A Web-based Generative Process Planning System for FDM-based Additive Manufacturing

Makanda, Inno Lorren Désir; Jiang, Pingyu

doi:10.1016/j.ifacol.2021.04.084

Cited by 4 publications

(2 citation statements)

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“…HTML, JavaScript, CSS, and other programming languages are used to build the web app. In particular, the orientation algorithm introduced in our previous work [41] is adopted, as its reasoning capabilities align with the technique described in Section 3.1. The LPT rule presented in Algorithm 2 has been implemented in Python language.…”

Section: Prototype Implementationmentioning

confidence: 99%

A Multi-Part Production Planning System for a Distributed Network of 3D Printers under the Context of Social Manufacturing

et al. 2022

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Additive manufacturing (AM) systems are currently evolving into network-based models, where the distributed manufacturing resources from multiple enterprises are coordinated to complete product orders. The layer-by-layer approach of AM technologies gives manufacturers unprecedented freedom to create complex parts tailored to customer needs, but this comes at slow build rates. Consequently, for AM to become mainstream in the industry, challenges in production planning remain to be addressed to increase AM system productivity. This paper considers two practical problems encountered in AM systems, namely, production planning and part-to-printer assignment, and a series of heuristic algorithms are proposed to solve these problems. In particular, an approach for automatically determining part orientation, part-to-printer allocation, and nesting of multiple parts for a distributed network of fused filament fabrication three-dimensional printers is described to reduce the total production cost and time regarding the context of social manufacturing. The proposed method is implemented through a web application. The case study, using real-world parts and comparative analysis findings, indicated that the proposed method produces high-performance results.

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Section: Prototype Implementationmentioning

confidence: 99%

A Multi-Part Production Planning System for a Distributed Network of 3D Printers under the Context of Social Manufacturing

et al. 2022

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“…Successful and sustainable 3D printing requires users to evaluate capabilities, identify use cases, and make decisions on orientation, process parameters (temperature, layer height, etc. ), and support structures, which can be challenging for some [14].…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Axiomatic Evaluation of Additive Manufacturing Equipment and the 3D Printing Process Based on Sustainability and Human Factors

Mendoza-Muñoz,

Montoya-Reyes,

Maldonado-Macías

et al. 2024

Processes

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As interest in additive manufacturing (AM) continues to increase, it has become more important to have a robust method to help potential users select the AM process that best suits their technological needs while providing the greatest potential benefits in terms of sustainability and its effect on people. This paper presents the development of a framework for selecting the best AM process for a given application by considering both sustainability and human factors through the combination of axiomatic design and the analytic hierarchy process. Thirty-one participants with varying levels of expertise (novice and advanced users) were involved in the study, considering the frequency of 3D printer usage (novice users: never, rarely; expert users: sometimes, almost always, always) for prototyping parts. They employed fused deposition modeling (FDM) and stereolithography (SLA) (both 3D desktop printers) and collected data on five evaluation criteria. The participation of experts helped establish a novel methodology, with material cost deemed most important (49.8%), followed by cycle time (28%), energy consumption (11.7%), error rate (6.6%), and equipment noise (3.9%). The results showed that FDM was the optimal equipment option for advanced users. By examining the information content of the other options, it was found that FDM demanded less information than SLA, regardless of the user’s level of expertise. The proposed method is appropriate to assess the sustainability aspect of FDM and SLA; however, it can be further improved by adding indicators such as environmental impact, recyclability, and ergonomic and occupational health factors.

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Personalized federated unsupervised learning for nozzle condition monitoring using vibration sensors in additive manufacturing

Makanda,

Jiang,

Yang

2025

Robotics and Computer-Integrated Manufacturing

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A Web-based Generative Process Planning System for FDM-based Additive Manufacturing

Cited by 4 publications

References 10 publications

A Multi-Part Production Planning System for a Distributed Network of 3D Printers under the Context of Social Manufacturing

A Multi-Part Production Planning System for a Distributed Network of 3D Printers under the Context of Social Manufacturing

A Hierarchical Axiomatic Evaluation of Additive Manufacturing Equipment and the 3D Printing Process Based on Sustainability and Human Factors

Personalized federated unsupervised learning for nozzle condition monitoring using vibration sensors in additive manufacturing

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