Computational Frameworks for Multi-Robot Cooperative 3D Printing and Planning

This dissertation proposes a novel cooperative 3D printing (C3DP) approach for multi-robot additive manufacturing (AM) and presents scheduling and planning strategies that enable multi-robot cooperation in the manufacturing environment. C3DP is the first step towards achieving the overarching goal of swarm manufacturing (SM). SM is a paradigm for distributed manufacturing that envisions networks of micro-factories, each of which employs thousands of mobile robots that can manufacture different products on demand. SM breaks down the complicated supply chain used to deliver a product from a large production facility from one part of the world to another. Instead, it establishes a network of geographically distributed micro-factories that can manufacture the product at a smaller scale without increasing the cost. In C3DP, many printhead-carrying mobile robots work together to print a single part cooperatively. While it holds the promise to mitigate issues associated with gantry-based 3D printers, such as lack of scalability in print size and print speed, its realization is challenging because existing studies in the relevant literature do not address the fundamental issues in C3DP that stem from the amalgamation of the mobile nature of the robots, and continuous nature of the manufacturing tasks. To address this challenge, this dissertation asks two fundamental research questions: RQ1) How can the traditional 3D printing process be transformed to enable multi-robot cooperative AM? RQ2) How can cooperative manufacturing planning be realized in the presence of inherent uncertainties in AM and constraints that are dynamic in both space and time? To answer RQ1, we discretize the process of 3D printing into multiple stages. These stages include chunking (dividing a part into smaller chunks), scheduling (assigning chunks to robots and generating print sequences), and path and motion planning. To test the viability of the approach, we conducted a study on the tensile strength of chunk-based parts to examine their mechanical integrity. The study demonstrates that the chunk-based part can be as strong as the conventionally 3D-printed part. Next, we present different computational frameworks to address scheduling issues in C3DP. These include the development of 1) the world-first working strategy for C3DP, 2) a framework for automatic print schedule generation, evaluation, and validation, and 3) a resource-constrained scheduling approach for C3DP that uses a meta-heuristic approach such as a modified Genetic Algorithm (MGA) and a new algorithm that uses a constraint-satisficing approach to obtain collision-free print schedules for C3DP. To answer RQ2, a multi-robot decentralized approach based on a simple set of rules is used to plan for C3DP. The approach is resilient to uncertainties such as variation in printing times and can even outperform the centralized approach that uses MGA with a conflict-based search for large-scale problems. By answering these two fundamental questions, the central objective of the research project to establish computational frameworks to enable multi-robot cooperative manufacturing was achieved. The search for answers to the RQs led to the development of novel concepts that can be used not only in C3DP, but many other manufacturing tasks, in general, requiring cooperation among multiple robots

State of the art in Japanese CAD methodologies for mechanical products : industrial practice and university research

Originally published in the Working paper series of the MIT International Motor Vehicle Program.Includes bibliographical references (p. 34-35).Daniel E. Whitney

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Survey on Additive Manufacturing, Cloud 3D Printing and Services

Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacement for traditional manufacturing resources in case of spontaneous problems in the established manufacturing processes. In order to be of use in these scenarios the AM resources must adhere to a strict principle of transparency and service composition in adherence to the Cloud Computing (CC) paradigm. With this review we provide an overview over CM, AM and relevant domains as well as present the historical development of scientific research in these fields, starting from 2002. Part of this work is also a meta-review on the domain to further detail its development and structure

Mass Production Processes

It is always hard to set manufacturing systems to produce large quantities of standardized parts. Controlling these mass production lines needs deep knowledge, hard experience, and the required related tools as well. The use of modern methods and techniques to produce a large quantity of products within productive manufacturing processes provides improvements in manufacturing costs and product quality. In order to serve these purposes, this book aims to reflect on the advanced manufacturing systems of different alloys in production with related components and automation technologies. Additionally, it focuses on mass production processes designed according to Industry 4.0 considering different kinds of quality and improvement works in mass production systems for high productive and sustainable manufacturing. This book may be interesting to researchers, industrial employees, or any other partners who work for better quality manufacturing at any stage of the mass production processes

Structural Innovation Through Digital Means: Wooden Waves, Galaxia, Conifera, Sandwaves, Polibot, Silkworm

This folio presents a body of investigation into the possibilities of innovative built structures developed by manipulating digital technologies to generate new structural systems and tested using manual as well as digital construction methods. Research is generated through a range of projects, in different contexts, at various scales, using innovative building and structural design, and considering the tools generated to make the project as part of the research output. The work further tests new and emerging patterns of architectural practice, construction and procurement. Projects have moved towards a more environmentally-friendly parametrically generated approach e.g. through developing re-useable and compostable structures. Mamou-Mani developed this research through commissioned briefs by clients and self-initiated competition entries for large-scale permanent structures. Often, projects are inspired by patterns found in nature, and the research explores, develops, tests and expands upon these parametrically to suggest new structural models. Structures included are the installations for Buro Happold’s headquarters (2015): for fashion brand COS (2018) –one of the largest PLA (bioplastic made from fermented sugar) 3D-printed structures in the world to date; the largest sand-printed installation to date; and the well-published 60 metre-wide, 20 metre-high Galaxia temporary temple building erected for the 2018 Burning Man event in the USA, duly burned down after use. Innovative procurement and construction methods involved working with volunteers and students as well as skilled construction teams, and formulating self-generated projects that raise finance using crowd-funding platforms and ‘investment angels’, and considering the new architectures these might generate. Software innovations include the Silkworm plugin that exports G-code from Grasshopper, enabling one of the world’s largest 3D-printed pavilions. Iterative knowledge development from this research is shared through the WeWantToLearn.net blog which has 1.6M viewers, as well hands-on exchange with volunteers, students and skilled construction teams, as well as more conventional dissemination

