Computational Design of Scheduling Strategies for Multi-Robot Cooperative 3D Printing

2019 ◽  
Author(s):  
Laxmi Poudel ◽  
Wenchao Zhou ◽  
Zhenghui Sha
Keyword(s):  
3D Printing ◽  
Computational Design ◽  
Geometric Constraints ◽  
Computational Framework ◽  
Novel Approach ◽  
Dependency Tree ◽  
Scheduling Strategies ◽  
Evaluation Metric ◽  
Printing Time

Abstract Cooperative 3D printing (C3DP) is a novel approach to additive manufacturing, where multiple printhead-carrying mobile robots work together cooperatively to print a desired part. The core of C3DP is the chunk-based printing strategy in which the desired part is first split into smaller chunks, and then the chunks are assigned to individual printing robots. These robots will work on the chunks simultaneously and in a scheduled sequence until the entire part is complete. Though promising, C3DP lacks proper framework that enables automatic chunking and scheduling given the available number of robots. In this study, we develop a computational framework that can automatically generate print schedule for specified number of chunks. The framework contains 1) a random generator that creates random print schedule using adjacency matrix which represents directed dependency tree (DDT) structure of chunks; 2) a set of geometric constraints against which the randomly generated schedules will be checked for validation; and 3) a printing time evaluation metric for comparing the performance of all valid schedules. With the developed framework, we present a case study by printing a large rectangular plate which has dimensions beyond what traditional desktop printers can print. The study showcases that our computation framework can successfully generate a variety of scheduling strategies for collision-free C3DP without any human interventions.

A Heuristic Scaling Strategy for Multi-Robot Cooperative Three-Dimensional Printing

2020 ◽  
Vol 20 (4) ◽  
Author(s):  
Laxmi Poudel ◽  
Chandler Blair ◽  
Jace McPherson ◽  
Zhenghui Sha ◽  
Wenchao Zhou
Keyword(s):  
3D Printing ◽  
Fused Deposition Modeling ◽  
Three Dimensional ◽  
Evaluation Framework ◽  
Geometric Constraints ◽  
Mathematical Representation ◽  
Printing Process ◽  
Fused Deposition ◽  
3D Printers ◽  
Printing Time

Abstract While three-dimensional (3D) printing has been making significant strides over the past decades, it still trails behind mainstream manufacturing due to its lack of scalability in both print size and print speed. Cooperative 3D printing (C3DP) is an emerging technology that holds the promise to mitigate both of these issues by having a swarm of printhead-carrying mobile robots working together to finish a single print job cooperatively. In our previous work, we have developed a chunk-based printing strategy to enable the cooperative 3D printing with two fused deposition modeling (FDM) mobile 3D printers, which allows each of them to print one chunk at a time without interfering with the other and the printed part. In this paper, we present a novel method in discretizing the continuous 3D printing process, where the desired part is discretized into chunks, resulting in multi-stage 3D printing process. In addition, the key contribution of this study is the first working scaling strategy for cooperative 3D printing based on simple heuristics, called scalable parallel arrays of robots for 3DP (SPAR3), which enables many mobile 3D printers to work together to reduce the total printing time for large prints. In order to evaluate the performance of the printing strategy, a framework is developed based on directed dependency tree (DDT), which provides a mathematical and graphical description of dependency relationships and sequence of printing tasks. The graph-based framework can be used to estimate the total print time for a given print strategy. Along with the time evaluation metric, the developed framework provides us with a mathematical representation of geometric constraints that are temporospatially dynamic and need to be satisfied in order to achieve collision-free printing for any C3DP strategy. The DDT-based evaluation framework is then used to evaluate the proposed SPAR3 strategy. The results validate the SPAR3 as a collision-free strategy that can significantly shorten the printing time (about 11 times faster with 16 robots for the demonstrated examples) in comparison with the traditional 3D printing with single printhead.

