Transforming Traditional Production Systems into Smart Production Systems
Permanent URI for this collection
Browse
Recent Submissions
1 - 6 of 6
-
ItemAdvances in Automated Generation of Convolutional Neural Networks from Synthetic Data in Industrial Environments( 2020-01-07)The usage of convolutional neural networks has revolutionized data processing and its application in the industry during the last few years. Especially detection in images, a historically hard task to automate is now available on every smart phone. Nonetheless, this technology has not yet spread in the industry of car production, where lots of visual tests and quality checks are still performed manually. Even though the vision capabilities convolutional neural networks can give machines are already respectable, they still need well prepared training data that is costly and time-consuming to produce. This paper describes our effort to test and improve a system to automatically synthesize training images. This existing system renders computer aided design models into scenes and out of that produces realistic images and corresponding labels. Two new models, Single Shot Detector and RetinaNet are retrained under the use of distractors and then tested against each other. The better performing RetinaNet is then tested for performance under training with a variety of datasets from different domains in order to observe the models strength and weakness under domain shifts. These domains are real photographs, rendered models and images of objects cut and pasted into different backgrounds. The results show that the model trained with a mixture of all domains performs best.
-
ItemCreation of an Experimental Engineering Toolbox for the Digital Transformation of Manual Jet Engine Assembly( 2020-01-07)The fast and safe motion of goods and people is one of the foundations of the modern world. Jet aircraft is the fastest transport at the moment along with high-speed trains. Accordingly, both production and maintenance of aircrafts are an important task of our modern industrial environment as well. Modern aircraft engines require appropriate care and understanding of design and manufacturing. This is even more important, as the production of aerospace engines remains a manual process in many cases with limited data sources. Thus, quality control will need to take into account verification of manufacturing and assembly steps through specific checks and controls whilst implementing additional data sources. Automation of tasks still is at a low level. In this article a review of the challenges with regard to controls, automation and process and technical understanding for aerospace engine production and repair is provided. As this requires the collaboration of many teams and partners, an improvement and step change towards deeper understanding and process efficiency is required. As many operations remain manual, innovations for how humans interact with the technology and collaborate with an industrial environment are needed. The project in this article demonstrates the creation and usage of the proposed solutions for collaboration, troubleshooting and error correction.
-
ItemTransformation towards Human Workforce 4.0 through the Qualification Model LTA-FIT( 2020-01-07)Even though digitization is a topic of high interest and promises remarkable benefits for the German manufacturing industry, it is still in its development phase. Thus, necessities arising from it are not predictable, yet. A great obstacle that will occur is the appropriate qualification of employees at all levels, sectors and firm sizes. The integration of new technologies will have radical impact on the role and job profile of all staff members as they alter the requirements across the whole value chain and lead to new processes. Latest studies showed that an insufficient qualification of employees is one of the greatest challenges for a successful digitization. Thus, to regain a globally leading position in the manufacturing industry German companies, especially small and medium sized enterprises (SME), need to qualify their employees appropriately. The LTA-FIT model is an approach that aims to face these challenges.
-
ItemTrain-the-Trainer Concept for the “Industrie 4.0-CheckUp”( 2020-01-07)The digitalization of society is causing companies’ environmental conditions to change. New customer demands, a change in employee thinking and a market situation altered by new competitors are making the digital transformation of companies a necessity. Identifying capabilities in a company, recommending actions and then implementing actions necessitates ascertaining the company’s level of development in terms of digital transformation. A multitude of capability maturity models and different approaches to use exist to meet the needs of SMEs and large companies. Since the dimensions of Industrie 4.0 are understood slightly differently all over the world, this paper formulates a train-the-trainer approach that ensures a global baseline understanding based on a dedicated capability maturity model. The paper concludes with a discussion of future applications for this method.
-
ItemDigital Transformation Strategies for Achieving Operational Excellence: a Cross-Country Evaluation( 2020-01-07)The last decade, the manufacturing industry has been concerned with digital transformation as a new lever for competitiveness. Nevertheless, the industrial digital transformation remains an emerging topic, which is not well understood academically and in terms of its practical implications. Due to different contextual factors and national development policies, companies from different countries adopted different strategies for supporting it. These often led to peculiar effects on the companies’ operations. This paper is investigating how four large manufacturers - two from Italy and two from Denmark – are strategically addressing this agenda. The aim of this research is to offer a perspective on technology transformation in diverse contexts by studying the different digital transformation strategies adopted by these companies and discussing their dependencies on contextual factors.
-
Item