Technical Library | 2021-11-22 20:39:44.0
Quality control is a key activity performed by manufacturing companies to verify product conformance to the requirements and specifications. Standardized quality control ensures that all the products are evaluated under the same criteria. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing and provided greater data availability. Such data availability has spurred the development of artificial intelligence models, which allow higher degrees of automation and reduced bias when inspecting the products. Furthermore, the increased speed of inspection reduces overall costs and time required for defect inspection. In this research, we compare five streaming machine learning algorithms applied to visual defect inspection with real world data provided by Philips Consumer Lifestyle BV. Furthermore, we compare them in a streaming active learning context, which reduces the data labeling effort in a real-world context. Our results show that active learning reduces the data labeling effort by almost 15% on average for the worst case, while keeping an acceptable classification performance. The use of machine learning models for automated visual inspection are expected to speed up the quality inspection up to 40%.
Technical Library | 2016-09-12 10:16:04.0
It is hard to open an Industry newsletter or visit an equipment manufacturer’s website without coming across a mention of the Internet of Things (IoT), Industry 4.0, SMART Manufacturing or ‘big data’. The accessibility to obtain data will only increase and this information and its real-time processing will become one of the most important resources for companies in the future. Production machinery will no longer simply processes the product, but the product will communicate with the machinery to tell it exactly what to do. Industry 4.0 has the vision to connects embedded system technologies and SMART production processes to drastically transform industry and production giving way to the SMART factory development. Future development in oven technology will allow machines to be controlled more intelligently and remotely resulting in the lowest cost model for manufacturing flow.
Technical Library | 2020-10-08 00:55:22.0
This article presents the development of a stretchable sensor network with high signal-to-noise ratio and measurement accuracy for real-time distributed sensing and remote monitoring. The described sensor network was designed as an island-and-serpentine type network comprising a grid of sensor "islands" connected by interconnecting "serpentines." A novel high-yield manufacturing process was developed to fabricate networks on recyclable 4-inch wafers at a low cost. The resulting stretched sensor network has 17 distributed and functionalized sensing nodes with low tolerance and high resolution. The sensor network includes Piezoelectric (PZT), Strain Gauge(SG), and Resistive Temperature Detector (RTD) sensors. The design and development of a flexible frame with signal conditioning, data acquisition, and wireless data transmission electronics for the stretchable sensor network are also presented. The primary purpose of the frame subsystem is to convert sensor signals into meaningful data, which are displayed in real-time for an end-user to view and analyze. The challenges and demonstrated successes in developing this new system are demonstrated, including (a) developing separate signal conditioning circuitry and components for all three sensor types (b) enabling simultaneous sampling for PZT sensors for impact detection and (c)configuration of firmware/software for correct system operation. The network was expanded with an in-house developed automated stretch machine to expand it to cover the desired area. The released and stretched network was laminated into an aerospace composite wing with edge-mount electronics for signal conditioning, processing, power, and wireless communication.
Technical Library | 2019-05-23 21:56:56.0
Automatic on-line shoe sole spraying system: automatic shoe sole spraying system, simple and convenient operation, using 3D vision positioning system. Automatic recognition and automatic generation of spraying trajectory. Robot non-contact spraying gun is used to complete the process of shoe sole spraying with maturity, stability, high speed and high precision along the predetermined trajectory. The automatic generation of spraying trajectory is the realization of shoe sole spraying technology. Shoe sole spraying characteristics: 1.Positioning System: 3D Visual Positioning 2.Components: Intelligent Robot, Laser Scanner, Industrial Computer, Gum Spraying System, Conveyor Belt, Electrical Control System, etc. 3.Spraying time: slightly different according to shoe size and spraying time Fully automatic sole spraying advantages: 1. Simple application: suitable for soles of different specifications, models and sizes 2. Faster speed: 6-8 seconds to complete sole scanning and spraying, superior to similar products at home and abroad. 3. Quality stability: gum spraying trajectory is scheduled, gum dosage is fixed, gum spraying quality is greatly improved. 4. High cost performance: the same performance, the price is only 1/3 of the same type of equipment of European brand. 5. Reduce wear and tear: glue is fully utilized and not wasted, reducing human contact with glue. Intelligent operation advantage manual only need general operation can be automated workshop, mechanical arm automatic spraying glue, accurate spraying, reduce glue waste. Environmental protection effect of long-term close contact with glue seriously affects human health and mechanical work, glue does not directly contact, do not harm the human body. Fully automatic spraying, shoe sole adhesion process for automatic spraying machine, will not cause great challenges! With the deepening of personalized shoemaking, higher requirements have been put forward for the spraying technology in shoemaking process. The method of creating spraying trajectory must be adapted to shoes of different sizes and styles. The automatic generation of spraying trajectory is one of the key technologies to realize the automation of shoe sole spraying process. The method of off-line programming and real-time generation of spraying trajectory for robots based on the three-dimensional CAD model of sole and the data of sole. A new method of generating spray trajectory by scanning the sole of shoe upper with linear structured light sensor is presented. The feasibility of the method is verified by industrial robots. Aiming at the need of generating shoe sole spray rubber trajectory based on line structured light, the format standard of IGES file of three-dimensional model of shoe sole was tested. The shoe sole contour line and the shoe sole surface were extracted, and then the offset curve of the shoe sole contour line on the shoe sole surface was calculated to obtain the spray rubber trajectory. Three-dimensional profilometer is to use structured light to obtain sole information, effectively improve the automatic shoemaking spraying process, which will help to improve the efficiency of shoemaking, improve the quality of footwear products, and promote the development of personalized shoemaking.
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