Layerwise In-Process Quality Monitoring in Laser Powder Bed Fusion

Imani, Farhad; Gaikwad, Aniruddha; Montazeri, Mohammad; Rao, Prahalada; Yang, Hui; Reutzel, Edward W.

doi:10.1115/msec2018-6477

Cited by 41 publications

(13 citation statements)

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“…The labeled dataset was then used to train supervised ML approaches such as support vector machine (SVM) and neural network (NN). Imani et al [86] used ML techniques like SVM, K-NN, and NN to find the process parameters such as hatch distance, laser power, and scanning speed that likely to produce more porous parts. The link between process parameters and the location, size, and frequency of the pores was studied.…”

Section: Supervised Learning Approachmentioning

confidence: 99%

In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing

et al. 2020

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In recent years, technological advancements have led to the industrialization of the laser powder bed fusion process. Despite all of the advancements, quality assurance, reliability, and lack of repeatability of the laser powder bed fusion process still hinder risk-averse industries from adopting it wholeheartedly. The process-induced defects or drifts can have a detrimental effect on the quality of the final part, which could lead to catastrophic failure of the finished part. It led to the development of in situ monitoring systems to effectively monitor the process signatures during printing. Nevertheless, post-processing of the in situ data and defect detection in an automated fashion are major challenges. Nowadays, many studies have been focused on incorporating machine learning approaches to solve this problem and develop a feedback control loop system to monitor the process in real-time. In our study, we review the types of process defects that can be monitored via process signatures captured by in situ sensing devices and recent advancements in the field of data analytics for easy and automated defect detection. We also discuss the working principles of the most common in situ sensing sensors to have a better understanding of the process. Commercially available in situ monitoring devices on laser powder bed fusion systems are also reviewed. This review is inspired by the work of Grasso and Colosimo, which presented an overall review of powder bed fusion technology.

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Section: Supervised Learning Approachmentioning

confidence: 99%

In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing

et al. 2020

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“…Although different methods like x-ray, 3D tomography, ultrasonic, CMOS camera, and infrared technology have been used to measure the quality of AM, in-situ metrology is a more promising way among researchers to monitor a melt pool due to highly irregular nature of a melt pool [16], [17]. The shape, position, and temperature of a melt pool are the main features used to estimate the quality.…”

Section: B Requirements Of Intelligent Metrology In Metal Ammentioning

confidence: 99%

An Intelligent Metrology Architecture With AVM for Metal Additive Manufacturing

Yang

Adnan

Huang

et al. 2019

IEEE Robot. Autom. Lett.

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The capability of measuring melt pool variation is the key evaluating metal additive manufacturing quality. To measure the variation, a metrology architecture with in situ melt pool measurement and an estimation module is required. However, it is a challenge to effectively extract significant features from the huge data collected by the in situ metrology for quality estimation requirement. The purpose of this letter is to propose an intelligent metrology architecture with an in situ metrology (ISM) module and an enhanced automatic virtual metrology (AVM) system. The ISM module can extract the melt pool features with a coaxial camera and a pyrometer. On the other hand, the AVM system is improved with a feature selection method to solve the issue of limited samples in the component modeling quality. The examples with different metals are adopted to illustrate how the system works for estimating surface roughness and density of components, and, in the future, the system can even serve as the feedback signal for adaptive control of the process parameters by layering in an additive manufacturing system. Index Terms-Metal additive manufacturing, intelligent metrology architecture, in-situ metrology, melt pool, AVM. I. INTRODUCTION M ETAL additive manufacturing (AM) recently gains popularity due to its enormous benefits over traditional manufacturing, e.g., the flexibility in design, customized product on demand, and small-scale manufacturing. Metal AM utilizes 3D design to build a component directly, layer-by-layer, using the laser beam to melt the metal powder. Due to process complexity, condition variations, and hardware constraints of the layer Manuscript

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“…This applies to both additive manufacturing and subtractive manufacturing. In subtractive manufacturing simply changing the G-code or M-code to make the spindle or the feed rate work slower not only will cause a longer production but also could potentially change the mechanical properties of the product [37,38]. This could be worsen when metals and alloys are used and could endanger the system where weak or damaged components are used in safety-critical systems, potentially endanger human lives [39,40].…”

Section: Performancementioning

confidence: 99%

Cybersecurity Concerns for Total Productive Maintenance in Smart Manufacturing Systems

Zarreh¹,

Wan²,

Lee³

et al. 2019

Preprint

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Maintenance is the core function to keep a system running and avoid failure. Total Productive Maintenance (TPM) has broadly utilized maintenance strategy to improve the customer's satisfaction and hence obtain a competitive advancement. However, the complexity of smart manufacturing systems due to the recent advancements, specifically the integration of internet and network systems with traditional manufacturing platforms, has made this function more challenging. The focus of this paper is to explain how cybersecurity could impact the TPM by affecting the overall equipment effectiveness (OEE) in a smart manufacturing system by providing a structured literature survey. First, it provides concerns on principle of TPM regarding cybersecurity in smart manufacturing systems. Then, it highlights the effect of a variety of cyber-physical threats on OEE, as a main key performance indicator of TPM and how differently they can reduce OEE. The countermeasures that could be considered to compensate for the negative impact of a cybersecurity threat on the overall effectiveness of the system also will be discussed. Finally, research gaps and challenges are identified to improve overall equipment effectiveness (OEE) in presence of cybersecurity threats in critical manufacturing industries.

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Layerwise In-Process Quality Monitoring in Laser Powder Bed Fusion

Cited by 41 publications

References 0 publications

In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing

In Situ Monitoring Systems of The SLM Process: On the Need to Develop Machine Learning Models for Data Processing

An Intelligent Metrology Architecture With AVM for Metal Additive Manufacturing

Cybersecurity Concerns for Total Productive Maintenance in Smart Manufacturing Systems

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