Dataset Information

Additive and Photochemical Manufacturing of Copper.

ABSTRACT: In recent years, 3D printing technologies have been extensively developed, enabling rapid prototyping from a conceptual design to an actual product. However, additive manufacturing of metals in the existing technologies is still cost-intensive and time-consuming. Herein a novel platform for low-cost additive manufacturing is introduced by simultaneously combining the laser-induced forward transfer (LIFT) method with photochemical reaction. Using acrylonitrile butadiene styrene (ABS) polymer as the sacrificial layer, sufficient ejection momentum can be generated in the LIFT method. A low-cost continuous wave (CW) laser diode at 405?nm was utilized and proved to be able to transfer the photochemically synthesized copper onto the target substrate. The wavelength-dependent photochemical behaviour in the LIFT method was verified and characterized by both theoretical and experimental studies compared to 1064?nm fiber laser. The conductivity of the synthesized copper patterns could be enhanced using post electroless plating while retaining the designed pattern shapes. Prototypes of electronic circuits were accordingly built and demonstrated for powering up LEDs. Apart from pristine PDMS materials with low surface energies, the proposed method can simultaneously perform laser-induced forward transfer and photochemical synthesis of metals, starting from their metal oxide forms, onto various target substrates such as polyimide, glass and thermoplastics.

SUBMITTER: Yung WK

PROVIDER: S-EPMC5175138 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Additive and Photochemical Manufacturing of Copper.

Yung Winco K C WK Sun Bo B Meng Zhengong Z Huang Junfeng J Jin Yingdi Y Choy Hang Shan HS Cai Zhixiang Z Li Guijun G Ho Cheuk Lam CL Yang Jinlong J Wong Wai Yeung WY

Scientific reports 20161221

In recent years, 3D printing technologies have been extensively developed, enabling rapid prototyping from a conceptual design to an actual product. However, additive manufacturing of metals in the existing technologies is still cost-intensive and time-consuming. Herein a novel platform for low-cost additive manufacturing is introduced by simultaneously combining the laser-induced forward transfer (LIFT) method with photochemical reaction. Using acrylonitrile butadiene styrene (ABS) polymer as t ...[more]

PMID: 28000733

Similar Datasets

Project description:Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps-define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to postbuild inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM.

Dataset Information

Additive and Photochemical Manufacturing of Copper.

Publications

Additive and Photochemical Manufacturing of Copper.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets