Strengthening mechanism in thermomechanical fatigue properties of Ti6Al4V titanium alloy by laser shock peening

Additive manufacturing (AM) has undergone significant development over the past decades, resulting in vast amounts of data that carry valuable information. Numerous research studies have been conducted to extract insights from AM data and utilize it for optimizing various aspects such as the manufacturing process, supply chain, and real-time monitoring. Data integration into proposed digital twin frameworks and the application of machine learning techniques is expected to play pivotal roles in advancing AM in the future. In this paper, we provide an overview of machine learning and digital twin-assisted AM. On one hand, we discuss the research domain and highlight the machine-learning methods utilized in this field, including material analysis, design optimization, process parameter optimization, defect detection and monitoring, and sustainability. On the other hand, we examine the status of digital twin-assisted AM from the current research status to the technical approach and offer insights into future developments and perspectives in this area. This review paper aims to examine present research and development in the convergence of big data, machine learning, and digital twin-assisted AM. Although there are numerous review papers on machine learning for additive manufacturing and others on digital twins for AM, no existing paper has considered how these concepts are intrinsically connected and interrelated. Our paper is the first to integrate the three concepts big data, machine learning, and digital twins and propose a cohesive framework for how they can work together to improve the efficiency, accuracy, and sustainability of AM processes. By exploring latest advancements and applications within these domains, our objective is to emphasize the potential advantages and future possibilities associated with integration of these technologies in AM. 

Big data, machine learning, and digital twin assisted additive manufacturing: a review

Investigating the effect of laser shock peening on the wear behaviour of selective laser melted 316L stainless steel


 The lightweight aluminum (Al) alloys have been widely used in frontier fields like aerospace and automotive industries, which attracts great interest in additive manufacturing to process high-value Al parts. As a mainstream additive manufacturing technique, laser directed energy deposition (LDED) shows good scalability to meet requirements for large-format components manufacturing and repairing. However, LDED Al alloys are highly challenging due to the inherent poor printability (e.g., low laser absorption, high oxidation sensitivity and cracking tendency). To further promote the development of LDED high-performance Al alloys, this review gains a deep understanding of the challenges and strategies to improve printability in LDED Al alloys. The porosity, cracking, distortion, inclusions, elements evaporation and resultant inferior mechanical properties (than laser powder bed fusion) are the key challenges in LDED Al alloys. Processing parameter optimizations, in-situ alloy design, reinforcing particle addition and field assistance are the efficient approaches to improve the printability and performance of LDED Al alloys. The underlying correlations between processes, alloy innovation, characteristic microstructures, and achievable performances in LDED Al alloys are discussed. The benchmarking mechanical properties and primary strengthening mechanism of LDED Al alloys are summarized. This review aims to provide a critical and in-depth evaluation of current progress in LDED Al alloys. The future opportunities and perspectives in LDED high-performance Al alloys are also outlooked.

Review on Laser Directed Energy Deposited Aluminum Alloys

Surface integrity of SLM manufactured meso-size gears in laser shock peening without coating

In the recent decades, the laser-directed energy deposition (L-DED) of aluminium alloy has encountered significant challenges owing to the defects and tensile residual stress. In this study, the in-situ laser shock peening method is employed for the first time to improve the surface quality and mechanical properties of L-DED-fabricated AlSi10Mg alloy. The physical mechanism of the compressive pressure on the pore defects is accounted for using the finite element method. The numerical results indicate that severe plastic deformation driven by the shock wave contributes to defect healing, and the maximum pressure plays a crucial role. The effects of surface laser shock peening (SLSP) and layer-by-layer laser shock peening (LLSP) on the densification, surface quality, including the roughness and residual stress, microstructure, and mechanical properties of the samples are experimentally studied. After the LLSP treatment, the surface quality and mechanical properties of the samples exceed those of common aluminium alloys fabricated by laser powder bed fusion (L-PBF). After the SLSP treatment, a compressive residual stress with a maximum value of 30 ± 14 MPa is formed on the upper surface, and the LLSP treatment overcome the depth limitation of the affected zone. Furthermore, the LLSP treatment addresses the strength–ductility trade-off, and realizes an excellent combination of strength and ductility through grain refinement, dislocation strengthening, and compressive residual stress. Therefore, this work establishes the viability of in-situ laser shock peening for the rapid additive manufacturing of large-size aluminium alloys. The data used to support the findings of this study are available from the corresponding author upon request. 

In-situ laser shock peening for improved surface quality and mechanical properties of laser-directed energy-deposited AlSi10Mg alloy

Surface action mechanism and design considerations for the mechanical integrity of cu/low K BEOL interconnect during chemical mechanical polishing process

Innovative multiscale simulation with experimental validation of ultrafast laser processing in silicon carbide (4H-SiC)

Improved wear resistance of cemented carbide impact needle under high frequency micro-amplitude impact treated by high current pulsed electron beam

In the interconnects of microelectronic package with high current density and high-frequency fluctuation, electromigration (EM) events cause the cavities nucleation at the metal lines cathode and hillock pile-up at anode, which further lead to short circuit/open circuit and influence the overall device reliability substantially. In this work, mechanical modeling of EM-induced cavity failure was investigated and a modeling theory of efficient EM cavity morphology evolution based on physics was comprehensively introduced. According to mass diffusion theory, a dynamic model of elliptical cavity shape evolution with velocity and shape parameters was established. Meanwhile, kinetic equations of shape evolution expansion connected to cavity velocity and shape were proposed to explain the cavity expansion and cavity collapse processes, respectively. Based on the surface morphology analysis, it is obvious that cavity evolution form is determined by the competition between surface tension and electron wind. It is revealed that both them encourage cavity stretching in the direction of electric field intensity while compressing in the opposite. In addition, the mismatch of matter concentration between the left and right ends of the cavity will drive the cavity to migrate against the electric field intensity.

Physics-Based Model for Understanding Electromigration-Induced Cavity Evolution in Advanced Narrow Line Copper Interconnects

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Xu Han

Author Tools

Chat about Author