Artificial Intelligence as an Enabler of Quick and Effective Production Repurposing Manufactur-ing: An Exploratory Review and Future Research Propositions

The outbreak of Covid-19 created disruptions in manufacturing operations. One of the most serious negative impacts is the shortage of critical medical supplies. Manufacturing firms faced pressure from governments to use their manufacturing capacity to repurpose their production for meeting the critical demand for necessary products. For this purpose, recent advancements in technology and artificial intelligence (AI) could act as response solutions to conquer the threats linked with repurposing manufacturing (RM). The study’s purpose is to investigate the significance of AI in RM through a systematic literature review (SLR). This study gathered around 453 articles from the SCOPUS database in the selected research field. Structural Topic Modeling (STM) was utilized to generate emerging research themes from the selected documents on AI in RM. In addition, to study the research trends in the field of AI in RM, a bibliometric analysis was undertaken using the R-package. The findings of the study showed that there is a vast scope for research in this area as the yearly global production of articles in this field is limited. However, it is an evolving field and many research collaborations were identified. The study proposes a comprehensive research framework and propositions for future research development

Design, evaluation, and control of nexus: a multiscale additive manufacturing platform with integrated 3D printing and robotic assembly.

Additive manufacturing (AM) technology is an emerging approach to creating three-dimensional (3D) objects and has seen numerous applications in medical implants, transportation, aerospace, energy, consumer products, etc. Compared with manufacturing by forming and machining, additive manufacturing techniques provide more rapid, economical, efficient, reliable, and complex manufacturing processes. However, additive manufacturing also has limitations on print strength and dimensional tolerance, while traditional additive manufacturing hardware platforms for 3D printing have limited flexibility. In particular, part geometry and materials are limited to most 3D printing hardware. In addition, for multiscale and complex products, samples must be printed, fabricated, and transferred among different additive manufacturing platforms in different locations, which leads to high cost, long process time, and low yield of products. This thesis investigates methods to design, evaluate, and control the NeXus, which is a novel custom robotic platform for multiscale additive manufacturing with integrated 3D printing and robotic assembly. NeXus can be used to prototype miniature devices and systems, such as wearable MEMS sensor fabrics, microrobots for wafer-scale microfactories, tactile robot skins, next generation energy storage (solar cells), nanostructure plasmonic devices, and biosensors. The NeXus has the flexibility to fixture, position, transport, and assemble components across a wide spectrum of length scales (Macro-Meso-Micro-Nano, 1m to 100nm) and provides unparalleled additive process capabilities such as 3D printing through both aerosol jetting and ultrasonic bonding and forming, thin-film photonic sintering, fiber loom weaving, and in-situ Micro-Electro-Mechanical System (MEMS) packaging and interconnect formation. The NeXus system has a footprint of around 4m x 3.5m x 2.4m (X-Y-Z) and includes two industrial robotic arms, precision positioners, multiple manipulation tools, and additive manufacturing processes and packaging capabilities. The design of the NeXus platform adopted the Lean Robotic Micromanufacturing (LRM) design principles and simulation tools to mitigate development risks. The NeXus has more than 50 degrees of freedom (DOF) from different instruments, precise evaluation of the custom robots and positioners is indispensable before employing them in complex and multiscale applications. The integration and control of multi-functional instruments is also a challenge in the NeXus system due to different communication protocols and compatibility. Thus, the NeXus system is controlled by National Instruments (NI) LabVIEW real-time operating system (RTOS) with NI PXI controller and a LabVIEW State Machine User Interface (SMUI) and was programmed considering the synchronization of various instruments and sequencing of additive manufacturing processes for different tasks. The operation sequences of each robot along with relevant tools must be organized in safe mode to avoid crashes and damage to tools during robots’ motions. This thesis also describes two demonstrators that are realized by the NeXus system in detail: skin tactile sensor arrays and electronic textiles. The fabrication process of the skin tactile sensor uses the automated manufacturing line in the NeXus with pattern design, precise calibration, synchronization of an Aerosol Jet printer, and a custom positioner. The fabrication process for electronic textiles is a combination of MEMS fabrication techniques in the cleanroom and the collaboration of multiple NeXus robots including two industrial robotic arms and a custom high-precision positioner for the deterministic alignment process

Manta Ray Robot

The goal of this project was to improve UAV efficiency through use of biomimetic design. This was achieved through the application of a hydraulically actuated soft robotic fin. Drawing inspiration from the manta ray, a custom actuator was developed to achieve a feasible, lifelike locomotion method. The actuator was incorporated into a prototype robot to assess the performance and ease of integration

A soft, sensorized gripper for delicate harvesting of small fruits

Harvesting fruits and vegetables is a complex task worth to be fully automated with robotic systems. It involves several precision tasks that have to be performed with accuracy and the appropriate amount of force. Classical mechanical grippers, due to the complex control and stiffness, cannot always be used to harvest fruits and vegetables. Instead, the use of soft materials could provide a visible advancement. In this work, we propose a soft, sensorized gripper for harvesting applications. The sensing is performed by tracking a set of markers integrated into the soft part of the gripper. Different machine learning-based approaches have been used to map the markers’ position and dimensions into forces in order to perform a close-loop control of the gripper. Results show that force can be measured with an error of 2.6% in a range from 0 to 4 N. The gripper was integrated into a robotic arm having an external vision system used to detect plants and fruits (strawberries in our case scenario). As a proof of concept, we evaluated the performance of the robotic system in a laboratory scenario. Plant and fruit identification reached a positive rate of 98.2% and 92.4%, respectively, while the correct picking of the fruits, by removing it from the stalk without a direct cut, achieved an 82% of successful rate