Koncepcijsko projektiranje inteligentnog unutarnjeg transporta materijala korištenjem umjetne inteligencije

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matlab© software package is used for developing genetic algorithms, manufacturing process simulation, implementing search algorithms and neural network training. The obtained paths are tested by means of the Khepera II mobile robot system within a static laboratory model of manufacturing environment. The experiment results clearly show that an intelligent mobile robot can follow paths generated by using genetic algorithms as well as learn and predict optimal material transport flows thanks to using neural networks. The achieved positioning error of the mobile robot indicates that the conceptual design approach based on the axiomatic design theory can be used for designing the material transport and handling tasks in intelligent manufacturing systems.Pouzdan i efikasan transport materijala je jedan od ključnih zahtjeva koji utječe na povećanje produktivnosti u industriji. Iz tog razloga, u radu su predložena dva pristupa za inteligentan transport materijala korištenjem mobilnog robota. Prvi pristup se zasniva na primjeni genetskih algoritama za optimizaciju tehnoloških procesa. Optimalna putanja se dobiva korištenjem optimalnih tehnoloških procesa i genetskih algoritama za vremensko planiranje, uz minimalno vrijeme kao kriterij. Drugi pristup je temeljen na primjeni teorije grafova za generiranje putanja i neuronskih mreža za učenje generirane putanje. Matlab© softverski paket je korišten za razvoj genetskih algoritama, simulaciju tehnoloških procesa, implementaciju algoritama pretraživanja i obučavanje neuronskih mreža. Dobivene putanje su testirane pomoću Khepera II mobilnog robota u statičkom laboratorijskom modelu tehnološkog okruženja. Eksperimentalni rezultati pokazuju kako inteligentni mobilni robot prati putanje generirane korištenjem genetskih algoritama, kao i da uči i predviđa optimalne tokove materijala zahvaljujući neuronskim mrežama. Ostvarena pogreška pozicioniranja mobilnog robota ukazuje da se koncepcijski pristup baziran na aksiomatskoj teoriji projektiranja može koristiti u projektiranju transporta i manipulacije u inteligentnom tehnološkom sustavu

Towards a conceptual design of intelligent material transport using artificial intelligence

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matla

Towards a conceptual design of intelligent material transport using artificial intelligence

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matla

Scheduling of Flexible Manufacturing Systems using Intelligent heuristic search algorithm (IHSA*)

The complete scheduling of FMS includes two independent processes: sequencing of jobs and scheduling those prioritized jobs. In a flow shop or a Progressive type FMS, scheduling problem involves sequencing of ‘n’ jobs on ‘m’ machines with minimum makespan. Intelligent heuristic search algorithm (IHSA*) is used in this paper, which ensure to find an optimal solution for flow-shop problem involving arbitrary number of machines and jobs provided the job sequence is same on each machine. The initial version of IHSA* is based on the A* algorithm. The final version of IHSA* is the modification of the initial IHSA*. There are three modifications: first modification concerned with the selection of an admissible heuristic function, second modification concerned with the procedure which determine heuristic estimate as the search progresses and the third modification concerned with the searching of multiple optimal solution, if they exist. Both version of the IHSA* are presented in this paper with an example which illustrates the use of both

Simulation and optimisation of a specific flexible manufacturing system.

