Technical Development of Defect
Early Warning System
Difficult Technology Development
Firstly, the engineering model should be established, and the
engineering model affecting defects and defective products
should be established according to the on-site process,
products, equipment and automation conditions of the
enterprise. Have a deep understanding of the engineering
fields of process, product, equipment and automation. It is
best to have worked in various fields on site for several
years; If this technical level is not reached, the resulting
system will have one short board or another. Most of the
on-site problems are comprehensive. At first glance, a
considerable part of the problems are difficult to be solved
directly by basic theory; But in fact, there may be problems
in the coordination of various fields, which is difficult for
people with only one aspect of professional knowledge to
understand.
Based on the existing data of the factory, such as MES data,
industrial Internet data or other data collected with SCADA,
machine learning can fully bind the established model with the
current situation of process, products, equipment and
automation of the production line, that is, the accuracy of
the model on the production line of the collected data is very
high.
The most difficult is the system architecture of software and
field engineering problems. In addition to the design of
normal software architecture, such as data structure,
database, class, function, module and software interface, it
is particularly necessary to collect all relevant scenario use
cases, such as compiling the current work of all engineers on
site into intelligent software! If these scenario use cases
are not collected comprehensively, the system will not react
or react improperly when encountering scenario use cases that
have not been collected, and the processing instructions
obtained by the automation system are incorrect, which will
cause accidents! After the scene use cases are fully
collected, the system should be able to automatically form an
intelligent alarm, and when the operator takes action, the
system should be able to respond in time and take follow-up
actions. In particular, the system should guide the on-site
operator how to deal with relevant problems.
Technical
Development Stage of Defect Early Warning System
The technical development of defect early warning system,
taking lithium battery as an example, should include three
crucial stages:
(1) Early model development, including more than 100 sets of
models in various stages of lithium battery manufacturing, see
Overview of lithium battery manufacturing technology
development;
(2) The development of three contracts for the existing
lithium battery production line of a large electric vehicle /
lithium battery enterprise, including data acquisition and
application of MES data, defect prediction and corresponding
machine learning, as well as knife gap prediction, soft
measurement and corresponding knife gap machine learning, as
well as the research and development of knife gap measurement
hardware, see
Development case of lithium battery defect early warning
system;
(3) Customized development for various enterprises, production
lines and various production processes, such as interface
development for different MES software packages, see item by
item below.
Interface
Development of Existing Defect Early Warning System Installed
in Each Data System
The focus here is to add
the installation interface with each MES system / each
industrial Internet platform / each SCADA data system for the
defect early warning system, so that it can be fully
integrated into the existing data systems. At present, various
suppliers have developed a large number of data systems. For
example, there are about 20000 manufacturing execution systems
MES. About 10 types of input interfaces can be developed, and
the interface variables are exposed, so that users can connect
with the system through subroutines.
Setting Module of Customized Model of Defect Early Warning
System
The defect early warning system sets specific modules for
adding customized models during development. Metal Pass company
has designed and developed a number of proprietary customized
models, which are designed into corresponding modules. Based
on the customer's on-site situation, they are combined into
the customized module of defect early warning system during
installation. The company provides different versions of the
system and adds customized models. For common sections, the
company gives priority to setting up many versions of defect
early warning system, and gives priority to adding the
customized model of common sections to the defect early
warning system. During installation, you only need to select
the corresponding version in the list.
When there is a need for customized models on site, but the
company has never encountered similar customized models in
previous projects, according to the advantages of experts in
the team in model development, we can carry out certain
customized model development, and the newly developed model
module can be added to the defect early warning system. Our
team has developed more than 100 models of emerging industries
and more than 100 models of traditional industries, a total of
more than 200 sets.
Service Name: customized development, installation and
application of defect early warning system
Manufacturing industry, especially manufacturing industry,
whose quality needs to be improved
Innovation: Key Technologies of Smart Manufacturing,
such as engineering modeling and modeling of vital
characteristics in production, such as product quality or
defective rate
Defect Warning System Series
Development case of lithium
battery defect early warning system
Function and application of defect early warning system
products
Customer requirements of defect early warning system
Technical consultation of defect data based on Prediction
Production process optimization based on defect early warning
Introduction to defect early
warning system technology
Tech & Products
Situation,
Metauniverse,
4.0 brains,
Smart
manug,
Equipment Softw
Defect warning,
Defect detection,
Li-battery,
Level 2 platform
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