Micro Grooving of Cemented Carbide Using PCD Blade

Ultra–Agile Advanced Manufacturing System (U–AMS) has been proposed for an agile prototyping system of research and development, and Super Processing Center (SPC) has been developing as a core machine tool of U–AMS. SPC has high accuracy and rigidity by double column structure based on a vertical precision machining center, hydrostatic oil guides and hydro static/dynamic hybrid oil bearing. In addition, SPC can perform various processing functions by mounting various processing units. Micro grooving by grinding has been researching for one of the SPC unit. PCD blade was developed for micro grooving using SPC. PCD blade was made from PCD disc using wire electrical discharge machining. This paper describes the fabrication method of PCD blade, and the machining characteristics of cemented carbide using PCD blade. In the fabrication method of PCD blade, it was clarified that the cross-sectional shape of PCD blade depended on the feed speed of wire electrode. Micro grooving on cemented carbide surface was performed using developed PCD blade and SPC. As a result, it was confirmed that the micro grooves can be machined using PCD blade, and the width of groove was almost same value as the width of PCD blade. The wear of PCD blade after grooving with 200 grooves was estimated by the depth of grooves. As a result, it was clarified that the wear of PCD blade is approximately 5 μm.

Multiple Profiles Sensor-based Engineering Data Processing

Nowadays big data is widely adopted in industry field. In an advanced manufacturing system hundreds of sensors are deployed to collect key variables for system performance and the real-time data would be used for further monitoring and anomaly detection. However, there are many challenges for applying the sensor-based data directly, including the profile data has unsynchronized different length for different samples, the existence of obvious long-term drift, strong correlation of sensor clusters and the particular feature extraction. To solve these problems this invention presents a multiple profiles sensor-based engineering data processing system, including (1) preprocessing the signals to align the data and remove long-term drift, (2) clustering the sensors which have strong correlations, and (3) extracting particular features from different sensor clusters.

Automatic Classification for Anti Mixup Events in Advanced Manufacturing System

The mix-up is a phenomenon in which a tablet/capsule gets into a different package. It is an annoying problem because mixing different products in the same package could result dangerous for consumers that take the incorrect product or receive an unintended ingredient. So, the consequences could be very dangerous: overdose, interaction with other medications a consumer may be taking, or an allergic reaction. The manufacturers are not able to guarantee the contents of the packages and so for this reason they are very exposed to the risk in which users rightly want to obtain compensation for possible damages caused by the mix-up. The aim of this work is the identification of mix-up events, through machine learning approach based on data, coming from different embedded systems installed in the manufacturing facilities and from the information system, in order to implement integrated policies for data analysis and sensor fusion that leads to waste and detection of pieces that do not comply. In this field, two types of approaches from the point of view of embedded sensors (optical and NIR vision and interferometry) will be analyzed focusing in particular on data processing and their classification on advanced manufacturing scenarios. Results are presented considering a simulated scenario that uses pre-recorded real data to test, in a preliminary stage, the effectiveness and the novelty of the proposed approach.

Knowledge Innovation Network of Advanced Manufacturing System

For the advanced manufacturing network, the depth of knowledge innovation is the key to measure its success. This paper points out the necessity to design an effective knowledge innovation network, and presents it from the aspects of connotation, structure and training. Writer put forward a SVM dynamic mechanism of knowledge innovation, which emphasizes innovation culture, mutual trust and information network technology as the core. Research results show that, compared with traditional methods, knowledge innovation network can significantly improve the efficiency of innovation and manufacturing.

Methods of Maintenance Resources Configuration and Optimization for AMS Based on Effectiveness Evaluation

To improve effectiveness of the production and manufacturing equipment and reduce the downtime, methods of effectiveness evaluation and integrated configuration and optimization for spare parts and repair equipments are studied. It makes that advanced manufacturing system is target and .efficient work percentage is the measure of effectiveness. On the basis of AMS effectiveness concept, measure parameter is confirmed and influence factors are analyzed considering operation concept, design concept, maintenance concept and supply concept. And then, AMS effectiveness simulation evaluation models are proposed and simulation evaluation system is designed and developed. Optimization model for the support quantities of the spare parts and repair equipments is given with constraints on efficient work percentage, and solved using marginal analysis algorithm. Finally, an example is provided to verify the validity of the methods.