09307160301ML Allicdata Electronics

09307160301ML Connectors, Interconnects

Allicdata Part #:

09307160301ML-ND

Manufacturer Part#:

09307160301ML

Price: $ 18.34
Product Category:

Connectors, Interconnects

Manufacturer: HARTING
Short Description: CONN BASE BOTTOM ENTRY SZ16B
More Detail: N/A
DataSheet: 09307160301ML datasheet09307160301ML Datasheet/PDF
Quantity: 1000
Lead Free Status / RoHS Status: Lead free / RoHS Compliant
Moisture Sensitivity Level (MSL): 1 (Unlimited)
1 +: $ 16.66980
Stock 1000Can Ship Immediately
$ 18.34
Specifications
Series: Han® B
Packaging: Bulk 
Lead Free Status / RoHS Status: --
Part Status: Active
Moisture Sensitivity Level (MSL): --
Connector Type: Base - Panel Mount
Style: Bottom Entry
Size: 16B
Lock Location: Locking Clip (2) on Base Bottom
Thread Size: --
Size / Dimension: 4.449" L x 1.709" W x 1.138" H (113.00mm x 43.40mm x 28.90mm)
Housing Color: Black
Features: --
Ingress Protection: IP65 - Dust Tight, Water Resistant
Housing Material: Aluminum, Die Cast
Housing Finish: Powder Coated
Operating Temperature: -40°C ~ 125°C
Description

Due to market price fluctuations, if you need to purchase or consult the price. You can contact us or emial to us:   sales@allicdata.com

Heavy Duty Connectors - Housings, Hoods, Bases

Machine Learning (ML) is the field of study concerned with the development of algorithms that enable computers to learn from data equipped with a set of inference rules. ML has become the de facto standard in modern data science for data-driven problem solving and decision making due to its ability to distill actionable information from vast amounts of data. It is the catalyst for big data driven insights, tools and technologies and often replaces the need for manual data gathering and analysis. ML is increasingly used in a wide variety of applications from automotive and healthcare to telecommunications and finance.

Heavy Duty Connectors, such as housings, hoods and bases, serve as the backbone of any reliable and secure connection. As full-featured products, they come in a variety of form factors allowing for easy installation and a high degree of customizability. Heavy Duty Connectors are manufactured to provide dependable signal distribution, superior shielding, extreme tensile strength, and optimum environmental protection. Moreover, they are designed to function in extreme temperature, vibration, and shock conditions. For these reasons, Heavy Duty Connectors can be seen in industries such as marine, military, aerospace, and industrial applications since they provide robust and reliable connections without sacrificing on performance and longevity.

Using Machine Learning (ML) within the scope of Heavy Duty Connectors has recently gained traction. This involves the implementation of ML algorithms, such as convolutional neural networks (CNNs), as part of a comprehensive inspection process. These models are trained on large data sets of images to understand the variations in defects and irregularities. This coupled with the traditional inspection process can increase the accuracy and efficiency of the process.

Another application of ML is the use of predictive analytics. Since Heavy Duty Connectors are built to last for a long period of time, predicting and managing their lifecycles can help prevent downtime due to unexpected failures. This can be done by utilizing predictive analytics learning algorithms such as historical data, customer demands, and operational costs. This type of ML can help predict system-wide failures even before they happen and allow for corrective action.

Not only can ML be used to inspect and predict the lifecycles of Heavy Duty Connectors, but it can also be used to optimize the production process as well as the design of the connectors. Various ML techniques, such as supervised learning, regression, and clustering, can be used to determine important factors, such as connector size, type of material, and reliability, that contribute to the optimized design of the product. Machine learning can also be used to monitor production processes, such as thermal energy generation or plastic injection molding, to ensure that they are running efficiently and cost-effectively.

In conclusion, by applying ML algorithms to Heavy Duty Connectors, engineers are able to achieve a higher level of accuracy, efficiency, and reliability during the production, inspection, and optimization of the connectors. ML can be used to accurately detect faults or defects in the connectors, predict lifespan and improve operational performance, as well as optimize the entire design process. By exploiting the predictive power of ML, engineers are able to make better informed decisions and improve the quality and reliability of Heavy Duty Connectors.

The specific data is subject to PDF, and the above content is for reference

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