Through improving the redundant data filtering of unreliable data filter for radio frequency identification (RFID) with sliding-window, a data filter which integrates self-adaptive sliding-window and Euclidean distance is proposed. The input data required being filtered have been shunt by considering a large number of redundant data existing in the unreliable data for RFID and the redundant data in RFID are the main filtering object with utilizing the filter based on Euclidean distance. The comparison between the results from the method proposed in this paper and previous research shows that it can improve the accuracy of the RFID for unreliable data filtering and largely reduce the redundant reading rate.

Time compression in supply chains is a crucial aspect involved in the integration of warehousing and transport operations in the manufacturing industries. Supply chain flows could be interrupted due to many sources of delays that lead to additional time in dispatching process and reduction in customer service level. The problem considered in this paper consists of long waiting times of loading vehicles inside the plant. This work presents a simulation-based study to minimize vehicle dispatching time in a steel wire plant. Value stream map is developed to present a system perspective of processes involved in the overall supply chain. Process activity mapping is completed to provide a step by step analysis of activities involved in the vehicle dispatch process. A simulation model is developed for the system and a new model is proposed to improve the delivery performance by minimizing vehicles’ waiting time.

A fundamental criterion for reusing and continuously improving knowledge in product development is ensuring that the knowledge is explicit and visual. This paper is based on the situation of an engineer-to-order (ETO) manufacturing company, where historically grown product variety and related knowledge are diffuse (tacit). Consequently, several resources are used in (re)developing derivatives of previous products rather than innovating new ones. To establish a more competitive configure-to-order (CTO) product strategy, product knowledge needs to be revealed, systemized, and structured, and thus made explicit. Hence, product-specific knowledge and product variants have been analyzed and subsequently mapped at architectural, functional, and physical levels in one unified map and tested in the form of a proof-of-concept (POC) demonstrator with the introduced SME company. The result is a product portfolio map that forms a base for defining a systemized, transparent, unified product variant overview, which can be used as a basis for implementing a cross-variant product architecture and supporting knowledge-based approaches.

We have carried out at laboratory test to study the feasibility of using thermal radiation detectors for online thermal monitoring of electrical systems in wind turbines. A 25 kW frequency converter is instrumented with a thermal camera, operating in the 8–14 μm wavelength range, and a single-pixel thermopile sensor, operating in the 4–8 μm wavelength range, to monitor the temperature development of the power electronics under various load sequences. Both systems performed satisfactorily with insignificant temperature deviations when compared to data from calibrated point contact sensor. With spatial averaging over a 7 mm × 7 mm for the camera and temporal averaging over 60 s for the thermopile sensor, we reduce the root mean square noise to 45 mK and 68 mK respectively. The low cost and simple operation of the thermopile sensor make it very attractive for condition monitoring applications, whereas the attractive feature of the camera is the possibility of multi-point or distributed temperature measurements.

This paper provides an overview of models and methods for estimation of lifetime of technical components. Although the focus in this paper is on wind turbine applications, the major content of the paper is of general nature. Thus, most of the paper content is also valid for lifetime models applied to other technical systems. The models presented and discussed in this paper are classified in different types of model classes. The main classification used in this paper divides the models in the following classes: physical models, stochastic models, data-driven models and artificial intelligence, and combined models. The paper provides an overview of different models for the different classes. Furthermore, advantages and disadvantages of the models are discussed, and the estimation of model parameters is briefly described. Finally, a number of literature examples are given in this paper, providing an overview of applications of different models on wind turbines.

To avoid ergonomic problems in the early planning stages of a production line and achieve more satisfactory planning and design, ergonomic simulation is particularly important in digital production line planning. An ergonomics analysis method is presented by using two theories: Ovako working posture analysis system (OWAS) and Burandt-Schultetus hand-arm force analysis (BSHA). The processes of ergonomics analysis and simulation are discussed based on a platform of process simulation and process designer. As an example, the paper shows how ergonomics problems are considered in production line planning to make a better choice between different production line planning schemes.

In the present work, crystal structure and formation mechanism of the secondary phase in Heusler Ni-Mn-Sn-Co materials were investigated using X-ray diffraction, scanning/transmission electron microscopy and selected-area electron diffraction techniques. Experimental results showed that the secondary phase presented in both Ni44.1Mn35.1Sn10.8Co10 as-cast bulk alloy and melt-spun ribbon, possessing a face-centered cubic (fcc) Ni17Sn3-type structure. The secondary phase in the as-cast bulk alloy was resulted from a eutectic reaction after the formation of a primary dendritic β phase during cooling. However in the melt-spun rapidly solidified ribbon, the secondary phase was largely suppressed as nano-precipitates distributed along the grain boundaries, which was attributed to a divorced eutectic reaction. The secondary phase exhibited partial amorphous state due to high local cooling rate.

Laser welding is an established manufacturing technology for a large variety of automotive applications due to its attractive properties such as low heat input, high precision and fast welding speed. Especially when welding high strength steels, which are dominantly used in today’s car body construction, the low heat input by laser welding bears significant advantages with regard to the properties of the weld seam. The exploitation of the full application potential of laser welding in mass production requires an appropriate manufacturing concept and corresponding auxiliary technologies. The present paper demonstrates the integration of laser welding into the surrounding manufacturing concepts by a modular setup with different levels of automation. This approach offers flexible solutions for individual needs thereby optimizing investment cost, labor cost and productivity. Recently available laser sources enable exceptionally high welding speed on thin gauged sheet metals but require efficient material handling concepts to utilize the full speed potential. Industrial concepts are presented offering efficient material handling and high process robustness for mass production welding.

3D quality inspection is widely applied in many industrial fields including mould design, automotive and blade manufacturing, etc. A commonly used method is to obtain the point cloud of the inspected object and make a comparison between the point cloud and the corresponding CAD model or template. Thus, it is important to align the point cloud with the template first and foremost. Moreover, for the purpose of automatization of quality inspection, this alignment process is expected to be completed without manual interference. In this paper, we propose to combine the particle swarm optimization (PSO) with iterative closest point (ICP) algorithm to achieve the automated point cloud alignment. The combination of the two algorithms can achieve a balance between the alignment speed and accuracy, and avoid the local optimal caused by bad initial position of the point cloud.

For the typical color defects of polysilicon wafers, i.e., edge discoloration, color inaccuracy and color non-uniformity, a new integrated machine vision detection method is proposed based on an HSV color model. By transforming RGB image into three-channel HSV images, the HSV model can efficiently reduce the disturbances of complex wafer textures. A fuzzy color clustering method is used to detect edge discoloration by defining membership function for each channel image. The mean-value classifying method and region growing method are used to identify the other two defects, respectively. A vision detection system is developed and applied in the production of polysilicon wafers.