Additive manufacturing最新文献

{"title":"Effect of layer-wise femtosecond laser shock peening on cracking growth in laser powder bed fused AA 7075","authors":"Dianzheng Wang,&nbsp;Kailun Li,&nbsp;Jun Yao,&nbsp;Xiaozhuo Geng,&nbsp;Baorui Du","doi":"10.1016/j.addma.2024.104525","DOIUrl":"10.1016/j.addma.2024.104525","url":null,"abstract":"<div><div>Though laser powder bed fused (LPBF) technology has been widely applied in various industries, it still suffers from the issues of residual stress deformation and cracking, etc. This paper introduced the layer-wise femtosecond laser (fs-laser) shock peening (FLSP) firstly, as far as the authors know, to the LPBF process with the aim of tailoring the residual stress and suppressing cracking. A verification experiment on AA 7075 demonstrated that the surface crack density was reduced by 39 % with a layer-wise FLSP. The crack suppression can be explained from two aspects. On one side, the residual tensile stress was tailored to near zero, decreasing the cracking growth motivation. On the other side, the grain size was decreased while the dislocation density was increased with the FLSP, increasing the cracking growth resistance. This study provides novel ideas for solving the problems of deformation and cracking in LPBF technology.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"95 ","pages":"Article 104525"},"PeriodicalIF":10.3,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-layer thermal simulation using physics-informed neural network","authors":"Bohan Peng,&nbsp;Ajit Panesar","doi":"10.1016/j.addma.2024.104498","DOIUrl":"10.1016/j.addma.2024.104498","url":null,"abstract":"<div><div>This paper presents a physics-informed neural network (PINN)-based solution framework that predicts the thermal history during a multi-layer Directed Energy Deposition (DED) process. The meshless nature and the readily available derivative information of PINN solution opens up new opportunities for modelling the thermally induced distortion in metal Additive Manufacturing (AM). The proposed framework incorporates simple yet effective strategies that enable PINN to overcome the usual shortfall of neural networks (NNs) in dealing with discontinuities. It is a critical step for applying PINN to the multi-layer problem which intrinsically contains discontinuities due to the layer-by-layer nature of DED and other metal AM processes. The accuracy of the proposed framework is validated via a benchmark test against ANSYS simulation. Leveraging the possibility of initialisation with prior knowledge, PINN is also demonstrating potential computational time-savings, especially for larger parts. Furthermore, remarks on strategies to improve ease of training and prediction accuracy by PINN for the particular use case in DED temperature history prediction have been made. The proposed framework sets the foundation for the subsequent exploration of applying scientific machine learning (SciML) techniques to real-life engineering applications.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"95 ","pages":"Article 104498"},"PeriodicalIF":10.3,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Three-dimensional honeycomb structured BaTiO3-based piezoelectric ceramics via texturing and vat photopolymerization","authors":"Lianzhong Zhao ,&nbsp;Xi Yuan ,&nbsp;Xuefan Zhou ,&nbsp;Qijun Wang ,&nbsp;Jiang Li ,&nbsp;Xiang Xiong ,&nbsp;Qiang Zhang ,&nbsp;Chuan Chen ,&nbsp;Siyang Chen ,&nbsp;Dengfeng Ju ,&nbsp;Yan Zhang ,&nbsp;Dou Zhang","doi":"10.1016/j.addma.2024.104542","DOIUrl":"10.1016/j.addma.2024.104542","url":null,"abstract":"<div><div>Textured piezoelectric ceramics have attracted significant attention due to their ability to achieve ultra-high piezoelectric properties comparable to single crystals at a lower cost. Traditional processing techniques, such as tape casting, can efficiently produce textured piezoelectric ceramics with simple structures but are inadequate for fabricating three-dimensional structures with high complexity, thereby limiting their applications in specific fields. Vat photopolymerization (VPP), an advanced additive manufacturing technology, can rapidly and accurately create intricate three-dimensional structures. Crucially, VPP can provide the necessary shear force to align the templates, resulting in the highly-textured piezoelectric ceramics. In this study, BaTiO₃-based piezoelectric ceramics with a high degree of texture (97.2 %) were produced using VPP technology. These ceramics exhibited a large piezoelectric coefficient (<em>d</em>₃₃ = 511 pC/N), which was 66 % higher than that of non-textured ceramics. Furthermore, textured ceramics with a honeycomb structure were fabricated, demonstrating their potential in sensing applications. This work confirms the feasibility of using VPP technology to prepare high-performance, complex-structured textured ceramics, thereby promoting the development and application of textured piezoelectric ceramics.