Molds are fundamental process equipment in the manufacturing industry; they are, in themselves, customized products. The structures of mold cavities and cores vary depending on the specific product, resulting in numerous R&D and manufacturing stages, long production cycles, and high precision requirements. Consequently, the development of mold enterprises faces many bottlenecks and constraints. Therefore, standardization, digitalization, informationization, automation, and intelligentization—collectively referred to as “The ‘Five Transformations’ will become the inevitable path for the transformation and upgrading of mold enterprises. Implementing the ‘Five Transformations’ poses significant challenges for mold enterprises, primarily because, as a traditional manufacturing industry, the mold sector lacks comprehensive research on a systematic framework for these transformations. The digital mold factory system is designed and implemented with the ‘Five Transformations’ as its overarching goals and strategic roadmap, unfolding in a pyramid-shaped progression from the bottom up, as illustrated in the figure.”

Digital Factory System “ Five Transformations ”

 

Standardization of the digital mold manufacturing system involves formalizing operational processes and establishing technical standards and operating procedures for each process step, which forms the foundation of the system. Digitalization refers to the application of computer-aided design and machining technologies to transform physical entities into virtual models, enabling simulation-based design, manufacturing, and modeling, thereby generating data that can be systematically shared and transmitted. Informatization entails connecting individual process steps through an integrated system to enable data acquisition, transmission, and analysis, thus providing data support for equipment automation and intelligence. Automation is achieved by leveraging sensor technology and program-driven control to enable equipment to execute tasks automatically in accordance with predefined instructions. Intelligence, on the other hand, relies on big-data analytics to generate optimal solutions based on pre-established rules, automatically adjust and control the manufacturing process, deliver precise outcomes, and realize flexible manufacturing.

1. Standardization of Digital Molding Factories

The first step in building a digital mold factory is to fundamentally transform traditional operational processes by identifying which tasks are currently performed manually and which can be automated or智能化ized, and then establishing new, standardized operating procedures.

The following figure For mold components NC machining processes: Currently, the production model in most mold manufacturing workshops in the industry is for a single operator to control 1 - Two machine tools handle the entire process—from tool setup and workpiece positioning to program verification, part loading, machine start-up monitoring, and final completion and unloading—yet the operation is cumbersome, inefficient, and fraught with safety risks, typically involving 12 sequential steps. Under a digital mold manufacturing plant architecture, this can be streamlined to five key steps: logistics, tool management, workholding, DNC (networked distributed CNC), and MDC (manufacturing data collection and monitoring), thereby transforming the traditional single-operator, multi-skill, serial workflow into a digitally enabled factory operation model that supports “five-digitization” initiatives.

Digital Factory System Process Optimization

By standardizing the operational workflow, the objectives of a digital mold manufacturing plant system are defined, such as: The digital factory system for NC machining processes needs to achieve the following three objectives.

( 1) Enhance equipment utilization. The overarching objective of a digital factory system is to reduce the skill requirements for operators and to minimize unscheduled downtime by ensuring that all necessary resources are in place prior to part machining. Any gaps or deficiencies are addressed through process optimization—such as improving tooling, refining manufacturing processes, and standardizing procedure management—to minimize auxiliary idle time on machine tools. This not only elevates lean manufacturing performance but also enables rapid real-time feedback on equipment operating status and fault conditions to relevant personnel during the machining process, making unplanned equipment shutdowns readily visible and easily traceable through the system.

( 2) Achieving collaboration across the production process. The digital factory system enables end-to-end management of CNC machining, covering all stages—from preparing NC programs and related technical documentation prior to part machining, to tool preparation, material provisioning, workpiece setup, and reporting equipment malfunctions—for which repair requests are submitted. By dispatching preparatory instructions in accordance with the production schedule, CAM programmers, tool preparers, setup technicians, and logistics personnel receive these instructions and concurrently carry out their respective resource preparations. Finally, based on the outcomes of these steps, the system generates start-of-machining commands, thereby automating most of the tasks that previously required manual coordination, intervention, and decision-making.

( 3) Remote control and closed-loop control. For CNC equipment equipped with network interfaces, the advanced DNC control functions enable remote delivery of the required machine tool programs directly into the machine’s memory at the scheduled start time, eliminating the need for manual intervention to load the CNC program. The system remotely controls the NC code—the primary machining carrier for CNC machines—determining which machining codes are sent to the machine and when, based on the production schedule. This prevents on-site operators from deviating from the planned machining sequence or from unauthorized modifications to the NC program, thereby ensuring compliance with operational procedures.

