Search

The key driving factors in using humans and robots in collaborative applications for assembly processes are to reduce assembly time, cost and to improve the human working environment from an ergonomic viewpoint. Currently, there are limited automated procedures in assembly operations in house construction because the traditional type of assembly process depends entirely on manpower. This is common in the assembly process in different industries since assembly is one of the most demanding and intense manufacturing processes, and it is difficult to automate. This paper presents a case study on the implementation of human-robot collaboration for window assembly by way of an offline robot programming simulation. A self-adaptive software architecture that runs on a real-time target machine is also proposed for robotic window assembly. The window assembly method that will be used in this study is called “Click-In” and is manufactured by Fixture System Sweden AB. Apart from robot simulations, detailed suggestions are given for building a pilot cell for robot window assembly. The case study presented in this paper has both economical and ergonomic goals. The economic goal is to reduce the assembly time which will lead to an increase in window production. By introducing human-robot collaboration, operators do not need to perform uncomfortable assembly operations—rather the robot will perform these un-ergonomic operations. The feasibility of both goals is verified with offline robot programming simulation.

Over time, maintenance methods have developed following the dynamic manufacturers’ demands. Now, with the coming industrial revolution, new maintenance approaches have to be developed to fulfil the new demands of future industry, as well as to allow companies to benefit from technological advances. Therefore, the research question of this study is: how to develop a maintenance approach for factories of the future? To answer this question, this thesis proposes tools to identify and prioritise maintenance related problems that impact company’s profitability. It explores designing and implementation of a digitalised maintenance approach for future factories. Furthermore, it investigates tools and methods to collect data efficiently by sensors. The results achieved in this thesis are 1) a mathematical representation and application of a model that identifies and prioritises causes of deficiencies in production processes, 2) a model that identifies and prioritises failures that impact the competitive advantages and profitability of companies, 3) characterisation of a maintenance approach for future factories, 4) frameworks that could be utilised to develop a maintenance approach for future factories, as well as, guidelines that help to design this approach, 5) guidelines for the integration of digitalised maintenance with the database of other working areas, 6) an algorithm for adaptive sampling for sensors, as well as, a proposal for a generic software architecture to facilitate designing, modelling and implementation of adaptive sampling algorithms. The conclusion of this thesis confirms previous findings that maintenance has an impact on companies’ competitive advantages, other working areas and profitability. To design and implement a maintenance system, its elements should be extracted from the primary objective of maintenance. These elements should be then allocated in a suitable architecture and their mechanism should also be defined. Prior to implementation and integration, mapping the concept design to production problems can be used to examine its performance. An approach to collect data efficiently by sensors is to use adaptive sampling. The developed adaptive algorithm and the reference software framework for adaptive sampling algorithms could be used for this purpose.

The PreCoM system will consist of different modules, namely the PreCoM sense module for data collection, pre-processing and communication, PreCoM AI for further data analysis and predictive analytics, PreCoM AR/PLIV for supporting the maintenance actions by applying innovative augmented reality technology and PreCoM connect for integrating all before mentioned modules into production planning and maintenance planning. This deliverable describes and illustrates data-driven statistical models to improve the diagnosis and prediction in the presence of uncertainty (different types of machines or components, working conditions, etc.). We present the statistical model and we validate it by conducting a proof of concept with two well-known databases: MIMOSA and Mill-NASA. For the MIMOSA database, two software tools will be introduced. First tool is PreVib, a software module which implements a mechanistic model for predicting the vibration level, when using vibration-based maintenance (VBM). Its aim is to detect deviations in the condition of a component at an early stage in order to act for keeping the probability of failure of machine/significant component low until a condition-based replacement is done. The second software tool is ProLife, an implementation of a probabilistic approach. The goal of ProLife is to assess the probability of failure of a component/equipment and the residual lifetime of a component/equipment when its condition monitoring level, e.g. vibration level is significantly high. These two software modules will be used in PreCoM for rotating components. For the Mill-NASA dataset, we develop statistical models using two approaches. The first model is based in time series analysis (TSA). TSA can predict the time when a variable of interest (target parameter such as vibration in a critical component, such as a cutting tool) overcomes a specific threshold. This event may suggest tool replacement or a specific maintenance action because of an excessive wear or changes in product quality. The second model is based in lifetime data analysis (LTA) techniques, such as proportional hazard (PH) models or accelerated failure time (AFT) models. We apply these models to estimate relevant parameters like, for instance, the mean residual lifetime (MRL) depending on working conditions and the probability of failure at some future time. We also apply LTA methods to predict the remaining operation time of the machine. Both LTA and TSA approaches will be used for non-rotating components. The proof of concept illustrates how the proposed statistical models can be applied to real data. The statistical models should be evaluated on the data gathered at the demonstration companies of the PreCoM project as soon as they become available.