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"95 ","pages":"Article 104542"},"PeriodicalIF":10.3,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Additive manufacturing of Diels-Alder self-healing polymers: Separate heating system to enhance mechanical, healing properties and assembly-free smart structures","authors":"A. Pavone ,&nbsp;S. Terryn ,&nbsp;H. Abdolmaleki ,&nbsp;A.C. Cornellà ,&nbsp;G. Stano ,&nbsp;G. Percoco ,&nbsp;B. Vanderborght","doi":"10.1016/j.addma.2024.104535","DOIUrl":"10.1016/j.addma.2024.104535","url":null,"abstract":"<div><div>Over the past decades, self-healing polymers have become increasingly popular due to their unique ability to recover mechanical and functional properties after sustaining structural damage, which significantly extends their lifespan compared to traditional polymers. Material Extrusion (MEX) 3D printing has recently emerged as a possible manufacturing approach for processing self-healing polymers; however, commercial MEX 3D printers lack of the flexibility to fabricate complex and functional structures based on such materials. In this work, an innovative MEX setup for extruding self-healing polymer networks based on a thermo-reversible reaction is presented. The proposed approach is based on the leverage of a separate heating system (SHS), enabling the degelation of the self-healing polymer network into a printable ink. This SHS regulates both the syringe-barrel, and nozzle temperatures during the processing (degelation and extrusion) of self-healing inks, leading to enhanced mechanical performance (Young modulus, tensile strength), and extrusion accuracy of 3D printed structures. The effectiveness of the SHS-based approach is demonstrated by an improved geometrical accuracy (filament deviation reduced by 26 %), which is directly correlated to the mitigation of the extrusion force (variability reduced by 77 %). Moreover, the SHS approach also improved both the mechanical properties and the self-healing performance of the printed parts. Finally, two different self-healing polymers a dielectric and an electrically conductive were extruded in a single manufacturing cycle to fabricate a self-sensing structure. This structure is capable of detecting bending with a sensitivity of 3.10 Ω/degree, even after healing. This paper aims to advance the role of MEX beyond its current limitations by enabling processing of high-quality self-healing structures with embedded sensors.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"95 ","pages":"Article 104535"},"PeriodicalIF":10.3,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing classical Scheil–Gulliver model calculations by predicting generated phases and corresponding compositions through machine learning techniques","authors":"Zhengdi Liu,&nbsp;Wenwen Sun","doi":"10.1016/j.addma.2024.104516","DOIUrl":"10.1016/j.addma.2024.104516","url":null,"abstract":"<div><div>The classical Scheil-Gulliver model is an important tool for simulating non-equilibrium solidification processes in materials science, especially for rapid cooling processes such as additive manufacturing. However, the high computational intensity of the Scheil-Gulliver calculations through the <strong>CAL</strong>culation of <strong>PHA</strong>se <strong>D</strong>iagrams (CALPHAD) method, especially for complex alloys, limits its application in high-throughput scenarios. This study introduces a novel machine learning (ML)-based approach to enhance the calculation of the Scheil-Gulliver model, facilitating efficient and large-scale simulations. We developed a suite of ML models to predict generated phases and their elemental composition in the Fe-Ni-Cr-Mn system. By integrating these models with a parallel calculation algorithm, the calculation process is completed in 52 minutes, while performing direct one-by-one calculations could take months. Our high-throughput calculations successfully processed 176,688 out of 176,851 compositions. Based on the calculated data, an algorithm was designed for linear gradient pathway planning. Thirty pathways from the BCC_B2 phase to the FCC_L12 phase were used for exemplification, with 28 pathways validated as feasible.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"95 ","pages":"Article 104516"},"PeriodicalIF":10.3,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward 3D printability prediction for thermoplastic polymer nanocomposites: Insights from extrusion printing of PLA-based systems","authors":"Burcu Ozdemir ,&nbsp;Miguel Hernández-del-Valle ,&nbsp;Maggie Gaunt ,&nbsp;Christina Schenk ,&nbsp;Lucía Echevarría-Pastrana ,&nbsp;Juan P. Fernández-Blázquez ,&nbsp;De-Yi Wang ,&nbsp;Maciej Haranczyk","doi":"10.1016/j.addma.2024.104533","DOIUrl":"10.1016/j.addma.2024.104533","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The development of new thermoplastic-based nanocomposites for, as well as using, 3D printing requires extensive experimental testing. One typically goes through many failed, or otherwise sub-optimal, iterations before finding acceptable solutions (e.g. compositions, 3D printing parameters). It is desirable to reduce the number of such iterations as well as exclude failed experiments that often require laborious disassembly and cleaning of the 3D printer. This issue could be addressed if we were able to understand, and ultimately predict ahead of experiments if a given material can be 3D printed successfully. Herein, we report on our investigations into forecasting the printing and resultant properties of polymer nanocomposites while encompassing both material properties and printing parameters, enabling the model to generalize across various thermoplastics and additives. To do so, nanocomposites of two different commercially available bio-based PLAs with varying concentrations of nanoclay (NC) and graphene nanoplatelets (GNP) were prepared using a twin-screw extruder. The thermal and rheological properties of the nanocomposites were analyzed. These materials were printed at varying temperature and flow