Enabling Multi-Robot Cooperative Additive Manufacturing: Centralized vs. Decentralized Approaches

2021 ◽  
Author(s):  
Saivipulteja Elagandula ◽  
Laxmi Poudel ◽  
Wenchao Zhou ◽  
Zhenghui Sha
Keyword(s):  
3D Printing ◽  
Large Scale ◽  
Small Scale ◽  
Scale Problem ◽  
Problem Case ◽  
Large Scale Problem ◽  
First Case ◽  
Novel Approach ◽  
Multi Robot

Abstract This paper presents a decentralized approach based on a simple set of rules to carry out multi-robot cooperative 3D printing. Cooperative 3D printing is a novel approach to 3D printing that uses multiple mobile 3D printing robots to print a large part by dividing and assigning the part to multiple robots in parallel using the concept of chunk-based printing. The results obtained using the decentralized approach are then compared with those obtained from the centralized approach. Two case studies were performed to evaluate the performance of both approaches using makespan as the evaluation criterion. The first case is a small-scale problem with four printing robots and 20 chunks, whereas the second case study is a large-scale problem with ten printing robots and 200 chunks. The result shows that the centralized approach provides a better solution compared to the decentralized approach in both cases in terms of makespan. However, the gap between the solutions seems to shrink with the scale of the problem. While further study is required to verify this conclusion, the decrease in this gap indicates that the decentralized approach might compare favorably over the centralized approach for a large-scale problem in manufacturing using multiple mobile 3D printing robots. Additionally, the runtime for the large-scale problem (Case II) increases by 27-fold compared to the small-scale problem (Case I) for the centralized approach, whereas it only increased by less than 2-fold for the decentralized approach.

scholarly journals A Generative Approach for Scheduling Multi-Robot Cooperative Three-Dimensional Printing

2020 ◽  
Vol 20 (6) ◽  
Author(s):  
Laxmi Poudel ◽  
Wenchao Zhou ◽  
Zhenghui Sha
Keyword(s):  
Three Dimensional ◽  
Solution Space ◽  
Geometric Constraints ◽  
Human Cognition ◽  
Optimization Approach ◽  
Three Dimensional Printing ◽  
Novel Approach ◽  
Dependency Tree ◽  
Complicated Geometry ◽  
Generative Approach

Abstract Cooperative 3D printing (C3DP) is a novel approach to additive manufacturing, where multiple printhead-carrying mobile robots work cooperatively to print the desired part. The core of C3DP is the chunk-based printing strategy in which the desired part is first split into smaller chunks and then the chunks are assigned to individual robots to print and bond. These robots will work simultaneously in a scheduled sequence to print the entire part. Although promising, C3DP lacks a generative approach that enables automatic chunking and scheduling. In this study, we aim to develop a generative approach that can automatically generate different print schedules for a chunked object by exploring a larger solution space that is often beyond the capability of human cognition. The generative approach contains (1) a random generator of diverse print schedules based on an adjacency matrix that represents a directed dependency tree structure of chunks; (2) a set of geometric constraints against which the randomly generated schedules will be checked for validation, and (3) a printing time evaluator for comparing the performance of all valid schedules. We demonstrate the efficacy of the generative approach using two case studies: a large simple rectangular bar and a miniature folding sport utility vehicle (SUV) with more complicated geometry. This study demonstrates that the generative approach can generate a large number of different print schedules for collision-free C3DP, which cannot be explored solely using human heuristics. This generative approach lays the foundation for building the optimization approach of C3DP scheduling.

Resource-Constrained Scheduling for Multi-Robot Cooperative 3D Printing

2021 ◽  
pp. 1-29
Author(s):  
Laxmi Poudel ◽  
Wenchao Zhou ◽  
Zhenghui Sha
Keyword(s):  
3D Printing ◽  
Case Studies ◽  
Parallel Operation ◽  
Modified Genetic Algorithm ◽  
Novel Approach ◽  
And Performance ◽  
Printed Materials ◽  
Crossover And Mutation ◽  
Multi Robot

