Distributed manufacturing for and by the masses

Fused deposition modelling (FDM) 3D printing, as a supporting technology in social manufacturing and cloud manufacturing, is a rapidly growing technology in the era of industry 4.0. It produces objects with the layer-by-layer material accumulation technique. However, qualitative uncertainties are the common challenges yet. In order to assure print quality, studying the error causing parameters and minimizing their effects is important. This paper presents a feedback-based error compensation strategy, which integrates fuzzy inference system and grey wolf optimization algorithm. The objectives are twofold. First, the possible errors in FDM 3D printing are discussed in detail and optimal error causing parameters are obtained in percentage. This is used to understand the effects of the printing errors in every phase of the 3D printing process. From the nine optimization configuration trials used, Config-6 that has 100 number of iterations and 60 wolves is

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“…where the term η is an emphasis coefficient conventionally given in the interval [1,2]. The GWO algorithm is clearly illustrated in Fig.…”

Section: Optimization Methodsmentioning

confidence: 99%

“…The individual interest oriented commodities are favored by the customers. Three dimensional (3D) printing, as a massively distributed manufacturing (MDM), is a viable solution for customer interest-based fabrication [1]. 3D printing possesses the following steps [2].…”

Section: Introductionmentioning

confidence: 99%

A feedback-based print quality improving strategy for FDM 3D printing: an optimal design approach

Tamir

Xiong

Fang

et al. 2022

Int J Adv Manuf Technol

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“…With the advent of the 4th industrial revolution, information and communication technologies are integrated into most spheres of industry. [1][2][3] The technologies of the new era require architecture. However, existing human-interactive technologies involve various sensors that are physically connected to microprocessors, through which the information received by the sensors (input) is transferred to displays (output).…”

Section: Introductionmentioning

confidence: 99%

“…With the advent of the 4th industrial revolution, information and communication technologies are integrated into most spheres of industry. [ 1–3 ] The technologies of the new era require hyperconnectivity, superintelligence and hyperconvergence based on the emerging internet of things (IoT) and metaverse technology capable of connecting humans, machines and things through a wireless network. [ 4–8 ] The intelligent IoT, combined with artificial intelligence (AI), blockchain, big data and cloud systems, is being rapidly implemented in various fields such as medicine, [ 9,10 ] pharmaceuticals, [ 11 ] agriculture, [ 12 ] transportation [ 13 ] and construction.…”

Section: Introductionmentioning

confidence: 99%

Soft Human–Machine Interface Sensing Displays: Materials and Devices

Park

Jiang

et al. 2023

Advanced Materials

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The development of human‐interactive sensing displays (HISDs) that simultaneously detect and visualize stimuli is important for numerous cutting‐edge human–machine interface technologies. Therefore, innovative device platforms with optimized architectures of HISDs combined with novel high‐performance sensing and display materials are demonstrated. This study comprehensively reviews the recent advances in HISDs, particularly the device architectures that enable scaling‐down and simplifying the HISD, as well as material designs capable of directly visualizing input information received by various sensors. Various HISD platforms for integrating sensors and displays are described. HISDs consist of a sensor and display connected through a microprocessor, and attempts to assemble the two devices by eliminating the microprocessor are detailed. Single‐device HISD technologies are highlighted in which input stimuli acquired by sensory components are directly visualized with various optical components, such as electroluminescence, mechanoluminescence and structural color. The review forecasts future HISD technologies that demand the development of materials with molecular‐level synthetic precision that enables simultaneous sensing and visualization. Furthermore, emerging HISDs combined with artificial intelligence technologies and those enabling simultaneous detection and visualization of extrasensory information are discussed.

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“…Our rationale for creating such a robot is to increase the transparency of the manufacturing process of the robot, helping users build a better understanding of the system; add more customizability so that users can adapt the robot to their individual use-case; and have this robot serve as an introduction to multiple fields such as programming, manufacturing, life sciences, and chemistry. Our goals were (1) to design an LHR that can easily be assembled with snaptogether laser-cut parts, which are easily modified from open-source blueprints (i.e., CAD files) and is compatible with standard lab plasticware (e.g., 96-well plates); (2) to use inexpensive open-source DIY electronics such as Arduino boards [18,19]; (3) to enable distributed manufacturing (i.e., each individual user can manufacture the parts locally for themselves) [20], (4) to integrate this robot fully with a block-based programming language to enable nonspecialist users to operate and automate protocols; (5) to characterize the performance parameters of this robot, and (6) to take into account the perspective of potential future users (i.e., teachers) through a joint and iterative co-design process. We first describe the design of the A) The main frame of the robot is made of laser-cut plastic parts, while all consumables (i.e.…”

Section: Introductionmentioning

confidence: 99%

DIY liquid handling robots for integrated STEM education and life science research

Lam

Fuhrmann³

et al. 2022

PLoS ONE

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Automation has played a key role in improving the safety, accuracy, and efficiency of manufacturing and industrial processes and has the potential to greatly increase throughput in the life sciences. However, the lack of accessible entry-point automation hardware in life science research and STEM education hinders its widespread adoption and development for life science applications. Here we investigate the design of a low-cost (~$150) open-source DIY Arduino-controlled liquid handling robot (LHR) featuring plastic laser-cut parts. The robot moves in three axes with 0.5 mm accuracy and reliably dispenses liquid down to 20 μL. The open source, modular design allows for flexibility and easy modification. A block-based programming interface (Snap4Arduino) further extends the accessibility of this robot, encouraging adaptation and use by educators, hobbyists and beginner programmers. This robot was co-designed with teachers, and we detail the teachers’ feedback in the context of a qualitative study. We conclude that affordable and accessible LHRs similar to this one could provide a useful educational tool to be deployed in classrooms, and LHR-based curricula may encourage interest in STEM and effectively introduce automation technology to life science enthusiasts.

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Distributed manufacturing for and by the masses

Abstract: Networked systems, artificial intelligence, and an engaged public can enable mass customization of local products

Cited by 38 publications

References 6 publications

A feedback-based print quality improving strategy for FDM 3D printing: an optimal design approach

A feedback-based print quality improving strategy for FDM 3D printing: an optimal design approach

Soft Human–Machine Interface Sensing Displays: Materials and Devices

DIY liquid handling robots for integrated STEM education and life science research

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