using a pellet printer. The quality of the printing was evaluated by measuring weight fluctuation, internal diameter of cylindrical specimen, and surface uniformity. The interactions between material properties and printing parameters are complex but captured effectively by a machine learning model, specifically we demonstrate such a predictive model to forecast printability and, printing quality utilizing a Random Forest algorithm. Printability was predicted by developing a classification model with constraints based on the weight fluctuation (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;Δ&lt;/mi&gt;&lt;mi&gt;W&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) of the printed sample w.r.t. the optimal print; defining “not printable” for &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;≤&lt;/mo&gt;&lt;mi&gt;Δ&lt;/mi&gt;&lt;mi&gt;W&lt;/mi&gt;&lt;mo&gt;&lt;&lt;/mo&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;8&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; and “printable” for &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;Δ&lt;/mi&gt;&lt;mi&gt;W&lt;/mi&gt;&lt;mo&gt;≥&lt;/mo&gt;&lt;mo&gt;−&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;8&lt;/mn&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. The classification model for predicting printability, performed well with an accuracy of 92.8% and identified flow index and complex viscosity, contributing 52% to the model’s importance. Another model to predict &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;Δ&lt;/mi&gt;&lt;mi&gt;W&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; of the only on successful prints also showed strong performance, emphasizing the importance of viscoelastic properties, thermal stability, and printing temperature. For diameter change (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mi&gt;Δ&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;D&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;), the Random Forest model identified flow consistency index, complex viscosity, and thermal stability as influential parameters, with crystallization enthalpy gaining increased importance, reflecting its role in cryst","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"95 ","pages":"Article 104533"},"PeriodicalIF":10.3,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Volumetric 3D printing of ionic conductive elastomers for multifunctional flexible electronics","authors":"Shuai Peng ,&nbsp;Geming Chen ,&nbsp;Xuan Luo ,&nbsp;Xinghao Zhang ,&nbsp;Dongya Li ,&nbsp;Yibo Xu ,&nbsp;Chonghao Sun ,&nbsp;Erwei Shang ,&nbsp;Xiaolong Wang ,&nbsp;Yu Liu","doi":"10.1016/j.addma.2024.104536","DOIUrl":"10.1016/j.addma.2024.104536","url":null,"abstract":"<div><div>Flexible electronics based on ionic conductive elastomers (ICE) hold significant potential for applications in smart wearables, self-powered sensing, and human-computer interaction. However, current fabrication techniques constrain ICE-based ionic electronic components to simplified volumetric geometries, limiting their functionality. This work reports a volumetric 3D printing (V3DP) for fabricating flexible electronic components with excessive transparency, high conductivity, excellent thermal stability, and superior adhesion. By controlling the light dose, this printing technique enables precise modulation of the printed structures' mechanical properties. Furthermore, V3DP greatly improves the processing efficiency of high-viscosity ionic conductive liquids and makes it easier to prepare composite structures, combining different conductive mechanisms through unique overprinting. This study provides a promising strategy for preparing multifunctional, liquid-free, ionic flexible electronics, such as strain sensors and ionic-electronic triboelectric nanogenerators (iTENG).</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"95 ","pages":"Article 104536"},"PeriodicalIF":10.3,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing laser powder bed fusion with non-spherical powder: Powder-process-structure-property relationships through experimental and analytical studies of fatigue performance","authors":"Mohammadreza Asherloo ,&nbsp;Madhavan Sampath Ramadurai ,&nbsp;Mike Heim ,&nbsp;Dave Nelson ,&nbsp;Muktesh Paliwal ,&nbsp;Iman Ghamarian ,&nbsp;Anthony D. Rollett ,&nbsp;Amir Mostafaei","doi":"10.1016/j.addma.2024.104534","DOIUrl":"10.1016/j.addma.2024.104534","url":null,"abstract":"<div><div>This study investigates the multifaceted interdependencies among powder characteristics (i.e., non-spherical morphology and particle size ranging 50–120 or 75–175 µm), laser powder bed fusion (L-PBF) process condition (i.e., contouring), post-process treatments (i.e., hot isostatic pressing (HIP) and mechanical grinding) on the pore, microstructure, surface finish, and fatigue behavior of additively manufactured Ti-6Al-4V samples. Microstructure analysis shows a phase transformation α′ → α+β microstructure after HIP treatment (at 899±14 °C for 2 h under the applied pressure of 1034±34 bar) of the as-built Ti-6Al-4V parts. The findings from pore analysis using micro-computed tomography (μ-CT) show an increase in sub-surface pores when relatively smaller powders are L-PBF processed including contouring. Surface optical profilometry reveals a decrease in surface roughness when fine powder is L-PBF including contouring. Pore analysis conducted through μ-CT reveals that the presence of lack-of-fusion pores within the L-PBF processed coarse powder is more pronounced when compared to the fine powder. Furthermore, HIP treatment does not eliminate these pores. The fracture failure in as-printed parts occurs at the surface, while the combination of HIP and mechanical grinding alters crack initiation to subsurface pore defects. Fractography reveals that HIP and as-built samples followed the facet formation and pseudo-brittle fracture mechanisms, respectively. Fatigue life assessments, supported by statistical analysis, indicate that mechanical grinding and HIP significantly enhanced fatigue resistance, approaching the benchmarks set by wrought Ti-6Al-4V alloy. A fatigue prediction model which considers the surface roughness as a micro-notch has been used.