Abstract Cooperative 3D printing (C3DP) – a representative realization of cooperative manufacturing – is a novel approach that utilizes multiple mobile 3D printing robots for additive manufacturing. It makes the make-span much shorter compared to the traditional 3D printing due to parallel printing. In C3DP, collision-free scheduling is critical to the realization of cooperation and parallel operation among mobile printers. In the extant literature, there is a lack of methods to schedule multi-robot C3DP with limited resources. This study addresses this gap with two methods. The first method, dynamic dependency list algorithm (DDLA), uses constraint satisfaction to eliminate solutions that could result in collisions between robots and collisions between robots with already-printed materials. The second method, modified genetic algorithm (GA), uses chromosomes to represent chunk assignments and utilizes GA operators, such as the crossover and mutation, to generate diverse print schedules while maintaining the dependencies between chunks. Three case studies, including two large rectangular bars in different scales and a foldable SUV, are used to demonstrate the effectiveness and performance of the two methods. The results show that both methods can effectively generate valid print schedules using a specified number of robots while attempting to minimize the make-span. The results also show that both methods generate a print schedule with equal print time for the first two case studies with homogeneous chunks. In contrast, the modified GA outperforms the DDLA in the third case study, where the chunks are heterogeneous in volume and require different time to print.

A Novel Approach to the Automatic Designation of Predefined Census Enumeration Areas and Population Sampling Frames: A Case Study in Somalia

2019 ◽  
Author(s):  
Sarchil Qader ◽  
Veronique Lefebvre ◽  
Amy Ninneman ◽  
Kristen Himelein ◽  
Utz Pape ◽  
...  
Keyword(s):  
Population Sampling ◽  
Census Enumeration ◽  
Novel Approach

scholarly journals A Novel Approach for a Faster Prototyping of Winter Sport Equipment Using Digital Image Correlation and 3D Printing

Proceedings ◽  
2020 ◽  
Vol 49 (1) ◽  
pp. 125
Author(s):  
Martino Colonna ◽  
Benno Zingerle ◽  
Maria Federica Parisi ◽  
Claudio Gioia ◽  
Alessandro Speranzoni ◽  
...  
Keyword(s):  
3D Printing ◽  
Digital Image Correlation ◽  
Digital Image ◽  
Sport Activity ◽  
Image Correlation ◽  
Topological Optimization ◽  
Design Parameters ◽  
Sport Equipment ◽  
Novel Approach ◽  
3D Printed

The optimization of sport equipment parts requires considerable time and high costs due to the high complexity of the development process. For this reason, we have developed a novel approach to decrease the cost and time for the optimization of the design, which consists of producing a first prototype by 3D printing, applying the forces that normally acts during the sport activity using a test bench, and then measuring the local deformations using 3D digital image correlation (DIC). The design parameters are then modified by topological optimization and then DIC is performed again on the new 3D-printed modified part. The DIC analysis of 3D-printed parts has shown a good agreement with that of the injection-molded ones. The deformation measured with DIC are also well correlated with those provided by finite element method (FEM) analysis, and therefore DIC analysis proves to be a powerful tool to validate FEM models.

Using hidden Markov models to find discrete targets in continuous sociophonetic data

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Daniel Duncan
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Abstract Representation ◽  
Language Variation And Change ◽  
Novel Approach ◽  
Single Target ◽  
The Individual ◽  
Individual Speaker

Abstract Advances in sociophonetic research resulted in features once sorted into discrete bins now being measured continuously. This has implied a shift in what sociolinguists view as the abstract representation of the sociolinguistic variable. When measured discretely, variation is variation in selection: one variant is selected for production, and factors influencing language variation and change are influencing the frequency at which variants are selected. Measured continuously, variation is variation in execution: speakers have a single target for production, which they approximate with varying success. This paper suggests that both approaches can and should be considered in sociophonetic analysis. To that end, I offer the use of hidden Markov models (HMMs) as a novel approach to find speakers’ multiple targets within continuous data. Using the lot vowel among whites in Greater St. Louis as a case study, I compare 2-state and 1-state HMMs constructed at the individual speaker level. Ten of fifty-two speakers’ production is shown to involve the regular use of distinct fronted and backed variants of the vowel. This finding illustrates HMMs’ capacity to allow us to consider variation as both variant selection and execution, making them a useful tool in the analysis of sociophonetic data.

scholarly journals 3D printing system for earth-based construction: Case study of cob

2021 ◽  
Vol 124 ◽  
pp. 103577
Author(s):  
Mohamed Gomaa ◽  
Wassim Jabi ◽  
Alejandro Veliz Reyes ◽  
Veronica Soebarto
Keyword(s):  
3D Printing ◽  
Printing System
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