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Applications of Artificial Intelligence in Additive Manufacturing最新文献

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Regression and Artificial Intelligence Models to Predict the Surface Roughness in Additive Manufacturing 回归和人工智能模型预测增材制造中的表面粗糙度
Applications of Artificial Intelligence in Additive Manufacturing Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8516-0.ch003
Mohamed Hamoud Ahmed, A. Barakat, Abuubakr Ibrahim Abdelwahab
{"title":"Regression and Artificial Intelligence Models to Predict the Surface Roughness in Additive Manufacturing","authors":"Mohamed Hamoud Ahmed, A. Barakat, Abuubakr Ibrahim Abdelwahab","doi":"10.4018/978-1-7998-8516-0.ch003","DOIUrl":"https://doi.org/10.4018/978-1-7998-8516-0.ch003","url":null,"abstract":"In additive manufacturing (AM), it is necessary to study the surface roughness, which affected the building parameters such as layer thickness and building orientation. Some AM machines have minimum layer thickness that doesn't satisfy the desired roughness. Also, it produces a fine surface that isn't required. This increases the building time and cost without any benefits. To overcome these problems and achieve a certain surface roughness, a prediction model is proposed in this chapter. Regression models were used to predict the surface roughness through the building orientation. ANN was used to predict the surface roughness through the building orientation and the layer thickness together. ANN was constructed based on experimental work that study the effect of layer thickness and building orientation on the surface roughness. Some data were used in the training process and others were used in the verification process. The results show that the layer thickness parameter has an effect more than the building orientation parameter on the surface roughness.","PeriodicalId":215367,"journal":{"name":"Applications of Artificial Intelligence in Additive Manufacturing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126066849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Online Detection and Prediction of Fused Deposition Modelled Parts Using Artificial Intelligence 基于人工智能的熔融沉积模型零件在线检测与预测
Applications of Artificial Intelligence in Additive Manufacturing Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8516-0.ch009
S. Salunkhe, G. Kanagachidambaresan, C. Rajkumar, K. Jayanthi
{"title":"Online Detection and Prediction of Fused Deposition Modelled Parts Using Artificial Intelligence","authors":"S. Salunkhe, G. Kanagachidambaresan, C. Rajkumar, K. Jayanthi","doi":"10.4018/978-1-7998-8516-0.ch009","DOIUrl":"https://doi.org/10.4018/978-1-7998-8516-0.ch009","url":null,"abstract":"Fused deposition modelling (FDM) is a technology used for filament deposition of heated plastic filaments by a given pattern by the melted extrusion process. Delamination is a critical issue of FDM's incredibly complex parts. In this chapter, the artificial intelligence (machine learning) model is used for online detections and prediction of FDM parts. The proposed machine learning and convolutional neural network model is capable of online detect delamination of FDM parts. The proposed model can also be applied for different types of additive manufacturing materials with less human interaction.","PeriodicalId":215367,"journal":{"name":"Applications of Artificial Intelligence in Additive Manufacturing","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133495082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Experimental Investigations and Multi-Objective Optimization of Selective Inhibition Sintering Process Using the Dragonfly Algorithm 基于蜻蜓算法的选择性抑制烧结工艺实验研究及多目标优化
Applications of Artificial Intelligence in Additive Manufacturing Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8516-0.ch005
