Quality Monitoring and Control in Additive Manufacturing

Colosimo, Bianca Maria

doi:10.1002/9781118445112.stat08241

Cited by 2 publications

(2 citation statements)

References 47 publications

(32 reference statements)

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“…A c c e p t e d M a n u s c r i p t efficient solutions to handle very big and fast datasets. Typical problems often encountered in industrial applications of statistical quality monitoring and control (also referred to as statistical process control -SPC) characterize the practical implementation of many approaches developed for in-situ monitoring of AM , Colosimo 2018, Colosimo 2020: the need for large training data sets, which implies similar shapes printed along all the layers and repeated production of similar jobs, together with the need of solutions for big data mining (in terms of volume, velocity and variety) are all barriers to the use of the developed approaches in real industrial settings.…”

Section: Open Issues and Future Research Directionsmentioning

confidence: 99%

In-situ measurement and monitoring methods for metal powder bed fusion: an updated review

Grasso

Remani

Dickins

et al. 2021

Meas. Sci. Technol.

126

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The possibility of using a variety of sensor signals acquired during metal powder bed fusion processes, to support part and process qualification and for the early detection of anomalies and defects, has been continuously attracting an increasing interest. The number of research studies in this field has been characterised by significant growth in the last few years, with several advances and new solutions compared with first seminal works. Moreover, industrial powder bed fusion systems are increasingly equipped with sensors and toolkits for data collection, visualisation and, in some cases, embedded in-process analysis. Many new methods have been proposed and defect detection capabilities have been demonstrated. Nevertheless, several challenges and open issues still need to be tackled to bridge the gap between methods proposed in the literature and actual industrial implementation. This paper presents an updated review of the literature on in-situ sensing, measurement and monitoring for metal powder bed fusion processes, with a classification of methods and a comparison of enabled performances. The study summarises the types and sizes of defects that are practically detectable while the part is being produced and the research areas where additional technological advances are currently needed.

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Section: Open Issues and Future Research Directionsmentioning

confidence: 99%

In-situ measurement and monitoring methods for metal powder bed fusion: an updated review

Grasso

Remani

Dickins

et al. 2021

Meas. Sci. Technol.

126

View full text Add to dashboard Cite

show abstract

“…Indeed, AM is one of the pillars of the Industry 4.0 paradigm where the "four Vs" commonly used to describe big data, i.e., volume, velocity, variety and veracity, well fit the large and complex datasets generated on a daily basis by end-users, machine builders and researchers. The layerwise production paradigm enables the collection of a large variety of signals on a layer-by-layer basis, during the entire AM process (Grasso et al, 2021;Colosimo et al, 2018;Colosimo, 2020). These signals range from high resolution images to high-speed videos, from surface topography reconstructions to thermographic measurements, etc.…”

Section: Introductionmentioning

confidence: 99%

Open data for open science in Industry 4.0: In-situ monitoring of quality in additive manufacturing

Gronle

Grasso

Granito

et al. 2022

Journal of Quality Technology

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Open science has the capacity of boosting innovative solutions and knowledge development thanks to a transparent access to data shared within the research community and collaborative networks. Because of this, it has become a policy priority in various research and development strategy plans and roadmaps, but the awareness if its potential is still limited in industry. Additive manufacturing (AM) represents a field where open science initiatives may have a great impact, as large academic and industrial communities are working in the same area, enormous quantities of data are generated on a daily basis by companies and research centres, and many challenging problems still need to be solved. This paper presents a case study based on an open science collaboration project between TRUMPF Laser-und Systemtechnik GmbH, one of the major AM systems developers and Politecnico di Milano. The case study relies on an open dataset including in-line and in-situ signals gathered during the laser powder bed fusion of specimens of aluminium parts on an industrial machine. The signals were acquired by means of two photodiodes installed co-axially to the laser path. The specimens were designed to introduce, on purpose, anomalies in certain locations and in certain layers. The dataset is specifically designed to support the development of novel in-situ monitoring methodologies for fast and robust anomaly detection while the part is being built. A layerwise statistical monitoring approach is proposed and preliminary results are presented, but the problem is open to additional research and to the exploration of novel solutions.

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Quality Monitoring and Control in Additive Manufacturing

Cited by 2 publications

References 47 publications

In-situ measurement and monitoring methods for metal powder bed fusion: an updated review

In-situ measurement and monitoring methods for metal powder bed fusion: an updated review

Open data for open science in Industry 4.0: In-situ monitoring of quality in additive manufacturing

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