Modern manufacturing are exposed to rapid and dynamic changes in operating conditions, market demands, national and international competition. Therefore, new generations and concepts of maintenance techniques are necessary to overcome the deficiencies in the current techniques. The purpose of this paper is to propse a maintenance model that is automatic, digitzalised and intelligent using Cyber Physical System (CPS-Maint). In this paper, we presented comparable maintenance approaches and described their shortages. Thereafter, we developed a new maintenance model. Then, A simulation using LabVIEW is conducted to examin the model and its behavior. The results showed that the required tasks are performed with no conflict among its components. The major result is the development of CPS-Maint and the major conclusion is applying CPS-Maint fulfills industry needs. This paper provides a framework that could help developers and researchers in desining and developing an automatic and smart maintenance system. Ej belagd 190415

In today's high market competition, industries attempt adapt new technologies retain their market share. With technology advancement in factories, maintenance methods are developed to suit the new manufacturers. demands. Now, with the Industry 4.0, new maintenance techniques have to be developed to fulfill the new demands which we refer to as Maintenance 4.0. Each currently used maintenance technique has its own advantages and disadvantages. Until now it is unclear if these techniques are suitable for Industry 4.0. This study shows how to identify the maintenance technique that is the most suitable to be further developed for Industry 4.0. In this paper, the tasks and features of Maintenance 4.0 are identified, and the suitability of the most popular maintenance techniques is examined with respect to Industry 4.0 demands. This is done by using Multiple Attribute Decision Making combined with the Simple Additive Weight. The results show that Total Quality Maintenance(TQMain) and then Condition Based Maintenance(CBM) are the highest ranked among the examined maintenance techniques, and therefore it is concluded that these maintenance techniques could be used as a based to develop Maintenance 4.0. PreCoM

With the recent advances of manufacturing technologies, referred to as Industry 4.0, maintenance approaches have to be developed to fulfill the new demands. The technological complexity associated to Industry 4.0 makes designing maintenance solutions particularly challenging. This paper proposes a novel maintenance framework leveraging principles from self-adaptation and software architecture. The framework was tested in an operational scenario where the vibration of electrical motors in a plant needs to be managed; the results showed a proper operation. As a conclusion, the proposed framework could be used to develop maintenance systems for Industry 4.0.

The objective of this thesis is to develop a maintenance approach that fulfills the requirements of Industry 4.0. It explores the role and importance of maintenance activities in today’s industry. Then, it develops the features and tasks required to be performed by maintenance to fulfill the demands of Industry 4.0. Finally, it develops a reference model to be used in designing maintenance system for Industry 4.0. To perform these studies, real data were collected and applied as well as a typical scenario was implemented. The results achieved in the papers of this thesis are 1) a mathematical representation and application of a model that identifies, analyses and prioritizes economic weakness in working areas related to production, 2) a model that analyses, identifies and prioritizes failures that impact the competitive advantages and profitability of companies, 3) characterization of a suitable maintenance technique for Industry 4.0 and 4) a reference model i.e. a framework, that could be utilized to develop a maintenance approach for Industry 4.0. The conclusion of this thesis confirms that maintenance has a significant impact on companies’ competitive advantages, other working areas and profitability. To achieve a suitable maintenance technique for Industry 4.0, this technique must be able to monitor, diagnose, prognosis, schedule, assist in execution and present the relevant information. In order to perform these tasks several features must be acquired, the most important features are to be: digitized, automated, intelligent, able to communicate with other systems for data gathering and monitoring, openness, detect deviation in the condition at an early stage, cost- effective, flexible for adding new CM techniques, provide accurate decisions and scalable. The developed framework could be used as a base to design a maintenance system for Industry 4.0. This study contributes to our understanding of the maintenance importance in today’s industry and how to develop a maintenance approach for Industry 4.0.