Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning

Xia, Chunyang; Pan, Zengxi; Polden, Joseph; Li, Huijun; Xu, Yanling; Chen, Shanben

doi:10.1007/s10845-020-01725-4

Cited by 114 publications

(65 citation statements)

References 27 publications

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“…These findings were reinforced by the model's prediction accuracy of 99%. Similar to material extrusion, DED produces parts with poor surface roughness [40]. Xia et al [40], used an NN to model and predict surface roughness based on overlap ratio, welding speed, and wire feed speed with a root mean square error of 6.94%.…”

Section: Parameter Optimisationmentioning

confidence: 99%

“…Similar to material extrusion, DED produces parts with poor surface roughness [40]. Xia et al [40], used an NN to model and predict surface roughness based on overlap ratio, welding speed, and wire feed speed with a root mean square error of 6.94%. A small training set was identified as a major limiter on the model's accuracy [40].…”

Section: Parameter Optimisationmentioning

confidence: 99%

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Machine Learning for Additive Manufacturing

2021

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Additive manufacturing (AM) is the name given to a family of manufacturing processes where materials are joined to make parts from 3D modelling data, generally in a layer-upon-layer manner. AM is rapidly increasing in industrial adoption for the manufacture of end-use parts, which is therefore pushing for the maturation of design, process, and production techniques. Machine learning (ML) is a branch of artificial intelligence concerned with training programs to self-improve and has applications in a wide range of areas, such as computer vision, prediction, and information retrieval. Many of the problems facing AM can be categorised into one or more of these application areas. Studies have shown ML techniques to be effective in improving AM design, process, and production but there are limited industrial case studies to support further development of these techniques.

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Section: Parameter Optimisationmentioning

confidence: 99%

Section: Parameter Optimisationmentioning

confidence: 99%

Machine Learning for Additive Manufacturing

2021

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“…Advanced DA, such as Artificial Intelligence (AI) and ML, can effectively use AM big data to produce actionable intelligence and new knowledge for decision-makers. Advanced DA has successfully been applied to derive the relationships between (1) process parameters and creep rates (Sanchez et al, 2021), (2) process parameters and surface roughness (Xia et al, 2021), and (3) part geometry and printability (Mycroft et al, 2020). It has also been used to monitor layer defects and melt pool conditions in real time by analyzing temperature data (Mahato et al, 2020), acoustic signals (Ye et al, 2018), optical images (Davtalab et al, 2020;Kwon et al, 2020), andvideo-imaging data (Bugatti &Colosimo, 2021).…”

Section: Data-driven Additive Manufacturingmentioning

confidence: 99%

Collaborative knowledge management to identify data analytics opportunities in additive manufacturing

Park

Lee

et al. 2021

J Intell Manuf

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Additive Manufacturing (AM) is becoming data-intensive. The ability to identify Data Analytics (DA) opportunities for effective use of AM data becomes a critical factor in the success of AM. To successfully identify high-potential DA opportunities in AM requires a set of distinctive interdisciplinary knowledge. This paper proposes a methodology that enables collaborative knowledge management for identifying and prioritizing DA opportunities in AM. The framework of the proposed methodology has three components: a team of experts, a DA Opportunity Knowledge Base (DOKB), and a prioritization tool. The team of experts provides diverse knowledge that can be used to identify and prioritize DA opportunities. The DOKB, developed by using the Web Ontology Language (OWL), captures diverse knowledge from the experts to identify DA opportunities. The prioritization tool ranks the identified DA opportunities by using the Fuzzy integrated Technique of Order Preference Similarity to the Ideal Solution (Fuzzy-TOPSIS). A case study, in which National Institute of Standards and Technology (NIST) researchers participated, demonstrates our methodology. As a result, 264 DA opportunities for AM's Laser-Powder Bed Fusion (L-PBF) process are identified and prioritized. The prioritized DA opportunities help set a DA direction for L-PBF AM. Our methodology keeps knowledge sharable, reusable, revisable, and extendable. Thus, this methodology can continue to facilitate collaboration within the AM community to identify high potential and high impact DA opportunities in AM.

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“…Furthermore, the tool's path can be adjusted based on the obtained model and can be utilized when generating the robot code. Thus, if a predictive model can be implemented properly, it will lead to products of higher quality and productivity increases [7]. Methodologies for solving the issue of predicting layer geometry during the deposition process are divided into two approaches.…”

Section: Introductionmentioning

confidence: 99%

Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

et al. 2021

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In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using the layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in the final product, proper implementation of the bead geometry is essential. For this reason, the paper focuses on this process and proposes a layer geometry (width and height) prediction model to improve deposition accuracy. More specifically, a machine learning regression algorithm is applied on several experimental data to predict the bead geometry across layers. Furthermore, a neural network-based approach was used to study the influence of different deposition parameters, namely laser power, wire-feed rate and travel speed on bead geometry. To validate the effectiveness of the proposed approach, a test split validation strategy was applied to train and validate the machine learning models. The results show a particular evolutionary trend and confirm that the process parameters have a direct influence on the bead geometry, and so, too, on the final part. Several deposition parameters have been found to obtain an accurate prediction model with low errors and good layer deposition. Finally, this study indicates that the machine learning approach can efficiently be used to predict the bead geometry and could help later in designing a proper controller in the LWAM process.

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Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning

Cited by 114 publications

References 27 publications

Machine Learning for Additive Manufacturing

Machine Learning for Additive Manufacturing

Collaborative knowledge management to identify data analytics opportunities in additive manufacturing

Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study

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