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Abstract In this paper, we present a decentralized approach based on a simple set of rules to schedule multi-robot cooperative additive manufacturing (AM). The results obtained using the decentralized approach are compared with those obtained from an optimization-based method, representing the class of centralized approaches for manufacturing scheduling. Two simulated case studies are conducted to evaluate the performance of both approaches in total makespan. In the first case, four rectangular bars of different dimensions from small to large are printed. Each bar is first divided into small subtasks (called chunks), and four robots are then assigned to cooperatively print the resulting chunks. The second case study focuses on testing geometric complexity, where four robots are used to print a mask stencil (an inverse stencil, not face covering). The result shows that the centralized approach provides a better solution (shorter makespan) compared to the decentralized approach for small-scale problems (i.e., a few robots and chunks). However, the gap between the solutions shrinks while the scale increases, and the decentralized approach outperforms the centralized approach for large-scale problems. Additionally, the runtime for the centralized approach increased by 39-fold for the extra-large problem (600 chunks and four robots) compared to the small-scale problem (20 chunks and four robots). In contrast, the runtime for the decentralized approach was not affected by the scale of the problem. Finally, a Monte-Carlo analysis was performed to evaluate the robustness of the centralized approach against uncertainties in AM. The result shows that the variations in the printing time of different robots can lead to a significant discrepancy between the generated plan and the actual implementation, thereby causing collisions between robots that should have not happened if there were no uncertainties. On the other hand, the decentralized approach is more robust because a collision-free schedule is generated in real-time. 

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. 

Tor exit blocking, in which websites disallow clients arriving from Tor, is a growing and potentially existential threat to the anonymity network. This paper introduces HebTor, a new and robust architecture for exit bridges—short-lived proxies that serve as alternative egress points for Tor. A key insight of HebTor is that exit bridges can operate as Tor onion services, allowing any device that can create outbound TCP connections to serve as an exit bridge, regardless of the presence of NATs and/or firewalls. HebTor employs a micro-payment system that compensates exit bridge operators for their services, and a privacy-preserving reputation scheme that prevents freeloading. We show that HebTor effectively thwarts server-side blocking of Tor, and we describe the security, privacy, and legal implications of our design. 

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. 

This paper examines an existential threat to Tor— the increasing frequency at which websites apply discriminatory behavior to users who arrive via the anonymity network. Our main contribution is the introduction of Tor exit bridges. Exit bridges, constructed as short-lived virtual machines on cloud service providers, serve as alternative egress points for Tor and are designed to bypass server-side censorship. Due to the proliferation of managed cloud-based desktop services (e.g., Amazon Workspaces), there is already a surprisingly large fraction of web requests that originate in the cloud. Trivially disrupting exit bridges by blocking requests from the cloud would thus lead to significant collateral damage. Our experiments demonstrate that exit bridges effectively circumvent server-side blocking of Tor with low overhead. Ad- ditionally, we perform a cost-analysis of exit bridges and show that even a large-scale deployment can be done at low cost. 

Abstract 3D printing has been extensively used for rapid prototyping as well as low-volume production in aerospace, automotive, and medical industries. However, conventional manufacturing processes (i.e., injection molding and CNC machining) are more economical than 3D printing for high-volume mass production. In addition, current 3D printing techniques are not capable of fabricating large components due to the limited build size of commercially available 3D printers. To increase 3D printing throughput and build volume, a novel cooperative 3D printing technique has been recently introduced. Cooperative 3D printing is an additive manufacturing process where individual mobile 3D printers collaborate on printing a part simultaneously, thereby increasing printing speed and build volume. While cooperative 3D printing has the potential to fabricate larger components more efficiently, the mechanical properties of the components fabricated by cooperative 3D printing have not been systematically characterized. This paper aims to develop a data-driven predictive model that predicts the tensile strength of the components fabricated by cooperative 3D printing. Experimental results have shown that the predictive model is capable of predicting tensile strength as well as identifying the significant factors that affect the tensile strength. 

Cooperative 3D printing (C3DP) is a novel approach to additive manufacturing, where multiple mobile 3D printing robots work together cooperatively to print the desired part. At the core of C3DP lies the chunk-based printing strategy. This strategy splits the desired part into smaller chunks, and then the chunks are assigned and scheduled to be printed by individual printing robots. In our previous work, we presented various hardware and software components of C3DP, such as mobile 3D printers, chunk-based slicing, scheduling, and simulation. In this study, we present a fully integrated and functional C3DP platform with all necessary components, including chunker, slicer, scheduler, printing robots, build floor, and outline how they work in unison from a system-level perspective. To realize C3DP, new developments of both hardware and software are presented, including new chunking approaches, scalable scheduler for multiple robots, SCARA-based printing robots, a mobile platform for transporting printing robots, modular floor tiles, and a charging station for the mobile platform. Finally, we demonstrate the capability of the system using two case studies. In these demonstrations, a CAD model of a part is fed to the chunker, divided into smaller chunks, passed to the scheduler, and assigned and scheduled to be printed by the scheduler with a given number of robots. The slicer generates G-code for each of the chunks and combines G-code into one file for each robot. The simulator then uses the G-code generated by the slicer to generate animations for visualization purposes.

 

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.

 

Despite the emergence of probabilistic logic programming (PLP) languages for data driven applications, there are currently no debugging tools based on provenance for PLP programs. In this paper, we propose a novel provenance model and system, called P3 (Provenance for Probabilistic logic Programs) for analyzing PLP programs. P3 enables four types of provenance queries: traditional explanation queries, queries for finding the set of most important derivations within an approximate error, top-K most influential queries, and modification queries that enable us to modify tuple probabilities with fewest modifications to program or input data. We apply these queries into real-world scenarios and present theoretical analysis and practical algorithms for such queries. We have developed a prototype of P3, and our evaluation on real-world data demonstrates that the system can support a wide-range of provenance queries with explainable results. Moreover, the system maintains provenance and execute queries efficiently with low overhead.