Using a Support Vector Machine for building a Quality Prediction Model for Center-less Honing process

Gejji, Abha; Shukla, Shruti; Pimparkar, Siddhee; Pattharwala, Tamanna; Bewoor, Anand K.

doi:10.1016/j.promfg.2020.03.086

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Ahmad et al [61] showed that SVM works accurately to forecast electrical energy consumption with the help of past datasets. Gejji et al [62] also compared the SVM for quality prediction model of honing process concerning neural networks, logical regression, and decision trees. Razvi et al [6] mentioned the application of SVM in different additive manufacturing domains as regression and classification to predict the defects, melt pool analysis, and parameter optimization.…”

Section: Classifiermentioning

confidence: 99%

Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques

et al. 2022

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Machine learning (ML) is one of the artificial intelligence tools which uses past data to learn the relationship between input and output and helps to predict future trends. Powder bed fusion additive manufacturing (PBF-AM) is extensively used for a wide range of applications in the industry. The AM process establishment for new material is a crucial task with trial-and-error approaches. In this work, ML techniques have been applied for the prediction of the density of PBF-AM. Density is the most vital property in evaluating the overall quality of the AM building part. The ML techniques, namely, artificial neural network (ANN), K-nearest neighbor (KNN), support vector machine (SVM), and linear regression (LR), are used to develop a model for predicting the density of the stainless steel (SS) 316L build part. These four methods are validated using R-squared values and different error functions to compare the predicted result. The ANN and SVM model performed well with the R-square value of 0.95 and 0.923, respectively, for the density prediction. The ML models would be beneficial for the prediction of the process parameters. Further, the developed ML model would also be helpful for the future application of ML in additive manufacturing.

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Section: Classifiermentioning

confidence: 99%

Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques

et al. 2022

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Security strategies for AI systems in Industry 4.0

Bairaktaris,

Johannssen,

Tran

2024

Quality & Reliability Eng

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The recent emergence and widespread adoption of Artificial Intelligence (AI) across various industries have not only increased efficiency and innovation but also introduced complex security challenges. Addressing these security challenges is an ever‐evolving area of research that seeks to mitigate issues affecting a wide range of individuals and sectors. Understanding the threats and vulnerabilities posed by these systems, and how to effectively defend against them, has become more crucial than ever. This paper aims to provide an overview of the most common attack vectors and their respective defense strategies, focusing particularly on those relevant to Industry 4.0. A major area of interest is adversarial machine learning, a relatively new field that focuses on corrupting, confusing, and manipulating AI models by intervening in different phases of their life cycle. The key findings indicate that FLAME offers the best protection against data poisoning attacks for neural network image detection models in a federated learning environment. Additionally, due to the specific threat model in Industry 4.0, defensive distillation emerges as the most promising defense strategy against evasion attacks.

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Machine learning and deep learning based predictive quality in manufacturing: a systematic review

2022

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With the ongoing digitization of the manufacturing industry and the ability to bring together data from manufacturing processes and quality measurements, there is enormous potential to use machine learning and deep learning techniques for quality assurance. In this context, predictive quality enables manufacturing companies to make data-driven estimations about the product quality based on process data. In the current state of research, numerous approaches to predictive quality exist in a wide variety of use cases and domains. Their applications range from quality predictions during production using sensor data to automated quality inspection in the field based on measurement data. However, there is currently a lack of an overall view of where predictive quality research stands as a whole, what approaches are currently being investigated, and what challenges currently exist. This paper addresses these issues by conducting a comprehensive and systematic review of scientific publications between 2012 and 2021 dealing with predictive quality in manufacturing. The publications are categorized according to the manufacturing processes they address as well as the data bases and machine learning models they use. In this process, key insights into the scope of this field are collected along with gaps and similarities in the solution approaches. Finally, open challenges for predictive quality are derived from the results and an outlook on future research directions to solve them is provided.

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Using a Support Vector Machine for building a Quality Prediction Model for Center-less Honing process

Cited by 5 publications

References 13 publications

Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques

Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques

Security strategies for AI systems in Industry 4.0

Machine learning and deep learning based predictive quality in manufacturing: a systematic review

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