Technical Library | 2019-02-05 13:43:14.0
SonoFlux Servo with InSight automated board recognition system helps PCB manufacturers take steps toward greater automation and traceability and the goal of Industry 4.0.
Technical Library | 2021-08-04 18:39:07.0
The digital transformation with concepts like Industry 4.0, Smart Factory and the Internet of Things will change production, work processes and business models in electronics manufacturing radically and forever. It is all about connecting, gathering and exchanging big data. It is all about connectivity, and the gathering and exchange of big data.
Technical Library | 2021-06-02 19:39:14.0
With an estimated value creation potential for manufacturers and suppliers of USD 3.7 trillion in 2025,1 high hopes are set on Industry 4.0 to bring the next industrial revolution to discrete manufacturing. Yet, only about 30 percent of companies are capturing value from Industry 4.0 solutions at scale today. Approaches are dominated by envisioning technology development going forward rather than identifying areas of largest impact and tracking it back to Industry 4.0 value drivers. Further governance and organizational anchoring are often unclear. Resulting hurdles related to a lack of clarity regarding business value, limited resources, and an overwhelming number of potential use cases leave the majority of companies stuck in "pilot purgatory."
Technical Library | 2021-12-02 01:48:53.0
Some mechanical and assembly productions of existing companies of the Industry 3.0 and mechanical and assembly productions of perspective companies of the Industry 4.0 are described. The basic components of a smart factory and their interconnection to organize a production activity using humanless and paperless technologies are defined. A comparison analysis of parts and blanks movement to complete route sheet of the item manufacturing (radio and electronic item designing) in the companies of the Industry 3.0 and Industry 4.0 is given. The components of a digital item designing company to be created and implemented in the industry at first hand are defined.
University of Information Technologies, Mechanics and Optics [ITMO University]
Technical Library | 2017-04-28 07:53:37.0
A major drawback to Industry 4.0 that few write about is maintenance of an industry 4.0 plant. The maintenance aspect is a much greater and immediate drawback than even the commonly known major concern of security, and the lesser concern of system integration standards. Maintenance of 4.0 systems has, and will continue to result in related huge increases in process downtime. The barriers to overcoming the maintenance/downtime drawbacks of a 4.0 system are almost insurmountable. Has the Smart Manufacturing Leadership Coalition (SMLC) addressed the maintenance paradox? “... model also demands the ability to calculate and manage risk and uncertainty within very different operating structures. ..” Continue reading in pdf or for even more see and share http://bin95.com/Industry40inUSA.htm
Technical Library | 2021-08-04 18:41:30.0
Industry 4.0 is one of the most exciting developments in the manufacturing industry in decades. It promises vast improvements for both manufacturers and their customers. For some companies, however, it can be overwhelming, and it can be difficult with the current available information to understand exactly what the benefits will be in the average factory, and to calculate the return on the investment. Therefore, it may be helpful to bring the discussion down to a tangible level and to isolate one little part of the whole smart electronic assembly factory, namely reflow.
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 | 2016-09-19 20:26:36.0
This white paper seeks to set out the value of a ‘smarter’ approach to the reflow process and how a more intelligent oven can offer real added value and performance to the entire line. It also lays out some of the criteria that is important when selecting smart equipment for a smart process, that conforms to, and is ready for, IoM or Industry 4.0
Technical Library | 2021-12-02 01:44:00.0
With the advent of Industry 4.0, production processes have been endowed with intelligent cyber-physical systems generating massive amounts of streaming sensor data. Internet of Things technologies have enabled capturing, managing, and processing production data at a large scale in order to utilize this data as an asset for the optimization of production processes. In this work, we focus on the automatic detection of physical defects in the production of surfacemount devices. We show how to build a classification model based on random forests that efficiently detects defect products with a high degree of precision. In fact, the results of our preliminary experimental analysis indicate that our approach is able to correctly determine defects in a simulated production environment of surface-mount devices with a MCC score of 0.96. We investigate the feasibility of utilizing this approach in realistic settings. We believe that our approach will help to advance the production of surface-mount devices.
Technical Library | 2021-11-22 20:44:44.0
Many automated optical inspection (AOI) companies use supervised object detection networks to inspect items, a technique which expends tremendous time and energy to mark defectives. Therefore, we propose an AOI system which uses an unsupervised learning network as the base algorithm to simultaneously generate anomaly alerts and reduce labeling costs. This AOI system works by deploying the GANomaly neural network and the supervised network to the manufacturing system. To improve the ability to distinguish anomaly items from normal items in industry and enhance the overall performance of the manufacturing process, the system uses the structural similarity index (SSIM) as part of the loss function as well as the scoring parameters. Thus, the proposed system will achieve the requirements of smart factories in the future (Industry 4.0).
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