A Collaborative and Ubiquitous System for Fabricating Dental Parts Using 3D Printing Technologies

The state of additive manufacturing in dental research - A systematic scoping review of 2012-2022

Background/purpose

Additive manufacturing (AM), also known as 3D printing, has the potential to transform the industry. While there have been advancements in using AM for dental restorations, there is still a need for further research to develop functional biomedical and dental materials. It's crucial to understand the current status of AM technology and research trends to advance dental research in this field. The aim of this study is to reveal the current status of international scientific publications in the field of dental research related to AM technologies.

Materials and methods

In this study, a systematic scoping review was conducted using appropriate keywords within the scope of international scientific publishing databases (PubMed and Web of Science). The review included related clinical and laboratory research, including both human and animal studies, case reports, review articles, and questionnaire studies. A total of 187 research studies were evaluated for quantitative synthesis in this review.

Results

The findings highlighted a rising trend in research numbers over the years (From 2012 to 2022). The most publications were produced in 2020 and 2021, with annual percentage increases of 25.7% and 26.2%, respectively. The majority of AM-related publications in dentistry research originate from Korea. The pioneer dental sub-fields with the ost publications in its category are prosthodontics and implantology, respectively.

Conclusion

The final review result clearly stated an expectation for the future that the research in dentistry would concentrate on AM technologies in order to increase the new product and process development in dental materials, tools, implants and new generation modelling strategy related to AM. The results of this work can be used as indicators of trends related to AM research in dentistry and/or as prospects for future publication expectations in this field.

Large-Scale Customized Production Scheduling of Multiagent-Based Medical 3D Printing

Three-dimensional (3D) printing, also known as additive manufacturing, has unique advantages over traditional manufacturing technologies; thus, it has attracted widespread attention in the medical field. Especially in the context of the frequent occurrence of major public health events, where the medical industry's demand for large-scale and customized production is increasing, traditional 3D printing production scheduling methods take a long time to handle large-scale customized medical 3D printing (M-3DP) production and have weak intelligent collaboration ability in the face of job-to-device matching under multimaterial printing. Given the problem caused by M-3DP large-scale customized production scheduling, an intelligent collaborative scheduling multiagent-based method is proposed in this study. First, a multiagent-based optimization model is established. On this basis, an improved genetic algorithm embedded with the product mix strategy and the intelligent matching mechanism is designed to optimize the completion time and load balance between devices. Finally, the effectiveness of the proposed method is evaluated using numerical simulation. The simulation results indicated that compared with the simple genetic algorithm, particle swarm optimization, and snake optimizer, the improved genetic algorithm could better reduce the M-3DP mass customization production scheduling time, optimize the load balance between devices, and promote the "intelligent manufacturing" process of M-3DP mass customization.

Assessing cloud manufacturing applications using an optimally rectified FAHP approach

Cloud Manufacturing (CMfg) is a new manufacturing paradigm that promises to reduce costs, improve data analysis, increase efficiency and flexibility, and provide manufacturers with closer partnerships. However, most past CMfg research has focused on either the information technology infrastructure or the planning and scheduling of a hypothetical CMfg system. In addition, the cost effectiveness of a CMfg application has rarely been assessed. As a result, a manufacturer is not sure whether to adopt a CMfg application or not. To address this issue, an optimally rectified fuzzy analytical hierarchy process (OR-FAHP) approach is proposed in this study to assess a CMfg application. The OR-FAHP approach solves the inconsistency problem of the conventional FAHP method, a well-known technology assessment technique, to make the analysis results more trustable. The OR-FAHP approach has been applied to assess and compare 10 CMfg applications.

Compressive and Flexural Strength of 3D-Printed and Conventional Resins Designated for Interim Fixed Dental Prostheses: An In Vitro Comparison

A provisionalization sequence is essential for obtaining a predictable final prosthetic outcome. An assessment of the mechanical behavior of interim prosthetic materials could orient clinicians towards selecting an appropriate material for each clinical case. The aim of this study was to comparatively evaluate the mechanical behavior—with compressive and three-point flexural tests—of certain 3D-printed and conventional resins used to obtain interim fixed dental prostheses. Four interim resin materials were investigated: two 3D-printed resins and two conventional resins (an auto-polymerized resin and a pressure/heat-cured acrylic resin). Cylindrically shaped samples (25 × 25 mm/diameter × height) were obtained for the compression tests and bar-shaped samples (80 × 20 × 5 mm/length × width × thickness) were produced for the flexural tests, observing the producers’ recommendations. The resulting 40 resin samples were subjected to mechanical tests using a universal testing machine. Additionally, a fractographic analysis of failed samples in bending was performed. The results showed that the additive manufactured samples exhibited higher elastic moduli (2.4 ± 0.02 GPa and 2.6 ± 0.18 GPa) than the conventional samples (1.3 ± 0.19 GPa and 1.3 ± 0.38 GPa), as well as a higher average bending strength (141 ± 17 MPa and 143 ± 15 MPa) when compared to the conventional samples (88 ± 10 MPa and 76 ± 7 MPa); the results also suggested that the materials were more homogenous when produced via additive manufacturing.

