Laser metal deposition on-line monitoring via plasma emission spectroscopy and spectral correlation techniques

Plasma spectroscopic techniques focused on the analysis of the plasma background radiation have been studied to enable an efficient on-line monitoring of a laser metal deposition process. The influence of different process parameters and elements, such as laser power, process speed, powder feeding rate and different powder and substrate compositions has been analyzed by means of several experimental trials. The resulting cladding patch analyzes via visual inspection and macrographs have been correlated with their associated spectroscopic monitoring signals. These studies have indicated that on-line quality monitoring of the laser metal deposition process is feasible by means of the proposed solutions, avoiding the identification and use of plasma emission lines. The latter improves the computational performance and avoids, not only the identification of each emission line, but also their specific sensitivity to certain defects. Spectral correlation techniques have also been proposed for monitoring purposes, thus enabling a more quantitative analysisThis work was supported in part by the Project “Noves tecnologies de laser cladding per a processos de conformat” (RD15-1-0098) funded by ACCIO (Generalitat de Catalunya) via FEDER funds. This work was also supported by projects PID2019-107270RB-C21/ AEI / 10.13039/501100011033

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.publishedVersio

선택적 레이저 용융 적층 제조 공정의 용융풀 데이터를 활용한 합성곱 신경망 기반 기공 감지

학위논문(석사) -- 서울대학교대학원 : 공과대학 항공우주공학과, 2022.2. 윤군진.본 학위논문은 선택적 소결 방식의 3D 프린팅 적층공정으로 출력된 대상의 내부 기공을 탐지하는 방법에 관한 논문이다. 금속 적층제조공법은 형상이 복잡한 부품을 전통적인 제조방식 (절삭, 주조 등) 보다 비교적 쉽고 빠르게 제작할 수 있는 장점이 있지만, 연속적으로 분말을 용융-소결시켜 제작하기 때문에 그 과정에서 일어나는 다양한 메커니즘에 의해 결함이 발생할 수 있다. 이러한 이유로 부품의 품질을 보장하기 위해 X-ray를 활용한 비파괴적인 검사 방법이 주로 사용되어왔지만, 비용과 시간이 많이 소요된다는 단점이 있다. 이를 극복하고자 공정 중의 다양한 데이터 (이미지, 음파신호 등)를 인공지능과 결합한 방법들이 시도되었고 끊임없이 연구되고 있다. 본 논문에서는 부가적인 데이터 획득 장치 없이 공정 중 용융풀에서 반사된 광량 신호 데이터를 기반으로 3차원 컨볼루션 신경망 (3D-CNN) 학습을 통해 결함 (lack-of-fusion 및 keyhole 로 인해 발생하는 기공)을 예측하는 방법을 제시한다. 훈련 및 검증을 위한 데이터를 얻기 위해 공정 매개변수인 에너지밀도를 19.84 J/mm^3 에서 110.12 J/mm^3 까지 임의로 설정하여 인공적으로 기공이 형성되는 시편을 제작한다. 제안된 신경망은 공정 중 수집된 3차원화된 광량 신호를 입력으로 받아 작은 크기의 3D moving window로 스캔하여 국부적 검사를 수행하며, micro-CT 결과로 라벨링 된 출력값과 매칭되어 학습된다. 출력값으로 기공의 종류와 국소부피분율을 예측하기 위해 분류 (classification) 와 회귀 (regression) 이 동시에 계산되는 모델이 사용되었다. 훈련된 모델의 성능을 평가하기 위해 기공이 임의로 배치된 테스트용 시편이 제작되었으며, 그 결과 제안된 모델은 lack-of-fusion 및 keyhole 두가지 경우 모두에서 직경이 80 μm 이상인 기공을 최대 78.37%의 진양성률 (true positive ratio) 로 검출할 수 있었다.This thesis is about a method for detecting the internal pores in the additive manufacturing (AM) process, especially selective laser melting (SLM). Metal additive manufacturing has the advantage of producing parts with complex shapes more easily and quickly than traditional manufacturing methods (cutting, casting, etc.). As this gradually expanded, non-destructive inspection methods using X-rays have been mainly used to ensure the quality of parts, but they have the disadvantage of being costly and time-consuming. To overcome such limitations, several methods using various data (images, acoustic signals, etc.) in artificial intelligence have been attempted. In this thesis, defects caused by lack-of-fusion and keyholes pores are predicted through a three-dimensional convolutional neural network (3D-CNN) based on photodiode light intensity data reflected from the melt pool during the process. Specimens with artificial defects are manufactured by arbitrarily setting the energy density from 19.84 J/〖mm〗^3 to 110.12 J/〖mm〗^3, as a process parameter. The proposed network takes the three-dimensional light intensity data collected during the process as an input, scans it with a small 3D moving window performing local inspection, and is trained by matching the output value labeled with the micro-CT results. In order to predict the type of pores and the local volume fraction as output values, a joint model is used which classification and regression are calculated simultaneously. Furthermore, test specimens with random pores are fabricated to evaluate the performance. As a result, the proposed model can detect pores with a diameter over 80 μm with a true positive ratio of up to 78.37% in both lack-of-fusion and keyhole cases.1. Introduction 7 1.1. Motivation 7 2. Backgrounds and related research 9 2.1. Theoretical background 9 2.1.1. Metal additive manufacturing 9 2.1.2. Melt pool monitoring system 11 2.1.3. Computed tomography (CT) analysis 14 2.1.4. Convolutional neural network 16 2.2. Related research 17 2.2.1. Acoustic signal based defect detection 17 2.2.2. Image-based defect detection 18 3. Experimental Setup 21 3.1. Material and Equipment 21 3.1.1. Material 21 3.1.2. Equipment 22 3.2. Design of specimen with pores 23 3.3. Porosity analysis with X-ray microscopes 27 3.4. Melt pool monitoring data preparation 30 3.4.1. Preprocessing of MPM data 31 3.4.2. Training and validation dataset labeling 35 4. Pore Detection Method 38 4.1. 3D-CNN model for pore detection 38 4.2. The decision of hyperparameters for 3D-CNN 42 5. Results and discussion 44 5.1. The pore distribution of specimen 44 5.2. Pore prediction results 50 5.2.1. Test Specimen configuration 50 5.2.2. Evaluation with test dataset of test specimens 53 6. Conclusion 60석