As current market competition evolves, most companies intend to increase their options for product customisation and accelerate their product upgrading. Correspondingly, manufacturers have to face the increasing size of product family, shortened product life cycle or rapid product/process change. Therefore, Flexible Manufacturing Systems (FMS) have been introduced that uses advanced machines and efficient transport systems to produce multiple products at the same time. However, an FMS can be complicated to manage because of the increased variability in products and processes. The research aims to develop manufacturing simulation and optimisation techniques for a FMS. This research will integrate Discrete Event Simulation (DES) and multi-objective optimisation approach to address the complexity and flexibility within an agile manufacturing environment. Due to the complexity of FMS, most current FMS optimisation research has engaged with FMS production problems separately without considering other inter-related problems in the same system such as dealing with operation sequence problem without considering Level of Flexibility (LoF), thus it is hard for the solution to provide a prospective impact for the whole system. There are very few real-world FMS implementations that are available to literatures, making it difficult to build and verify the models within a complete ecosystem. Consequently, most of the models in the research are oversimplified. Therefore, this research aims to develop a method to optimise FMS production considering the overall system, by having access to an FMS industrial implementation. This research contributes to knowledge in four main areas, namely, (1) the interactions of FMS production problems have been investigated, (2) a framework has been developed to integrate the simulation and optimisation for FMS to enable optimisation algorithms working with DES models effectively, (3) a comprehensive FMS simulation model has been built and validated on the industrial shop floor and (4) multi-objective optimisation has been applied to the FMS scheduling problem, considering interactions with other problems. Based on the results and limitations of this research, real-time simulation, mock-up FMS and improve computational efficiency are suggested for future work.PhD in Manufacturin

Réduction du comportement myope dans le contrôle des FMS : une approche semi-hétérarchique basée sur la simulation-optimisation

Heterarchical-based control for flexible manufacturing systems (FMS) localizes control capabilities in decisional entities (DE), resulting in highly reactive and low complex control architectures. However, these architectures present myopic behavior since DEs have limited visibility of other DEs and their behavior, making difficult to ensure certain global performance. This dissertation focuses on reducing myopic behavior. At first, a definition and a typology of myopic behavior in FMS is proposed. In this thesis, myopic behavior is dealt explicitly so global performance can be improved. Thus, we propose a semi-heterarchical architecture in which a global decisional entity (GDE) deals with different kinds of myopic decisions using simulation-based optimization (SbOs). Different optimization techniques can be used so myopic decisions can be dealt individually, favoring GDE modularity. Then, the SbOs can adopt different roles, being possible to reduce myopic behavior in different ways. More, it is also possible to grant local decisional entities with different autonomy levels by applying different interaction modes. In order to balance reactivity and global performance, our approach accepts configurations in which some myopic behaviors are reduced and others are accepted. Our approach was instantiated to control the assembly cell at Valenciennes AIPPRIMECA center. Simulation results showed that the proposed architecture reduces myopic behavior whereby it strikes a balance between reactivity and global performance. The real implementation on the assembly cell verified the effectiveness of our approach under realistic dynamic scenarios, and promising results were obtained.Le contrôle hétérarchique des systèmes de production flexibles (FMS) préconise un contrôle peu complexe et hautement réactif supporté par des entités décisionnelles locales (DEs). En dépit d'avancées prometteuses, ces architectures présentent un comportement myope car les DEs ont une visibilité informationnelle limitée sue les autres DEs, ce qui rend difficile la garantie d'une performance globale minimum. Cette thèse se concentre sur les approches permettant de réduire cette myopie. D'abord, une définition et une typologie de cette myopie dans les FMS sont proposées. Ensuite, nous proposons de traiter explicitement le comportement myope avec une architecture semi-hétérarchique. Dans celle-ci, une entité décisionnelle globale (GDE) traite différents types de décisions myopes à l'aide des différentes techniques d'optimisation basée sur la simulation (SbO). De plus, les SbO peuvent adopter plusieurs rôles, permettant de réduire le comportement myope de plusieurs façons. Il est également possible d'avoir plusieurs niveaux d'autonomie en appliquant différents modes d'interaction. Ainsi, notre approche accepte des configurations dans lesquelles certains comportements myopes sont réduits et d'autres sont acceptés. Notre approche a été instanciée pour contrôler la cellule flexible AIP- PRIMECA de l'Université de Valenciennes. Les résultats des simulations ont montré que l'architecture proposée peut réduire les comportements myopes en établissant un équilibre entre la réactivité et la performance globale. Des expérimentations réelles ont été réalisées sur la cellule AIP-PRIMECA pour des scenarios dynamiques et des résultats prometteurs ont été obtenus