S. M, Rajamani D., Balsubramanian E.
{"title":"Experimental Investigations and Multi-Objective Optimization of Selective Inhibition Sintering Process Using the Dragonfly Algorithm","authors":"S. M, Rajamani D., Balsubramanian E.","doi":"10.4018/978-1-7998-8516-0.ch005","DOIUrl":"https://doi.org/10.4018/978-1-7998-8516-0.ch005","url":null,"abstract":"The chapter focuses on utilizing a hybrid approach of response surface methodology and dragonfly algorithm for investigations and optimization of the selective inhibition sintering (SIS) process to improve the mechanical strengths such as tensile and flexural of fabricated high density polyethylene parts. The layer thickness (LT), heater energy (HE), heater and printer feedrate (HFR & PFR) are considered as the independent variables for the investigation. The SIS experiments are planned and conducted through a response surface methodology-based box-Behnken design approach to fabricate the test specimens. The optimal SIS parameters are obtained through a swarm intelligence metaheuristic technique namely dragonfly algorithm (DFA). The optimal parameter settings of LT of 0.102 mm, HE of 28.46 J/mm2, HFR of 3.22 mm/sec, and PFR of 110.49 mm/min are achieved through DFA for improved tensile and flexural strengths of 26.21 MPa and 65.71 MPa, respectively. Further, the prediction ability of DFA was compared with particle swarm optimization algorithm.","PeriodicalId":215367,"journal":{"name":"Applications of Artificial Intelligence in Additive Manufacturing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131058427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Application of Machine Learning Techniques in Additive Manufacturing: A Review 机器学习技术在增材制造中的应用综述
Applications of Artificial Intelligence in Additive Manufacturing Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8516-0.ch001
A. Gajakosh, R. Suresh Kumar, V. Mohanavel, R. Shanmugam, M. Ramoni
{"title":"Application of Machine Learning Techniques in Additive Manufacturing: A Review","authors":"A. Gajakosh, R. Suresh Kumar, V. Mohanavel, R. Shanmugam, M. Ramoni","doi":"10.4018/978-1-7998-8516-0.ch001","DOIUrl":"https://doi.org/10.4018/978-1-7998-8516-0.ch001","url":null,"abstract":"This chapter provides an analysis of the state-of-the-art in ML applications for optimizing the additive manufacturing process. This chapter primarily presents a review of the literature on the use of machine learning (ML) in optimizing the additive manufacturing process at various stages. The chapter identifies ML-researched areas in which ML can be used to optimize processes such as process design, process plan and control, process monitoring, quality enhancement of additively manufactured products, and so on. In addition, general literature on the intersection of additive manufacturing and machine learning will be presented. The benefits and drawbacks of ML for additive manufacturing will be discussed, as well as existing obstacles that are currently limiting applications.","PeriodicalId":215367,"journal":{"name":"Applications of Artificial Intelligence in Additive Manufacturing","volume":"87 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131124549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Process Optimizations of Direct Metal Laser Melting Using Digital Twin 基于数字孪生的直接金属激光熔化工艺优化
Applications of Artificial Intelligence in Additive Manufacturing Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8516-0.ch008
S. Salunkhe, V. Naranje, Jayavelu S., A. Rehman
{"title":"Process Optimizations of Direct Metal Laser Melting Using Digital Twin","authors":"S. Salunkhe, V. Naranje, Jayavelu S., A. Rehman","doi":"10.4018/978-1-7998-8516-0.ch008","DOIUrl":"https://doi.org/10.4018/978-1-7998-8516-0.ch008","url":null,"abstract":"Over the past decades, air traffic has increased to such an extent that it has highly impacted (anthropogenic) climate change due to heat, noise, particulates, and gas emissions. With airplane turbines being a pivotal contributor to such adverse developments, there has been an increasing interest in research regarding the optimization of airplane turbines. In line with these efforts, this chapter adopts an innovative approach that bridges the digital and physical through the application of digital twin technology to direct metal laser melting to optimize product development. Specifically, it encompasses a guideline towards how digital twin solutions are created based on all the latest research. A manual approach devises a digital twin interface where the prototype is manufactured used additive manufacturing. This manual can then be applied to optimize airplane turbines regarding their safety, environmental impact, fuel efficiency, and cost.","PeriodicalId":215367,"journal":{"name":"Applications of Artificial Intelligence in Additive Manufacturing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127981945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Optimized Robotic WAAM 优化的机器人WAAM
Applications of Artificial Intelligence in Additive Manufacturing Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8516-0.ch006
Aya A. A. Ramadan, S. Elatriby, Abd El Ghany, A. Barakat
