Review of AI-based methods for chatter detection in machining based on bibliometric analysis

Kounta, Cheick Abdoul Kadir A.; Arnaud, Lionel; Kamsu-Foguem, Bernard; Tangara, Fana

doi:10.1007/s00170-022-10059-9

Cited by 18 publications

(4 citation statements)

References 108 publications

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“…In parallel to these developments, machine learning algorithms have continued to develop and have demonstrated their potential for anomaly detection in CNC machines (Li et al (2020), Sun et al (2019), Kounta et al (2022)). Although these methods are powerful, a limitation of many data driven algorithms is the need for a large set of training data to be collected before they can be implemented.…”

Section: Introductionmentioning

confidence: 99%

Multi-class anomaly classification for CNC milling machines

Li,

Bedi,

Melek

2024

Preprint

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Anomaly detection in CNC milling machine operation is a necessary component to develop a smart, autonomous CNC milling machine. These anomalies range from phenomena such as chatter, to tool breakage, to work piece misalignment. There have been many methods for detecting different anomalies, but many of these methods are focused on classifying one anomaly at a time. In this work, we propose an LSTM-Autoencoder based system to apply dimensionality reduction on accelerometer signals, and then apply Linear Support Vector Classifier (SVC) for multi-class classification and prediction.We extend this work to Autoencoders trained by transfer learning to reduce the data burden and validate both methods experimentally.

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Section: Introductionmentioning

confidence: 99%

Multi-class anomaly classification for CNC milling machines

Li,

Bedi,

Melek

2024

Preprint

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“…These algorithms have been effective for implementing advanced capabilities ranging from tool wear monitoring [8] to cybersecurity of industrial machines [12] [9]. Machine learning techniques have also been implemented effectively for anomaly detection within the manufacturing environment [11] [13] [16] [19] [18] [20], showing their promise as a solution to this problem.…”

Section: Introductionmentioning

confidence: 99%

Incremental Learning of LSTM-AutoEncoder Anomaly Detection in Three-Axis CNC Machines

Li,

Bedi

et al. 2023

Preprint

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There has been a continual effort to develop smarter, more effective CNC machines, capable of fully autonomous operation. To achieve this goal, the machines must be able to automatically detect operational and process anomalies before they cause serious damage. It has been shown that using Artificial Intelligence techniques, such as LSTM-AutoEncoders is an effective method for anomaly detection of issues such as machine chatter. Transfer learning is a valuable tool to decrease the amount of data required to implement this approach, but has lower accuracy than directly training a network on a large dataset. By implementing an incremental-ensemble of weak learners, we have been able to, not only capture changes in system dynamics over time, but incrementally improve the accuracy of a network trained through transfer learning to be comparable to a network directly trained on a large dataset. This allows us to quickly deploy networks on new systems, and obtain highly accurate anomaly estimates

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“…This infrastructure is being enrich by adding specific purpose computing devices, such as Graphic Processing Units (GPU). Their use is specially useful in external processing architectures where computing intensive tasks of specialized fields such as CAD/CAM [5,35] or Artificial Intelligence (AI) [14] are being offloaded to the Cloud, where the use of GPUs provides better performance [13,32], and allows commodity hardware to perform this operations. This brings superior parallel computing capabilities that tackles new computing intensive needs [34].…”

Section: Introductionmentioning

confidence: 99%

Efficient GPU Cloud architectures for outsourcing high-performance processing to the Cloud

Maciá-Lillo

Ribes²,

Mora³

et al. 2022

Preprint

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The world is becoming increasingly dependant in computing intensive appliances. The appearance of new paradigms such as Internet of Things (IoT), and advances in technologies such as Computer Vision (CV) and Artificial Intelligence (AI) is creating a demand for high performance applications. In this regard, Graphics Processing Units (GPUs) have the ability to provide better performance by allowing a high degree of data parallelism. This devices are also beneficial in specialized fields of manufacturing industry such as CAD/CAM. For all this applications, there is a recent tendency to offload this computations to the Cloud, using a computing offloading Cloud architecture. However, the use of GPUs in the Cloud presents some inefficiencies, where GPU virtualization is still not fully resolved, as our research on what main Cloud providers currently offer in terms of GPU Cloud instances shows. To address this problems, this paper first makes a review of current GPU technologies and programming techniques that increase concurrency, to then propose a Cloud computing outsourcing architecture to make more efficient use of this devices in the Cloud.

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Review of AI-based methods for chatter detection in machining based on bibliometric analysis

Cited by 18 publications

References 108 publications

Multi-class anomaly classification for CNC milling machines

Multi-class anomaly classification for CNC milling machines

Incremental Learning of LSTM-AutoEncoder Anomaly Detection in Three-Axis CNC Machines

Efficient GPU Cloud architectures for outsourcing high-performance processing to the Cloud

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