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"95 ","pages":"Article 104534"},"PeriodicalIF":10.3,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Local porosity prediction in metal powder bed fusion using in-situ thermography: A comparative study of machine learning techniques","authors":"Simon Oster,&nbsp;Nils Scheuschner,&nbsp;Keerthana Chand,&nbsp;Simon J. Altenburg","doi":"10.1016/j.addma.2024.104502","DOIUrl":"10.1016/j.addma.2024.104502","url":null,"abstract":"<div><div>The formation of flaws such as internal porosity in parts produced by Metal-based Powder Bed Fusion with Laser Beam (PBF-LB/M) significantly hinders its broader industrial application, as porosity can potentially lead to part failure. Addressing this issue, this study explores the efficacy of in-situ thermography, particularly short-wave infrared thermography, for detecting and predicting porosity during manufacturing. This technique is capable of monitoring the part’s thermal history which is closely connected to the flaw formation process. Recent advancements in Machine Learning (ML) have been increasingly leveraged for porosity prediction in PBF-LB/M. However, previous research primarily focused on global rather than localized porosity prediction which simplified the complex prediction task. Thereby, the opportunity to correlate the predicted flaw position with expected part strain to judge the severity of the flaw for part performance is neglected. This study aims to bridge this gap by studying the potential of SWIR thermography for predicting local porosity levels using regression models. The models are trained on data from two identical HAYNES®282® specimens. We compare the effectiveness of feature-based and raw data-based models in predicting different porosity types and examine the importance of input data in porosity prediction. We show that models trained on SWIR thermogram data can identify systematic trends in local flaw formation. This is demonstrated for forced flaw formation using process parameter shifts and, moreover, for randomly formed flaws in the specimen bulk. Furthermore, we identify features of high importance for the prediction of lack-of-fusion and keyhole porosity from SWIR monitoring data.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"95 ","pages":"Article 104502"},"PeriodicalIF":10.3,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using virtual reality to orient parts for additive manufacturing and its effects on manufacturability and experiential outcomes","authors":"Jayant Mathur ,&nbsp;Scarlett R. Miller ,&nbsp;Timothy W. Simpson ,&nbsp;Nicholas A. Meisel","doi":"10.1016/j.addma.2024.104421","DOIUrl":"10.1016/j.addma.2024.104421","url":null,"abstract":"<div><div>Additive manufacturing (AM) enables the fabrication of geometrically complex designs through layer-by-layer joining of material along single or multiple directions. To determine favorable design and manufacturing solutions, designers must navigate this 3D spatial complexity while ensuring the functionality and manufacturability of their designs. Evaluating the manufacturability of their solutions necessitates modalities that help naturally visualize AM processes and the designs enabled by them. Digitally non-immersive visualization can reduce this expense, but digital immersion has the potential to further improve the experience before building. This research investigates how differences in immersion between computer-aided (CAx) and virtual reality (VR) environments affect a designer’s approach to solving a build-with-AM (BAM) problem and its outcomes. First, it studies how immersion affects determining favorable build orientations when considering the additive manufacturability outcomes of designs of varying complexity. Second, it studies how immersion affects the participants’ experiential outcomes, including evaluation time, attempts made, and cognitive load when solving the BAM problem. Analysis reveals that as design complexity increases, visualizing and manufacturing designs in VR improves additive manufacturability outcomes by reducing build time and support material usage compared to CAx, reducing manufacturing costs by up to 4.61 % ($32) per part. Using immersive VR also helps designers determine favorable build orientations faster with fewer attempts and without increasing the cognitive load experienced. These findings present important implications for the role of immersive experiences in preparing designers to quickly produce lower-cost and sustainable manufacturing solutions with AM.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"94 ","pages":"Article 104421"},"PeriodicalIF":10.3,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142318869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}