Digitization
Digitalization Process of Zhejiang Shenlong Chain Drive Co., Ltd.
Against the backdrop of the current digital wave sweeping the manufacturing industry, Zhejiang Shenlong Chain Drive Co., Ltd. has actively engaged in digital transformation. Through a series of reform measures and forward-looking plans, the company is committed to improving production efficiency, optimizing product quality, and enhancing market competitiveness.
Implemented Digital Reform Measures
Digital Upgrade of Production Processes
The company has comprehensively sorted out and digitally transformed its production links, introducing advanced automated testing technologies (including mechanical testing, electronic sensor testing, and image testing systems) to conduct 24/7 real-time monitoring of key quality characteristics throughout the entire production process. Relying on the digital technology of automatic chain-making machines, it has realized precise feeding, positioning, and pin-assembling operations, significantly reducing human operation errors and improving production stability and product consistency. In the heat treatment process, vacuum furnaces and multi-purpose furnace assembly lines have replaced traditional mesh belt furnaces, and digital control systems have been adopted to achieve precise temperature regulation, ensuring that the heat treatment quality of products in each batch stably meets the standards.
Digital Innovation in Testing Links
In the quality testing sector, the company uses more than 70 sets of advanced testing equipment to conduct all-dimensional testing on raw materials, semi-finished products, and finished products. At the same time, the testing equipment is connected to the digital testing management system to realize automatic collection, analysis, and storage of testing data. Built-in data analysis algorithms are used to quickly identify potential trends in product quality, providing scientific data support for the adjustment of production processes and further improving product quality and reliability.
Collaborative Digital Reform with Major Machinery Companies
Digital Collaboration in the Supply Chain
The company has actively joined hands with major upstream and downstream machinery companies to promote the digital transformation of supply chain collaboration. It has built a digital information sharing platform with raw material suppliers to realize real-time interaction of information such as raw material production progress and quality testing data, ensuring the timeliness and stability of raw material supply. In cooperation with downstream mechanical equipment manufacturing enterprises, it has established a collaborative design platform to promote real-time interaction and collaborative optimization of product design data, shortening the product R&D cycle and improving the efficiency of responding to customer needs.
Digital Collaboration in Manufacturing
In the manufacturing process, the company has jointly explored a collaborative manufacturing model with other machinery companies. Through the industrial Internet platform, it has realized the interconnection of production equipment and the collaborative allocation of production tasks. Relying on the platform to share key information such as production progress and equipment operation status in real time, it ensures the efficient and orderly progress of the collaborative production process and improves the overall production efficiency and resource utilization rate of the industrial chain.
Future Plan for Building a Digital Twin Model
Objectives of Building the Digital Twin Model
The company plans to build a digital twin model covering the entire production process within the next 3 years. Based on the actual production system, this model will collect multi-source information such as equipment operation data, process parameters, and product quality data to create a highly simulated digital model of the real factory in the virtual space. The core objective is to achieve refined and precise management and control of the production process, predict potential production problems in advance through virtual scenario simulation and analysis, and formulate optimal solutions. Ultimately, it aims to reduce production costs, improve production efficiency, and ensure product quality.