Process Monitoring and Uncertainty Quantification for Laser Powder Bed Fusion Additive Manufacturing

Metal Additive manufacturing (AM) such as Laser Powder-Bed Fusion (LPBF) processes offer new opportunities for building parts with geometries and features that other traditional processes cannot match. At the same time, LPBF imposes new challenges on practitioners. These challenges include high complexity of simulating the AM process, anisotropic mechanical properties, need for new monitoring methods. Part of this Dissertation develops a new method for layerwise anomaly detection during for LPBF. The method uses high-speed thermal imaging to capture melt pool temperature and is composed of a procedure utilizing spatial statistics and machine learning. Another parts of this Dissertation solves problems for efficient use of computer simulation models. Simulation models are vital for accelerated development of LPBF because we can integrate multiple computer simulation models at different scales to optimize the process prior to the part fabrication. This integration of computer models often happens in a hierarchical fashion and final model predicts the behavior of the most important Quantity of Interest (QoI). Once all the models are coupled, a system of models is created for which a formal Uncertainty Quantification (UQ) is needed to calibrate the unknown model parameters and analyze the discrepancy between the models and the real-world in order to identify regions of missing physics. This dissertation presents a framework for UQ of LPBF models with the following features: (1) models have multiple outputs instead of a single output, (2) models are coupled using the input and output variables that they share, and (3) models can have partially unobservable outputs for which no experimental data are present. This work proposes using Gaussian process (GP) and Bayesian networks (BN) as the main tool for handling UQ for a system of computer models with the aforementioned properties. For each of our methodologies, we present a case study of a specific alloy system. Experimental data are captured by additively manufacturing parts and single tracks to evaluate the proposed method. Our results show that the combination of GP and BN is a powerful and flexible tool to answer UQ problems for LPBF

In-situ Monitoring and Defect Detection of Selective Laser Melting Process and Impact of Process Parameters on the Quality of Fabricated SS 316L

Selective Laser Melting (SLM) is an advanced Additive Manufacturing (AM) technique for the 3D printing of metals. SLM process parameters and different types of defects that may appear during the manufacturing process affect the quality of the final product. Setting laser parameters and online defect detection contributes to improving the quality of parts fabricated through SLM technology. In this study, the effect of the process parameters on the properties of the product built by the SLM process was investigated, and an in-situ monitoring platform was developed to detect two types of defects during the SLM process. Different samples were built from stainless steel AISI 316 L powder, utilizing various laser process parameters. Using microscopy imaging technique, the melt structure features of the constructed samples were tested, and the results were analyzed. The dependency of porosity formation on laser process parameters and scan strategy was investigated. Moreover, hardness test was performed for all built samples. The platform developed for in-situ monitoring purposes includes an AM machine equipped with pulsed laser, camera, illumination system, and powerful industrial computer equipped with Cameral Link Adapter, FPGA, and Real-Time (RT) modules. An algorithm was designed using LabVIEW® software based on Particle Analysis (PA) to cease the process in the event of detection of defect in any fused layers. The first defect was caused by changing the laser spot diameter, which altered the energy intensity of the laser on the surface, and the second defect was created by the uneven thickness of powder on the platform. The monitoring system detected both defects and stopped the process immediately according to the designed algorithm. Images were taken from the melting process layer by layer using a high-performance camera.</p

SMART ADDITIVE MANUFACTURING: IN-PROCESS SENSING AND DATA ANALYTICS FOR ONLINE DEFECT DETECTION IN METAL ADDITIVE MANUFACTURING PROCESSES