A ubiquitous healthcare system of 3D printing facilities for making dentures: Application of type-II fuzzy logic

A ubiquitous healthcare (UH) system of multiple 3D printing facilities is established in this study for making dentures. The UH system receives orders from dental clinics, and then distributes the dentures to be printed among 3D printing facilities to save time. Compared with existing systems for similar purposes, the UH system has two novel features. The first is the consideration of the possibility of reprinting in formulating the plan to avoid replanning. The other is the cooperation with home delivery services that have gradually become popular during the COVID-19 pandemic to save transportation time. The new features are subject to considerable uncertainties. To account for the uncertainties, both printing time and transportation time are modelled using interval type-II trapezoidal fuzzy numbers. Subsequently, an interval type-II fuzzy mixed integer-linear programming (FMILP) model is formulated and optimized to plan the operations of the UH system. A case study has been conducted to illustrate the applicability of the proposed methodology. According to experimental results, the proposed methodology was able to shorten the order fulfillment time by up to 9%.

A fuzzy mid-term capacity and production planning model for a manufacturing system with cloud-based capacity

Most of the past cloud manufacturing (CMfg) studies investigated the short-term production planning or job scheduling of a CMfg system, while the mid-term or long-term capacity and production planning of a CMfg system has rarely been addressed. In addition, most existing methods are suitable for CMfg systems comprising three-dimensional (3D) printers, computer numerical control (CNC) machines or robots, but ignore the coordination and transportation required for moving jobs across factories. To fill these gaps, a fuzzy mid-term capacity and production planning model for a manufacturer with cloud-based capacity is proposed in this study. The proposed methodology guides a manufacturer in choosing between non-cloud-based capacity and cloud-based capacity. It can be applied to factories utilizing machines with different degrees of automation including highly automatic equipment (such as 3D printers, CNC machines, and robots) and lowly automatic (legacy) machines, while existing methods assume that orders can be easily transferred between machines that are often highly automatic. In the proposed methodology, first, various types of capacity are unequally prioritized. Then, a fuzzy mixed-integer nonlinear programming model is formulated and optimized to make the mid-term or long-term capacity and production plan of a factory. The fuzzy capacity and production planning model is designed for factories with parallel machines. The proposed methodology has been applied to a case to illustrate its applicability. According to the experimental results, the proposed methodology successfully reduced total costs by up to 8%. The advantage of the proposed methodology over existing practices in fulfilling customers’ demand was also obvious.

Assessing the Robustness of a Factory Amid the COVID-19 Pandemic: A Fuzzy Collaborative Intelligence Approach

The COVID-19 pandemic has affected the operations of factories worldwide. However, the impact of the COVID-19 pandemic on different factories is not the same. In other words, the robustness of factories to the COVID-19 pandemic varies. To explore this topic, this study proposes a fuzzy collaborative intelligence approach to assess the robustness of a factory to the COVID-19 pandemic. In the proposed methodology, first, a number of experts apply a fuzzy collaborative intelligence approach to jointly evaluate the relative priorities of factors that affect the robustness of a factory to the COVID-19 pandemic. Subsequently, based on the evaluated relative priorities, a fuzzy weighted average method is applied to assess the robustness of a factory to the COVID-19 pandemic. The assessment result can be compared with that of another factory using a fuzzy technique for order preference by similarity to ideal solution. The proposed methodology has been applied to assess the robustness of a wafer fabrication factory in Taiwan to the COVID-19 pandemic.

Forecasting the unit cost of a DRAM product using a layered partial-consensus fuzzy collaborative forecasting approach

A layered partial-consensus fuzzy collaborative forecasting approach is proposed in this study to forecast the unit cost of a dynamic random access memory (DRAM) product. In the layered partial-consensus fuzzy collaborative forecasting approach, the partial-consensus fuzzy intersection (PCFI) operator is applied instead of the prevalent fuzzy intersection (FI) operator to aggregate the fuzzy forecasts by experts. In this way, some meaningful information, such as the suitable number of experts, can be obtained through observing changes in the PCFI result when the number of experts varies. After applying the layered partial-consensus fuzzy collaborative forecasting approach to a real case, the experimental results revealed that the layered partial-consensus fuzzy collaborative forecasting approach outperformed three existing methods. The most significant advantage was up to 13%.

Assessing factors critical to smart technology applications to mobile health care - the fgm-fahp approach

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The current practices of smart technology applications to mobile health care are reviewed.

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The fuzzy geometric mean and fuzzy analytic hierarchy process approach is proposed to assess the relative importance of critical factors.

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The most important critical factors were the relaxation of laws, unobtrusiveness, and the correct identification of the need.

Smart technologies present numerous opportunities for enhancing mobile health care. However, some concerns regarding the viability of smart technology applications must be addressed. This study investigated these concerns by reviewing the current practices of smart technology applications to mobile health care. As a result, five factors critical to the applicability of a smart technology to mobile health care are identified, and the fuzzy geometric mean-fuzzy analytic hierarchy process (FGM-FAHP) approach is proposed to assess the relative importance levels of the identified factors. The experimental results showed that the three most critical factors identified include: (a) the relaxation of the related medical laws; (b) unobtrusiveness; and (c) the precise need and situation of a user. Accordingly, approximately 44%, 26%, and 15% of the budget should be allocated to the realization of the three critical factors, respectively. In addition, the challenges involved and opportunities for enhancing the effectiveness of existing applications are discussed.