Process Parameter Optimization with Numerical modelling and Experimentation design of Binder Jet Additive Manufacturing

Binder jetting technology is an additive manufacturing technology in which powder materials are binded together layer by layer forming the product from input CAD model. The process involves printing the product layer by layer, curing and sintering. The mechanical properties of 3D printed samples varies based on process parameters, hence there is a need to tune the process parameters for optimal characteristics. Three main parameters namely layer thickness, sintering time and sintering temperature were identified and the study focuses on the effect of parameters on dimensional accuracy and compressive strength of the samples. Full factorial experimental approach was used to conduct the experiments and analysis of variance was performed to determine the significance of parameters. Along with parameters optimization, feed forward back propagation artificial neural network model is developed to quantify the relationship between three parameters and compressive strength, the model is developed based on experimental data and validated with known data. Also, Compressive behavior of four lattice designs considered in the study were simulated by finite element analysis and numerical results were compared with experimental data in order to validate the finite element model. FE models of different lattice designs were developed from experimental test data using ANSYS and the simulated compressive behavior is compared to that experimental compression test results

A Framework for Optimizing Process Parameters in Powder Bed Fusion (PBF) Process using Artificial Neural Network (ANN)

Indiana University-Purdue University Indianapolis (IUPUI)Powder bed fusion (PBF) process is a metal additive manufacturing process, which can build parts with any complexity from a wide range of metallic materials. Research in the PBF process predominantly focuses on the impact of a few parameters on the ultimate properties of the printed part. The lack of a systematic approach to optimizing the process parameters for a better performance of given material results in a sub-optimal process limiting the potential of the application. This process needs a comprehensive study of all the influential parameters and their impact on the mechanical and microstructural properties of a fabricated part. Furthermore, there is a need to develop a quantitative system for mapping the material properties and process parameters with the ultimate quality of the fabricated part to achieve improvement in the manufacturing cycle as well as the quality of the final part produced by the PBF process. To address the aforementioned challenges, this research proposes a framework to optimize the process for 316L stainless steel material. This framework characterizes the influence of process parameters on the microstructure and mechanical properties of the fabricated part using a series of experiments. These experiments study the significance of process parameters and their variance as well as study the microstructure and mechanical properties of fabricated parts by conducting tensile, impact, hardness, surface roughness, and densification tests, and ultimately obtain the optimum range of parameters. This would result in a more complete understanding of the correlation between process parameters and part quality. Furthermore, the data acquired from the experiments are employed to develop an intelligent parameter suggestion multi-layer feedforward (FF) backpropagation (BP) artificial neural network (ANN). This network estimates the fabrication time and suggests the parameter setting accordingly to the user/manufacturers desired characteristics of the end-product. Further, research is in progress to evaluate the framework for assemblies and complex part designs and incorporate the results in the network for achieving process repeatability and consistency

Image Segmentation with Human-in-the-loop in Automated De-caking Process for Powder Bed Additive Manufacturing

Additive manufacturing (AM) becomes a critical technology that increases the speed and flexibility of production and reduces the lead time for high-mix, low-volume manufacturing. One of the major bottlenecks in further increasing its productivity lies around its post-processing procedures. This work focuses on tackling a critical and inevitable step in powder-bed additive manufacturing processes, i.e., powder cleaning or de-caking. Pressing concerns can be raised with human involvement when performing this task manually. Therefore, a robot-driven automatic powder cleaning system could be an alternative to reducing time consumption and increasing safety for AM operators. However, since the color and surface texture of the powder residuals and the sintered parts are similar from a computer vision perspective, it can be challenging for robots to plan their cleaning path. This study proposes a machine learning framework incorporating image segmentation and eye tracking to de-cake the parts printed by a powder bed additive manufacturing process. The proposed framework intends to partially incorporate human biological behaviors to increase the performance of an image segmentation algorithm to assist the path planning for the robot de-caking system. The proposed framework is verified and evaluated by comparing it with the state-of-the-art image segmentation algorithms. Case studies were utilized to validate and verify the proposed human-in-the-loop algorithms. With a mean accuracy, f1-score, precision, and IoU score of 81.2%, 82.3%, 85.8%, and 66.9%, respectively, the suggested HITL eye tracking plus segmentation framework produced the best performance out of all the algorithms evaluated and compared. Regarding computational time, the suggested HITL framework matches the running times of the other test existing models, with a mean time of 0.510655 seconds and a standard deviation of 0.008387. Finally, future works and directions are presented and discussed. A significant portion of this work can be found in (Asare-Manu et al., 2023

