Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…Conventionally, weld quality is determined by post-weld tests. These tests may be destructive or nondestructive, however, are time and capitalintensive, and are performed offline [3]. Instead, a network of sensors can be engaged for indirect monitoring, thereby creating a closed-loop feedback system from the sensory data [4].…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Mishra

Priyadarshi

Das

et al. 2022

JDMD

Self Cite

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Modern manufacturing aims to reduce downtime and track process anomalies to make profitable business decisions. This ideology is strengthened by Industry 4.0, which aims to continuously monitor high-value manufacturing assets. This article builds upon the Industry 4.0-concept to improve the efficiency of manufacturing systems. The major contribution is a framework for continuous monitoring and feedback-based control in the friction stir welding (FSW) process. It consists of a CNC manufacturing machine, sensors, edge, cloud systems, and deep neural networks, all working cohesively in real-time. The edge device, located near the FSW machine, consists of a neural network that receives sensory information and predicts weld quality in real-time. It addresses time-critical manufacturing decisions. Cloud receives the sensory data if weld quality is poor, and a second neural network predicts the new set of welding parameters that are sent as feedback to the welding machine. Several experiments are conducted for training the neural networks. The framework successfully tracks process quality and improves the welding by controlling it in real-time. The system enables faster monitoring and control achieved in less than 1 second. The framework is validated through several experiments.

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“…This is fulfilled by using sensors for acquiring data, and subsequent data analysis for continuous improvement [2]. Recently, 'Industry 4.0', a new concept has been introduced that embodies Internet of Things (IoT) and Cyber-physical Systems (CPS), which resembles the connection of physical and digital worlds [3,4]. A lot of hype can be seen, where the importance of these concepts is being discussed in manufacturing.…”

Section: Introductionmentioning

confidence: 99%

Sensor based real-time information for monitoring and control of a manufacturing process

et al. 2021

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This paper demonstrates a real-time process monitoring and control framework for the implementation of Industry 4.0 in manufacturing. For a case study, friction stir welding (FSW) has been selected owing to its extensive usage of welding in various manufacturing sectors. Force data has been collected online during the welding and sent to a cloud server enabling telewelding. It is processed through several signal processing and machine learning (ML) techniques for assessment of the weld quality. A control system thereafter controls the weld quality for avoiding the occurrence of weld-defect in realtime. Two ML models have been built, one for assessment of the weld quality, and the other for forecasting the controlled parameters for avoiding defect occurrence. For manufacturing sectors involved in mass production, this technique will be useful to keep a track of the process in real-time and avoid rejection of material in case of occurrence of an anomaly in the process. It is always beneficial for the manufacturing industry to track the quality of a product in real-time, and control the rejection rate, both of which have been successfully shown in this paper.

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Industry 4.0 in Welding

Cited by 12 publications

References 90 publications

Supervised recommendations of gas metal arc welding parameters

Supervised recommendations of gas metal arc welding parameters

Industry 4.0 Application in Manufacturing for Real-Time Monitoring and Control

Sensor based real-time information for monitoring and control of a manufacturing process

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