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

Uses and applications of artificial intelligence in manufacturing

The purpose of the THESIS is to provide engineers and personnels with a overview of the concepts that underline Artificial Intelligence and Expert Systems. Artificial Intelligence is concerned with the developments of theories and techniques required to provide a computational engine with the abilities to perceive, think and act, in an intelligent manner in a complex environment. Expert system is branch of Artificial Intelligence where the methods of reasoning emulate those of human experts. Artificial Intelligence derives it\u27s power from its ability to represent complex forms of knowledge, some of it common sense, heuristic and symbolic, and the ability to apply the knowledge in searching for solutions. The Thesis will review : The components of an intelligent system, The basics of knowledge representation, Search based problem solving methods, Expert system technologies, Uses and applications of AI in various manufacturing areas like Design, Process Planning, Production Management, Energy Management, Quality Assurance, Manufacturing Simulation, Robotics, Machine Vision etc. Prime objectives of the Thesis are to understand the basic concepts underlying Artificial Intelligence and be able to identify where the technology may be applied in the field of Manufacturing Engineering

Modular Machining Line Design and Reconfiguration: Some Optimization Methods

http://www.intechopen.com/articles/show/title/modular_machining_line_design_and_reconfiguration__some_optimization_methodsInternational audienc

Investigation of a Neural Network Methodology to Predict Transient Performance in Fms

Most rapid analytical evaluative models for Flexible Manufacturing Systems (FMSs) are based on the steady-state performance. There is a practical need to develop robust, easy to construct, and transportable transient-state evaluative models for FMSs. This study proposes an ANN based metamodeling framework that can capture various post disruption system behaviors of FMS. The proposed ANN based meta-modeling scheme consists of a hierarchical taxonomy of mutilple ANNs. Each set of ANNs collectively represents a different part of the underlying system modeling domain. The taxonomical arrangement of multiple ANNs overcomes shortcomings often found in single ANN based meta-modeling schemes. These shortcomings are generally related to the limited knowledge acquisition capability of these schemes. The study uses an Extend based discrete simulation model that is built after an experimental FMS with a limited disruption trigger and handling capabilities. The simulation model is used to study various post-disruption behaviors by a given FMS and to study the feasibility of the proposed modeling scheme as a viable means to provide "lookahead" capability for a low level controller.Findings and Conclusions: The proposed ANN based metamodeling approach using multiple ANNs, in a taxonomically organized modeling structure, is an efficient way to capture multiple target performance index observation processes with a similar overall post-disruption behavior pattern. Despite its accuracy issues, this methodology was proven especially effective when it has to deal with noisy time series such as TIS at observation under a data rich environment. The study is to prove that the proposed methodology could be a viable means to model transient system behaviors. As long as individual observation processes of the selected performance index can keep their variances smaller among themselves, the accuracy of the overall model would be acceptable. This non-parametric performance modeling technique using hierarchically organized multiple ANNs, is worth further investigation.Industrial Engineering & Managemen

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Multiple Criteria Decision Support; Proceedings of an International Workshop, Helsinki, Finland, August 7-11, 1989

Multiple Criteria Decision Making has been an important and active research area for some 20 years. In the 1970's, research focused on the theory of multiple objective mathematical programming and on procedures for solving multiple objective mathematical programming problems. During the 1980's, a shift in emphasis towards multiple criteria decision support was observed. Accordingly, much research has focused on the user interface, the behavioral foundations of decision making, and on supporting the entire decision-making process from problem structuring to solution implementation. Because of the shift in research emphasis the authors decided to make "Multiple Criteria Decision Support" the theme for the International Workshop, which was held at Suomen Saeaestoepankkiopisto in Espoo, Finland. The Workshop was organized by the Helsinki School of Economics, and sponsored by the Helsinki School of Economics and IIASA, Austria. This volume provides an up-to-date coverage of the theory and practice of multiple criteria decision support. The authors trust that it will serve the research community as well as the previously published Conference Proceedings based on IIASA Workshops

Modeling production configuration using nested colored object-oriented Petri-nets with changeable structures

Configuring production processes based on process platforms has been well recognized as an effective means for companies to provide product variety while maintaining mass production efficiency. The production processes of product families involve diverse variations in manufacturing and assembly processes resulted from a large variety of component parts and assemblies. This paper develops a multilevel system of nested colored object-oriented Petri nets with changeable structures to model the configuration of production processes. To capture the semantics associated with production configuration decisions, some unique modeling mechanisms are employed, including colored Petri nets, object-oriented Petri nets, changeable Petri net structures, and net nesting. The modeling formalism comprises resource nets, manufacturing nets, assembly nets and process nets. The paper demonstrates how these net definitions are applied to the specification of production process variants at different levels of abstraction. Also reported is a case study in an electronics company. The system model is further analyzed with focus on conflict prevention and deadlock detection