{"title":"Optimized Robotic WAAM","authors":"Aya A. A. Ramadan, S. Elatriby, Abd El Ghany, A. Barakat","doi":"10.4018/978-1-7998-8516-0.ch006","DOIUrl":"https://doi.org/10.4018/978-1-7998-8516-0.ch006","url":null,"abstract":"This chapter summarizes a PhD thesis introducing a methodology for optimizing robotic MIG (metal inert gas) to perform WAAM (wire and arc additive manufacturing) without using machines equipped with CMT (cold metal transfer) technology. It tries to find the optimal MIG parameters to make WAAM using a welding robot feasible production technique capable of making functional products with proper mechanical properties. Some experiments were performed first to collect data. Then NN (neural network) models were created to simulate the MIG process. Then different optimization techniques were used to find the optimal parameters to be used for deposition. These results were practically tested, and the best one was selected to be used in the third stage. In the third stage, a block of metal was deposited. Then samples were cut from deposited blocks in two directions and tested for tension stress. These samples were successful. They showed behavior close to base alloy.","PeriodicalId":215367,"journal":{"name":"Applications of Artificial Intelligence in Additive Manufacturing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121780037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Study and Application of Machine Learning Methods in Modern Additive Manufacturing Processes 机器学习方法在现代增材制造工艺中的研究与应用
Applications of Artificial Intelligence in Additive Manufacturing Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8516-0.ch004
R. Barua, S. Datta, P. Datta, A. Roychowdhury
{"title":"Study and Application of Machine Learning Methods in Modern Additive Manufacturing Processes","authors":"R. Barua, S. Datta, P. Datta, A. Roychowdhury","doi":"10.4018/978-1-7998-8516-0.ch004","DOIUrl":"https://doi.org/10.4018/978-1-7998-8516-0.ch004","url":null,"abstract":"Additive manufacturing (AM) make simpler the manufacturing of difficult geometric structures. Its possibility has quickly prolonged from the manufacture of pre-fabrication conception replicas to the making of finish practice portions driving the essential for superior part feature guarantee in the additively fabricated products. Machine learning (ML) is one of the encouraging methods that can be practiced to succeed in this aim. A modern study in this arena contains the procedure of managed and unconfirmed ML algorithms for excellent control and forecast of mechanical characteristics of AM products. This chapter describes the development of applying machine learning (ML) to numerous aspects of the additive manufacturing whole chain, counting model design, and quality evaluation. Present challenges in applying machine learning (ML) to additive manufacturing and possible solutions for these problems are then defined. Upcoming trends are planned in order to deliver a general discussion of this additive manufacturing area.","PeriodicalId":215367,"journal":{"name":"Applications of Artificial Intelligence in Additive Manufacturing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133364834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Additive Manufacturing Feature Taxonomy and Placement of Parts in AM Enclosure 增材制造特征分类和增材制造外壳中零件的放置
Applications of Artificial Intelligence in Additive Manufacturing Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8516-0.ch007
Prafull Agarwal, Rishi Kurian, R. Gupta
{"title":"Additive Manufacturing Feature Taxonomy and Placement of Parts in AM Enclosure","authors":"Prafull Agarwal, Rishi Kurian, R. Gupta","doi":"10.4018/978-1-7998-8516-0.ch007","DOIUrl":"https://doi.org/10.4018/978-1-7998-8516-0.ch007","url":null,"abstract":"Additive Manufacturing (AM) is a layer-by-layer deposition of material for the production of the desired product. The design flexibility associated with AM is much more when compared to the conventional manufacturing process. To manufacture a part with AM, two things play a critical role: the designing of the part and the other is the placement of the part in the build volume. As already mentioned, design flexibility associated with AM is much more when compared to the conventional manufacturing process. However, to correctly implement the design flexibility, we need a knowledge base at our disposal so that appropriate features can be used for the part production. The AM feature taxonomy forms the backbone of the knowledge base. The taxonomy comprises AM features classified based on different categories, which helps us understand every feature's importance. Talking about the part placement, we know that optimal placement is the key factor that makes the AM process economically feasible.","PeriodicalId":215367,"journal":{"name":"Applications of Artificial Intelligence in Additive Manufacturing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126881603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Parts Design and Process Optimization 零件设计和工艺优化
Applications of Artificial Intelligence in Additive Manufacturing Pub Date : 1900-01-01 DOI: 10.4018/978-1-7998-8516-0.ch002
H. Hassanin, Prveen Bidare, Y. Zweiri, K. Essa
{"title":"Parts Design and Process Optimization","authors":"H. Hassanin, Prveen Bidare, Y. Zweiri, K. Essa","doi":"10.4018/978-1-7998-8516-0.ch002","DOIUrl":"https://doi.org/10.4018/978-1-7998-8516-0.ch002","url":null,"abstract":"Artificial intelligence and additive manufacturing are primary drivers of Industry 4.0, which is reshaping the manufacturing industry. Based on the progressive layer-by-layer principle, additive manufacturing allows for the manufacturing of mechanical parts with a high degree of complexity. In this chapter, a deep learning neural network (DLNN) is introduced to rationalize the effect of cellular structure design factors as well as process variables on physical and mechanical properties utilizing laser powder bed fusion. The models developed were validated and utilized to create process maps. For both design and process optimization, the trained deep learning neural network model showed the highest accuracy. Deep learning neural networks were found to be an effective technique for predicting material properties from limited data sets, as per the findings.","PeriodicalId":215367,"journal":{"name":"Applications of Artificial Intelligence in Additive Manufacturing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127061470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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