The goal of this dissertation is to detect the incipient flaws in metal parts made using additive manufacturing processes (3D printing). The key idea is to embed sensors inside a 3D printing machine and conclude whether there are defects in the part as it is being built by analyzing the sensor data using artificial intelligence (machine learning). This is an important area of research, because, despite their revolutionary potential, additive manufacturing processes are yet to find wider acceptance in safety-critical industries, such as aerospace and biomedical, given their propensity to form defects. The presence of defects, such as porosity, can afflict as much as 20% of additive manufactured parts. This poor process consistency necessitates an approach wherein flaws are not only detected but also promptly corrected inside the machine. This dissertation takes the critical step in addressing the first of the above, i.e., detection of flaws using in-process sensor signatures. Accordingly, the objective of this work is to develop and apply a new class of machine learning algorithms motivated from the domain of spectral graph theory to analyze the in-process sensor data, and subsequently, detect the formation of part defects. Defects in additive manufacturing originate due to four main reasons, namely, material, process parameters, part design, and machine kinematics. In this work, the efficacy of the graph theoretic approach is determined to detect defects that occur in all the above four contexts. As an example, in Chapter 4, flaws such as lack-of-fusion porosity due to poor choice of process parameters in additive manufacturing are identified with statistical accuracy exceeding 80%. As a comparison, the accuracy of existing conventional statistical methods is less than 65%. Advisor: Prahalada Ra

Monitoring of hybrid manufacturing using acoustic emission sensor

The approach of hybrid manufacturing addressed in this research uses two manufacturing processes, one process builds a metal part using laser metal deposition, and the other process finishes the part using a milling machining. The ability to produce complete functioning parts in a short time with minimal cost and energy consumption has made hybrid manufacturing popular in many industries for parts repair and rapid prototyping. Monitoring of hybrid manufacturing processes has become popular because it increases the quality and accuracy of the parts produced and reduces both costs and production time. The goal of this work is to monitor the entire hybrid manufacturing process. During the laser metal deposition, the acoustic emission sensor will monitor the defect formation. The acoustic emission sensor will monitor the depth of cut during milling machining. There are three tasks in this study. The first task addresses depth-of-cut detection and tool-workpiece engagement using an acoustic emission monitoring system during milling machining for a deposited material. The second task, defects monitoring system was proposed to detect and classify defects in real time using an acoustic emission (AE) sensor and an unsupervised pattern recognition analysis (K-means clustering) in conjunction with a principal component analysis (PCA). In the third task, a study was conducted to investigate the ability of AE to detect and identify defects during laser metal deposition using a Logistic Regression Model (LR) and an Artificial Neural Network (ANN) --Abstract, page iv

SMART ADDITIVE MANUFACTURING: IN-PROCESS SENSING AND DATA ANALYTICS FOR ONLINE DEFECT DETECTION IN METAL ADDITIVE MANUFACTURING PROCESSES

The goal of this dissertation is to detect the incipient flaws in metal parts made using additive manufacturing processes (3D printing). The key idea is to embed sensors inside a 3D printing machine and conclude whether there are defects in the part as it is being built by analyzing the sensor data using artificial intelligence (machine learning). This is an important area of research, because, despite their revolutionary potential, additive manufacturing processes are yet to find wider acceptance in safety-critical industries, such as aerospace and biomedical, given their propensity to form defects. The presence of defects, such as porosity, can afflict as much as 20% of additive manufactured parts. This poor process consistency necessitates an approach wherein flaws are not only detected but also promptly corrected inside the machine. This dissertation takes the critical step in addressing the first of the above, i.e., detection of flaws using in-process sensor signatures. Accordingly, the objective of this work is to develop and apply a new class of machine learning algorithms motivated from the domain of spectral graph theory to analyze the in-process sensor data, and subsequently, detect the formation of part defects. Defects in additive manufacturing originate due to four main reasons, namely, material, process parameters, part design, and machine kinematics. In this work, the efficacy of the graph theoretic approach is determined to detect defects that occur in all the above four contexts. As an example, in Chapter 4, flaws such as lack-of-fusion porosity due to poor choice of process parameters in additive manufacturing are identified with statistical accuracy exceeding 80%. As a comparison, the accuracy of existing conventional statistical methods is less than 65%. Advisor: Prahalada Ra

DIRECT METAL LASER SINTERING OF TI-6AL-4V ALLOY: PROCESS-PROPERTY-GEOMETRY EMPIRICAL MODELING AND OPTIMIZATION

DIRECT METAL LASER SINTERING OF TI-6AL-4V ALLOY: PROCESS-PROPERTY-GEOMETRY EMPIRICAL MODELING AND OPTIMIZATIO

Additive Manufacturing (AM) of Metallic Alloys

The introduction of metal AM processes in such industrial sectors as the aerospace, automotive, defense, jewelry, medical and tool-making fields, has led to a significant reduction in waste material and in the lead times of the components, innovative designs with higher strength, lower weight, and fewer potential failure points from joining features. This Special Issue on “Additive Manufacturing (AM) of Metallic Alloys” contains a mixture of review articles and original contributions on some problems that limit the wider uptake and exploitation of metals in AM