Integrated process planning and scheduling using genetic algorithms

Projektiranje tehnoloških procesa i planiranje predstavljaju dvije najvažnije funkcije svakog proizvodnog procesa. Tradicionalno se one smatraju dvjema odvojenim funkcijama. U ovom se radu predlaže Genetički Algoritam (GA) za integraciju ovih aktivnosti, gdje se simultano odvija izbor najboljeg tehnološkog procesa i vremenski plan poslova u pogonu. U radu se za rješavanje te vrste problema predstavlja pristup zasnovan na proračunskoj tablici neovisnog područja. U modelu se razmatraju odnosi prvenstva u izvođenju poslova na temelju kojih se donosi implicitno predstavljanje mogućih planova za izvršenje svakog posla. Zbog provjere izvršenja i ostvarivosti predstavljenog pristupa, predloženi se algoritam provjeravao na nizu referentnih problema prilagođenih iz ranije objavljene literature. Eksperimentalni rezultati pokazuju da se predloženim pristupom mogu učinkoviti postići optimalna ili njima blizu rješenja za probleme prilagođene iz literature. Također je pokazano da predloženi algoritam ima opću namjenu i može se primijeniti za optimizaciju bilo koje objektivne funkcije bez promjene modela ili osnovne GA rutine.Process planning and scheduling are two of the most important functions in any manufacturing system. Traditionally process planning and scheduling are considered as two separate functions. In this paper a Genetic Algorithm (GA) for integrated process planning and scheduling is proposed where selection of the best process plan and scheduling of jobs in a job shop environment are done simultaneously. In the proposed approach a domain independent spreadsheet based approach is presented to solve this class of problems. The precedence relations among job operations are considered in the model, based on which implicit representation of a feasible process plans for each job can be done. To verify the performance and feasibility of the presented approach, the proposed algorithm has been evaluated against a number of benchmark problems that have been adapted from the previously published literature. The experimental results show that the proposed approach can efficiently achieve optimal or near-optimal solutions for the problems adopted from literature. It is also demonstrated that the proposed algorithm is of general purpose in application and could be used for the optimisation of any objective function without changing the model or the basic GA routine

Co-evolution in Manufacturing Systems Inspired by Biological Analogy

The artificial world experiences continuous changes that result in the evolution of design features of products and the capabilities of the corresponding manufacturing systems similar to the changes of species in the natural world. The idea of simulating the artificial world, based on the analogy between the symbiotic behaviour of products and manufacturing systems and the biological co-evolution of different species in nature, is expressed by a model and novel hypotheses regarding manufacturing co-evolution mechanism, preserving that co-evolution and using it for future planning and prediction. Biological analogy is also employed to drive the mathematical formulation of the model and its algorithms. Cladistics, a biological classification tool, is adapted and used to realize evolution trends of products and systems and their symbiosis was illustrated using another biological tool, tree reconciliation. A new mathematical method was developed to realize the co-development relationships between product features and manufacturing capabilities. It has been used for synthesizing / predicting new species of systems and products. The developed model was validated using machining and assembly case studies. Results have proven the proposed hypotheses, demonstrated the presence of manufacturing symbiosis and made predictions and synthesized new systems and products. The model has been also adapted for use in different applications such as; system layout design, identifying sustainable design features and products family redesign to promote modularity. The co-evolution model is significant as it closes the loop connecting products and systems to learn from their shared past development and predict their intertwined future, unlike available unidirectional design strategies. The economic life of manufacturing systems can be extended by better utilizing their available capabilities, since the co-evolution model directs products - systems development towards reaching a perfect co-evolution state. This research presents original ideas expressed by innovative co-evolution hypotheses in manufacturing, new mathematical model and algorithms, and demonstrates its advantages and benefits in a wide range of applications

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation