Good Production Cycles for Circular Robotic Cells

In this paper, we study cyclic production for throughput optimization in robotic flow-shops. We are focusing on simple production cycles. Robotic cells can have a linear or a circular layout: most classical results on linear cells cannot be extended to circular cells, making it difficult to quantify the potential gain brought by the latter configuration. Moreover, though the problem of finding the best one part production cycle is polynomial for linear cells, it is NP-hard for circular cells. We consider the special case of circular balanced cells. We first consider three basic production cycles, and focus on one which is specific to circular cells, for which we establish the expression of the cycle time. Then, we provide a counterexample to a classical conjecture still open in this configuration. Finally, based on computational experiments, we make a conjecture on the dominance of a family of cycle, which could lead to a polynomial algorithm for finding the best 1-cycle for circular balanced cells

Scheduling in flexible robotic manufacturing cells

Cataloged from PDF version of article.The focus of this thesis is the scheduling problems arising in robotic cells which consist of a number of machines and a material handling robot. The machines used in such systems for metal cutting industries are highly flexible CNC machines. Although flexibility is the key term that affects the performance of these systems, the current literature ignores this. As a consequence, the problems considered in the current literature are either too limiting or the provided solutions are suboptimal for the flexible systems. This thesis analyzes different robotic cell configurations with different sources of flexibility. This study is the first one to consider operation allocation problems and controllable processing times as well as some design problems and bicriteria models in the context of robotic cell scheduling. Also, a new class of robot move cycles is defined, which is overlooked in the existing literature. Optimal solutions are provided for solvable cases, whereas complexity analyses and efficient heuristic algorithms are provided for the remaining problems.Gültekin, HakanPh.D

Cyclic scheduling of a 2-machine robotic cell with tooling constraints

In this study, we deal with the robotic cell scheduling problem with two machines and identical parts. In an ideal FMS, CNC machines are capable of performing all the required operations as long as the required tools are stored in their tool magazines. However, this assumption may be unrealistic at times since the tool magazines have limited capacity and in many practical instances the required number of tools exceeds this capacity. In this respect, our study assumes that some operations can only be processed on the first machine while some others can only be processed on the second machine due to tooling constraints. Remaining operations can be processed on either machine. The problem is to find the allocation of the remaining operations to the machines and the optimal robot move cycle that jointly minimize the cycle time. We prove that the optimal solution is either a 1-unit or a 2-unit robot move cycle and we present the regions of optimality. Finally, a sensitivity analysis on the results is conducted. © 2005 Elsevier B.V. All rights reserved

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments

USING COEVOLUTION IN COMPLEX DOMAINS

Genetic Algorithms is a computational model inspired by Darwin's theory of evolution. It has a broad range of applications from function optimization to solving robotic control problems. Coevolution is an extension of Genetic Algorithms in which more than one population is evolved at the same time. Coevolution can be done in two ways: cooperatively, in which populations jointly try to solve an evolutionary problem, or competitively. Coevolution has been shown to be useful in solving many problems, yet its application in complex domains still needs to be demonstrated.Robotic soccer is a complex domain that has a dynamic and noisy environment. Many Reinforcement Learning techniques have been applied to the robotic soccer domain, since it is a great test bed for many machine learning methods. However, the success of Reinforcement Learning methods has been limited due to the huge state space of the domain. Evolutionary Algorithms have also been used to tackle this domain; nevertheless, their application has been limited to a small subset of the domain, and no attempt has been shown to be successful in acting on solving the whole problem.This thesis will try to answer the question of whether coevolution can be applied successfully to complex domains. Three techniques are introduced to tackle the robotic soccer problem. First, an incremental learning algorithm is used to achieve a desirable performance of some soccer tasks. Second, a hierarchical coevolution paradigm is introduced to allow coevolution to scale up in solving the problem. Third, an orchestration mechanism is utilized to manage the learning processes