359 research outputs found
Redesign of the control model at SEW-EURODRIVE B.V.
No full textSEW-EURODRIVE B.V. (SEW NL) is a manufacturer of drive products located in Rotterdam in The Netherlands and is a whole subsidiary of the German company SEW-EURODRIVE. SEW NL assembles drives and controllers. Each drive and controller is highly customizable according to the customers wishes and is assembled to order. Lead times are generally short (three workdays). Parts required for assembly of drive products can roughly be divided into two categories: eKanban parts and MRP parts. eKanban parts are small and cheap parts that are put into bins in bulk that are located at places where they are required. Whenever a bin is empty it will be scanned and new parts are ordered. MRP parts are large, more expensive and located in the internal warehouse. These parts are picked before the production order starts. Lead times of SEW NL are shorter than lead times of the supplier of SEW NL (SEW Germany), usually three workdays compared to two to four weeks of SEW Germany. Also, drives and controllers are assembled to order. Because of this, stock is required to meet the lead time requirements. In order to ensure parts never run out of stock, orders are only scheduled when all the required parts are available on the date for which the order is scheduled...Mechanical, Maritime and Materials EngineeringMarine and Transport TechnologyTransport Engineering and Logistics2014-TEL.784
Der Westen und die Wissenschaftliche Revolution
No full textThis is the author accepted manuscript.No abstrac
Facilitating beginning sewers in making the clothes that truly fit them
No full textThis IPD master thesis describes the process of developing the product-service of Maki. Maki is a startup with the focus of making the life of a beginning sewer easier. Currently, beginning sewers have a learning journey full of struggles. In this project, a design process is described that attempts to design an online learning platform for these beginning sewers. Through extensive user research, prototyping and exploring the field of sewing, the final concept was presented. Maki is a platform that provides sew-alongs where the user learns hands-on through making garments. The user first gets to design how they want the garment to be through varying modular elements, like sleeves, or collars. After this the sew-along guides them through a process where they learn to draw the pattern and sew it together. This is done with steps in text, illustration and video, so the beginning sewer is fully supported. Since Maki users have the desire to learn how to make and design the clothing that truly fit them, the sew-along teaches them how to draw their own patterns. This is done with the help of a set of printable rulers. The rulers and sew–along will help them build a skill-set that can serve as a stepping stone in making anything they want later in their learning journey. On the platform some collaboration with other users can take place. Users get the chance to inspire others with their work through sharing pictures of end-results. The sew-along also allows users to comment, so they can help each other out and a community of beginning sewers can grow.Integrated Product Desig
Development of a predictive maintenance model which provides fault identification and diagnostics on electrical gearmotor systems: An exploratory case study for SEW-Eurodrive
No full textIndustrial electric motors are at the heart of almost every industry. They are a 47 billion USD market in 2020 and consume 70% of all industrial electricity. They are generally paired with a gearbox and load, which is referred to as an electric gearmotor system. Being so essential means that it is important to avoid failures, 98% of 300 researched companies by ITIC reported a cost of 100.000 USD every hour of downtime. To detect failures a technology called fault detection and diagnosis (FDD) is used, which is a method to foresee failures or faults in a system that deteriorates over time through evaluating the state of the system. It is an extensively academically research subject, however, has hardly been adopted in industrial settings where electric gearmotor systems are applied. Thus, a FDD model was developed to provide insight, knowledge, and a practical example into the necessities of an industrial FDD model. This was achieved through conducting an analysis on the state-of-the-art of FDD in industrial settings and conducting a literature review on FDD. Based on an analysis of the conclusions a hybrid diagnostics model was developed. For the fault detection a model-based solution was used, it compared the predicted torque to the measured torque of a motor to create a health indication value. If this value crosses a pre-determined threshold an alarm would go off. For fault identification a decision tree machine learning algorithm is used to identify: blockage, bearing, gear or random failure in a system. To verify and validate the hybrid diagnostics model it was applied to a client of SEW Eurodrive where data was available of a known system. The system had a fault detection accuracy as high as 94% and could classify failures with an accuracy of 93%.Mechanical Engineering | Multi-Machine Engineerin
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