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In the process of selective assembly for precision and complex mechanical products, it is necessary to perform the selective material kitting one by one and conduct check repeatedly according to the optimal matching result to ensure quality. The traditional manual picking and checking method by Bill of Material is inefficient and laborious. Currently, there is still no consensus reached on how to better transmit information and implement the material kitting in the course of selective assembly. In this paper, we pioneer in testing the performance between mainstream methods (Pick-by-Voice, Pick-by-Light, Pick-by-AR) and traditional methods (Pick-by-Paper) in terms of task time, errors, workload, and information perception for the selective material kitting. We developed an AR (augmented reality) on the level of material individual information level and verified its applicability. Meanwhile, we enhanced the effect of material individual information expression from different visual perspectives. We found that Pick-by-AR can outperform others in terms of selective assembly, which means expanding the dimension of information expression and enhancing users' perception of information can help users quickly and accurately select parts with the same appearance but different quality characteristics fast and accurately, thus providing a viable option for material kitting in the selective assembly process. [ABSTRACT FROM AUTHOR]

Non-productive auxiliary processes affect the single part and small badge production of milling tools. The key production process grinding is inevitably linked to the auxiliary conditioning process. The time demand of those process steps decreases the overall productivity of the manufacturing process. However, today the machine operator decides on conditioning cycles individually by the use of experience. Until today, there is no objective data based approach available that supports the initiation of these conditioning processes or the adaption of the grinding process itself in order to improve its process efficiency. For this purpose, a process-related topography evaluation method of the grinding wheel surface is developed within this study. For the measurement, an optical method based on laser triangulation is used. The measurement system is implemented into a common tool grinding machine tool. In addition, characteristic topography values are defined that show the wear conditions of the grinding tool. Moreover, the data is summarized in a database of wear conditions. The developed measurement method can save grinding and dressing tool resources, process times and minimizes scrap parts. In addition, an adaptation of the process and a targeted launch of auxiliary processes can be enabled. The novel characteristic-based topography measurement creates the opportunity to enhance the tool life of the grinding wheels up to 30% without losing productivity. [ABSTRACT FROM AUTHOR]

Surface finish quality is becoming even more critical in modern manufacturing industry. In machining processes, surface roughness is directly linked to the cutting tool condition; a worn tool generally produces low-quality surfaces, incurring additional costs in material and time. Therefore, tool wear monitoring is critical for a cost-effective production line. In this paper, the feasibility of a vibration-based approach for tool wear monitoring has been checked for turning process. AISI 1045 unalloyed carbon steel has been machined with TNMG carbide insert twenty-one times for a total of 27 min of machining, which was a necessary amount of time to exceed (300 μm) as a flank wear threshold. Vibration signals have been acquired during the operation and then processed in order to extract a correlation between the surface roughness, tool wear level, and vibration comportment. First, spectral kurtosis has been calculated for the twenty-one performed runs signals; this step has allowed the locating of the optimal frequency band that contains the machining vibration signature, yet it did not give significant information about wear evolution. The signals have then been decomposed with ICEEMDAN and the energy of the high-frequency modes has been calculated. It has been found that the energy of the optimal frequency ICEEMDAN modes has increased in proportion to the increase of surface roughness degradation and thus, to tool wear increase. Therefore, IMF's energy can be used for tool wear condition monitoring. [ABSTRACT FROM AUTHOR]

In additive manufacturing (AM), it is necessary to know the influence of processing parameters in order to have better control over the microstructure and mechanical performance of the part. Laser powder bed fusion (LPBF) AM is beneficial for many reasons; however, it is limited by the thermal solidification conditions achievable in the available processing parameter ranges for single-beam processing methods. Therefore, this work investigates the effect of multiple, coordinated heat sources, which are used to strategically modify the melting and solidifying in the AM process. To model this, existing thermal models of the LPBF process have been modified to include the effects of multiple, coordinated laser beams. These computational models are used to calculate melt pool dimensions and thermal conditions (thermal gradient and cooling rate). Furthermore, the results of the simulations are used to determine the influence of the distance between the coordinated laser beams in different configurations. The multi-beam scanning strategies modeled in the present study are shown to alter both melt pool shape and size, and the thermal conditions at the onset of solidification. However, these variations are not shown to result in alterations in the grain morphology of Ti-6Al-4V components. This predictive method used in this research provides insight into the effects of using multiple coordinated beams in LPBF, which is a necessary step in increasing the capabilities of the AM process. [ABSTRACT FROM AUTHOR]