An early-stage decision-support framework for the implementation of intelligent automation

The constant pressure on manufacturing companies to improve productivity, reduce the lead time and progress in quality requires new technological developments and adoption.The rapid development of smart technology and robotics and autonomous systems (RAS) technology has a profound impact on manufacturing automation and might determine winners and losers of the next generation’s manufacturing competition. Simultaneously, recent smart technology developments in the areas enable an automation response to new production paradigms such as mass customisation and product-lifecycle considerations in the context of Industry 4.0. New paradigms, like mass customisation, increased both the complexity of the tasks and the risk due to smart technology integration. From a manufacturing automation perspective, intelligent automation has been identified as a possible response to arising demands. The presented research aims to support the industrial uptake of intelligent automation into manufacturing businesses by quantifying risks at the early design stage and business case development. An early-stage decision-support framework for the implementation of intelligent automation in manufacturing businesses is presented in this thesis.The framework is informed by an extensive literature review, updated and verified with surveys and workshops to add to the knowledge base due to the rapid development of the associated technologies. A paradigm shift from cost to a risk-modelling perspective is proposed to provide a more flexible and generic approach applicable throughout the current technology landscape. The proposed probabilistic decision-support framework consists of three parts:• A clustering algorithm to identify the manufacturing functions in manual processes from task analysis to mitigate early-stage design uncertainties• A Bayesian Belief Network (BBN) informed by an expert elicitation via the DELPHI method, where the identified functions become the unit of analysis.• A Markov-Chain Monte-Carlo method modelling the effects of uncertainties on the critical success factors to address issues of factor interdependencies after expert elicitation.Based on the overall decision framework a toolbox was developed in Microsoft Excel. Five different case studies are used to test and validate the framework. Evaluation of the results derived from the toolbox from the industrial feedback suggests a positive validation for commercial use. The main contributions to knowledge in the presented thesis arise from the following four points:• Early-stage decision-support framework for business case evaluation of intelligent automation.• Translating manual tasks to automation function via a novel clustering approach• Application of a Markov-Chain Monte-Carlo Method to simulate correlation between decision criteria• Causal relationship among Critical Success Factors has been established from business and technical perspectives.The implications on practise might be promising. The feedback arising from the created tool was promising from the industry, and a practical realisation of the decision-support tool seems to be desired from an industrial point of view.With respect to further work, the decision-support tool might have established a ground to analyse a human task automatically for automation purposes. The established clustering mechanisms and the related attributes could be connected to sensorial data and analyse a manufacturing task autonomously without the subjective input of task analysis experts. To enable such an autonomous process, however, the psychophysiological understanding must be increased in the future.</div

Design and Management of Manufacturing Systems

Although the design and management of manufacturing systems have been explored in the literature for many years now, they still remain topical problems in the current scientific research. The changing market trends, globalization, the constant pressure to reduce production costs, and technical and technological progress make it necessary to search for new manufacturing methods and ways of organizing them, and to modify manufacturing system design paradigms. This book presents current research in different areas connected with the design and management of manufacturing systems and covers such subject areas as: methods supporting the design of manufacturing systems, methods of improving maintenance processes in companies, the design and improvement of manufacturing processes, the control of production processes in modern manufacturing systems production methods and techniques used in modern manufacturing systems and environmental aspects of production and their impact on the design and management of manufacturing systems. The wide range of research findings reported in this book confirms that the design of manufacturing systems is a complex problem and that the achievement of goals set for modern manufacturing systems requires interdisciplinary knowledge and the simultaneous design of the product, process and system, as well as the knowledge of modern manufacturing and organizational methods and techniques