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

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

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

Determination of Aggregate Elastic Properties of Powder-Beds in Additive Manufacturing Using Convolutional Neural Networks

The most popular strategy for the estimation of effective elastic properties of powder-beds in Additively Manufactured structures (AM structures) is through either the Finite Element Method (FEM) or the Discrete Element Method (DEM). Both of these techniques, however, are computationally expensive for practical applications. This paper presents a novel Convolutional Neural Network (CNN) regression approach to estimate the effective elastic properties of powder-beds in AM structures. In this approach, the time-consuming DEM is used for CNN training purposes and not at run time. The DEM is used to model the interactions of powder particles and to evaluate the macro-level continuum-mechanical state variables (volume average of stress and strain). For the Neural Network training purposes, the DEM code creates a dataset, including hundreds of AM structures with their corresponding mechanical properties. The approach utilizes methods from deep learning to train a CNN capable of reducing the computational time needed to predict the effective elastic properties of the aggregate. The saving in computational time could reach 99.9995% compared to DEM, and on average, the difference in predicted effective elastic properties between the DEM code and trained CNN is less than 4%. The resulting sub-second level computational time can be considered as a step towards the development of a near real-time process control system capable of predicting the effective elastic properties of the aggregate at any given stage of the manufacturing process

Predicting Porosity and Microstructure of 3D Printed Part Using Machine Learning

Additive Manufacturing (AM) is characterized as building a 3-D object one layer at a time. Due to flexibility in design and functionality, additive manufacturing (AM) is an attractive technology for the manufacturing industry. Still, the lack of consistency in quality is one of the main limitations preventing the use of this process to produce end-use products. Current techniques in additive manufacturing face a significant challenge concerning various processing parameters, including scan speed/velocity, laser power, layer thickness, etc. which leads to the inconsistency of the quality of the printed products. Therefore, this research focuses on change, especially on the monitoring and regulation of processes, and helps us predict the level of porosity in a 3D printed part and classify grain growth structure as equiaxed or columnar given the simulation data using state-of-the-art machine learning algorithms. The input parameters considered in this study that affects porosity and grain growth structure are energy density, gas atmosphere, powder particle size and shape, and overlap rate. The data for training machine learning models are collected using ANSYS Additive Manufacturing simulations. The total data collected for porosity prediction is 482 data points, and for the grain growth structure is 12,333 data points. In order to predict the porosity and grain growth structure, a technique based on Artificial Intelligence (Machine learning) is suggested to make the necessary compensations to process monitoring and control, which will subsequently improve the quality of the final product. A feed-forward ANN model is trained in this methodology using an error back-propagation algorithm to predict the porosity level. Also, different classification models such as Support Vector Machines, Meta-classifier classify the microstructure as columnar or equiaxed grains, resulting in part quality improvement. The Backpropagation Neural Network model for porosity prediction gave an accuracy of 100% while outperforming other models. The best results for microstructure prediction are achieved by Meta-classifier, K-Nearest Neighbor, and Random Forest classifier with 100% accuracy. The findings in this study provide evidence and insight that Artificial intelligence and machine learning techniques can be used in the field of Additive Manufacturing for real-time process control and monitoring with the scope of implementation on a larger scale.Master of Science in EngineeringIndustrial and Systems Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/156397/1/Priya Dhage Final Thesis.pdfDescription of Priya Dhage Final Thesis.pdf : Thesi