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JUKI Fast Smart Modular Pick and Place Machine with speed 42000CPH RS-1

JUKI Fast Smart Modular Pick and Place Machine with speed 42000CPH RS-1

New Equipment | Soldering - Other

 Pick and Place Machine PrevNext JUKI Fast Smart Modular Pick and Place Machine with speed 42000CPH RS-1 Applicable Components: 0402~5050 Board size:50×50??650×370? Feeder inputs:max.112 placement capacity:40,000

Flason Electronic Co.,limited

FREE Webinar: Eliminate Circuit Board Problems and Failure Modes

Events Calendar | Tue Jan 23 00:00:00 EST 2018 - Tue Jan 23 00:00:00 EST 2018 | New Milford, Connecticut USA

FREE Webinar: Eliminate Circuit Board Problems and Failure Modes

Vision Engineering Inc.

Qualification and Reliability of Microvias

Events Calendar | Tue Dec 04 00:00:00 EST 2018 - Tue Dec 04 00:00:00 EST 2018 | ,

Qualification and Reliability of Microvias

CALCE Center for Advanced Life Cycle Engineering

https://www.youtube.com/watch?v=22uJi79Z0VY

https://www.youtube.com/watch?v=22uJi79Z0VY

Videos

Welcome to this Defect of the Month video on head in and on pillow, this series of videos were produced for and featured as part of the NPL/IPC video library and examples are taken from the NPL Defect Database. Over the last couple of years there hav

ASKbobwillis.com

Supercapacitors and Graphene Overtake Batteries: New Event

Industry News | 2014-06-26 19:47:04.0

The worlds of supercapacitors and graphene are converging. That is why IDTechEx has a complete two day nine conference co-located event on these subjects, which will take place in Santa Clara, CA this November 19-20.

IDTechEx, Inc.

Low Temperature Soldering Workshops and Training Materials

Industry News | 2019-09-03 12:08:08.0

With the growing interest in the use of low temperature soldering and the use of solder paste in rework we have created the first training resources available to downoad at www.bobwillis.co.uk Training materials for use in your own onsite workshops or tailor made onsite hands on sessions. The training material feature training wall charts and a photo album of images to use in PowerPoint, reports or on websites

ASKbobwillis.com

Enthone Expands ViaForm� Sales and Support Services

Industry News | 2003-05-21 09:17:38.0

Enters into Licensing and Joint Development Alliance with ATMI

Enthone

Electronic Components, Parts and Their Function

Industry News | 2018-12-08 03:22:25.0

Electronic Components, Parts and Their Function

Flason Electronic Co.,limited

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Technical Library | 2022-06-27 16:50:26.0

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

Institute of Electrical and Electronics Engineers (IEEE)


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