Soft Sensor-based Servo Press Monitoring

The force that a servo press exerts forming a workpiece is one the most important magnitudes in any metal forming operation. The process force, along with the characteristics of the die, is what shapes the workpiece. When the process force is greater than the maximum force for which the servo press was designed, the servo press integrity can be damaged. Therefore, the knowledge of the process force is of great interest for both, press manufacturers and users. As such, the metal forming sector is seeking systems that can monitor the process force and the operation of the servo press to analyse process’s performance and predict future deviations in the forming operation. Servo press users want to guarantee the quality of the formed parts and reduce facility downtimes due to malfunctions of the press. This dissertation addressed the monitoring of the process force and the dynamic performance of a servo press based on a model based statistical signal processing algorithm known as the dual particle filter (dPF). Initially both, the developed model of a servo press and the proposed dPF, have been experimentally evaluated and validated in a reduced scale test bench. The test bench has been designed and manufactured based on a design methodology that allows to replicate the kinematic and dynamic behaviour of different servo press facilities in the same test bench. The experimental validation has been also carried out in an industrial servo press under three different metal forming processes. The estimation results have proved the ability of the dPF to track the process force throughout the evaluated processes, obtaining a deviation lower than 5% with respect to the measured force signals at the maximum force position. The dPF algorithm has been accelerated by means of a field programmable gate array (FPGA) to achieve a real time estimation.Serbo prentsa batek pieza gordin bat eraldatzeko egindako prozesuko indarra edozein konformatu eragiketako magnitude garrantzitsuenetarikoa da. Prozesuko indarra da, trokelaren ezaugarriekin batera, pieza gordina eraldatzen duena. Prozesuko indarra prentsak diseinuaren arabera jasan dezakeena baino handiagoa bada, prentsak kalteak izan ditzake bere osotasunean. Beraz, prozesuko indarraren ezagutza interes handikoa da, prentsa egileentzat zein erabiltzaileentzat. Hori dela eta, metal eraldatzearen sektoreak prozesuko indarra eta prentsa beraren funtzionamendua monitoriza ditzaketen sistemen bila diardute, prentsaren jarduera aztertu eta eraldatzeko operazioetan etorkizunean izan daitezkeen desbideraketak aurreikusteko. Prentsa erabiltzaileek fabrikatutako piezen kalitatea bermatzea eta funtzionamendu akatsengatiko prentsaren geldialdiak murriztea bilatzen dute. Tesi honek servo prentsa baten prozesuko indarra eta portarea dinamikoaren monitorizazioa jorratzen ditu, dual particle filter (dPF) izeneko modeloetan oinarritutako seinalaren prozesamendu estadistikoko algoritmo baten bitartez. Lehenik eta behin, garatutako servo prentsaren modeloa eta proposatutako dPFa eskalatutako entsegutarako banku batean ebaluatu eta balioztatu dira. Eskalatutako entsegutarako bankua serbo prentsa desberdinen portaera zinematiko eta dinamikoa erreplikatzea ahalbidetzen duen metodologia baten bitartez diseinatu eta gauzatu da. Esperimentu bidezko balioztatzea serbo prentsa industrial batean ere gauzatu da hiru konformatuko prozesu desberdinetan. Estimazio emaitzek dPFak prozesuko indarrari jarraitzeko duen ahalmena forgatu dute, neurtutako indarrarekiko %5ekoa baino txikiagoko desbideraketa lortuz indar maximoa egiten den puntuan. dPF algoritmoa field programmable gate array (FPGA) baten bitartez azeleratu da, denbora errealeko estimazioa lortzeko.La fuerza que una servo prensa ejerce conformando una pieza es la magnitud más importante en cualquier operación de conformado. La fuerza aplicada, junto a las características del troquel, es la magnitud que da forma a la pieza. Cuando la fuerza de proceso es más grande que la fuerza máxima para la que fue diseñada la servo prensa, la integridad de ésta puede verse afectada. Por lo tanto, el conocimiento de la fuerza de proceso es de gr´an interés tanto para los fabricantes de prensas como para los usuarios de las mismas. Así pues, el sector del conformado está buscando sistemas capaces de monitorizar la fuerza de proceso y el funcionamiento de la servo prensa para analizar el proceso y predecir futuras desviaciones de las operaciones de conformado. Los usuarios de las servo prensas quieren garantizar la calidad de las piezas fabricadas y reducir las paradas de las servo prensas debidas al mal funcionamiento de las mismas. Esta tesis aborda la monitorización de la fuerza de proceso y el comportamiento dinámico de una servo prensa mediante un algoritmo de tratamiento estadístico de la señal conocido como el dual Particle Filter (dPF). Inicialmente, tanto el modelo desarrollado como el dPF propuesto han sido evaluados y validados experimentalmente en un banco de ensayos de escala reducida. El banco de ensayos ha sido diseñado y fabricado mediante una metodología de diseño que permite replicar el comportamiento cinem´atico y din´amico de distintas servo prensas en el mismo banco. La validación experimental también se ha llevado a cabo en una servo prensa industrial mediante tres procesos de conformado distintos. Los resultados de estimación han provado la habilidad del dPF para seguir la fuerza de proceso en los procesos evaluados, obteniendo una desviación menor que un 5% con respecto a las señales medidas en el punto donde se da la fuerza máxima. El algoritmo dPF ha sido acelerado mediante un filed programmable gate array (FPGA) para lograr estimaciones en tiempo real

Advances on Mechanics, Design Engineering and Manufacturing III

This open access book gathers contributions presented at the International Joint Conference on Mechanics, Design Engineering and Advanced Manufacturing (JCM 2020), held as a web conference on June 2–4, 2020. It reports on cutting-edge topics in product design and manufacturing, such as industrial methods for integrated product and process design; innovative design; and computer-aided design. Further topics covered include virtual simulation and reverse engineering; additive manufacturing; product manufacturing; engineering methods in medicine and education; representation techniques; and nautical, aeronautics and aerospace design and modeling. The book is organized into four main parts, reflecting the focus and primary themes of the conference. The contributions presented here not only provide researchers, engineers and experts in a range of industrial engineering subfields with extensive information to support their daily work; they are also intended to stimulate new research directions, advanced applications of the methods discussed and future interdisciplinary collaborations