2. Digitalization of Mold Manufacturing Plants

Mold manufacturing plant digitalization comprises modules such as production planning management, process management, process simulation and verification, process documentation management, machine tool networking, material consumption consignment, collaborative Kanban, machine tool monitoring, production data acquisition, collaborative manufacturing (including system Kanban and visual management), data analytics, and equipment management. The following figure The production planning management module is simultaneously connected to the process management module, the process documentation management module, and the material consumption consignment module; the process management module and the data acquisition, analysis, and monitoring module are connected to the machine tools; and the process documentation management module and the material consumption consignment module are connected to the collaborative Kanban module. Data exchange among these modules is facilitated through Internet-based protocol transmission enables data communication between devices and servers, including functions such as uploading and downloading CNC programs and backing up machine tool parameters.

Factory Digitalization Process

The Production Planning Management module is responsible for developing production plans based on customer orders. The system can automatically generate production plans in accordance with order requirements, which can then be manually revised as needed. Once the production plan is finalized, it is transmitted over the network simultaneously to the Process Management module, the Process Documentation Management module, and the Material Consignment Management module.

The process management module sends the production plan to the machining center, where programmers develop the manufacturing programs for the products specified in the order based on the production plan. These programs include the planned start time, the production process flow, the duration of each process, process parameters, the production takt time, and the required machine tools, cutting tools, electrodes, and workholding equipment.

The program management module also includes a program simulation and verification module, which is used to conduct simulated production on a computer. This module features powerful and practical CNC programming capabilities, enabling intelligent file comparison, three-dimensional simulation of tool-path trajectories, and other functions to verify whether the program contains errors, whether the production process is reasonable, whether the machining time is minimized, and whether the process parameters are appropriately set. It can also determine whether the machining plan requirements are met and predict machining times. If any irregularities are detected, the system provides feedback to the programmer for revision and subsequent re-verification. By using the program simulation and verification module, potential anomalies during the production preparation phase can be quickly identified and promptly corrected and refined; this step must be carried out before formal production begins. The configuration of the program editing and simulation module frees programmers from the tedious tasks of program editing, inspection, debugging, and data transfer, thereby enhancing their work efficiency.

3. Informationization of the Digital Mold Factory

The informationization system of a mold manufacturing plant can guide enterprises in the planning and development of information systems, integrating data across manufacturing processes with product development, production management, and business management. The system is based on Digital CAD/CAM/CAE technologies serve as methodologies for mold design and process engineering, leveraging PLM, SAP, MES, and other enterprise information systems as data transmission and storage channels. By optimizing design and manufacturing processes, these technologies enable a significant reduction in the number of personnel required for discrete mold design and manufacturing, thereby establishing an efficient, high-performance mold manufacturing ecosystem. Focusing on the current production landscape of manufacturing enterprises’ project control, this approach facilitates an IT-driven transformation from function-based management to process-based management, providing IT system support across the entire product lifecycle—from product planning to product realization. Through comprehensive cost budgeting and project management of the entire supply-chain resource base, cost control is achieved at every stage, a visualized project interface is established, and real-time information sharing with customers is enabled, ensuring rapid response to customer needs.

 

Deployment of an Information System for a Digital Mold Factory

An information-based system enables seamless data sharing across all stages—planning, work order assignment, automated data collection, quality management, and resource allocation—thereby eliminating traditional paper-based workflows and reducing error rates. By using graphical representations as the foundation of planning and leveraging automated machine-tool data collection as the core technology, the system decomposes, manages, and issues production plans based on foundational data and available production resources. Through meticulous management, it monitors plan execution in real time, closing the loop among production plans for each manufacturing stage, actual execution at each stage, program transmission, program management, production data collection, and production information feedback. This closed-loop approach facilitates rapid response to plan changes, timely traceability of product-manufacturing process information, and transparent production management, while simultaneously reducing the workload of managers and employees and enhancing overall operational efficiency.

The digital factory system achieves interconnectivity across all processes through digitization and information technology, thereby The integrated application of systems such as DNC, MDC, and MES centers on CNC machine tools, is driven by production planning, and aims to maximize the utilization rate of CNC equipment. It focuses on improving the efficiency and cycle time of pre-processing activities required before CNC machining, while promptly identifying abnormalities in the production preparation process and delays in plan execution. When the system determines that the conditions for a machining operation are met, it can automatically transmit the machining program, process documentation, and tool list information to the workstation, thereby enhancing the overall production efficiency of mold manufacturing enterprises.

4. Digital and Automated Mold Manufacturing

Within a digital mold manufacturing plant system, once standardization, digitization, and information integration have reached a certain level, automation upgrades can be implemented. Taking electrical discharge machining (EDM) of mold components as an example, EDM is the process with the longest workflow and the lowest production efficiency among all stages of mold manufacturing, encompassing such key operations as electrode design, electrode material procurement, workpiece clamping, electrode machining, electrode inspection, electrode storage, electrode logistics handling, and EDM itself. Through standardized analysis of operational workflows, automated solutions can replace manual labor in processes such as electrode clamping and positioning, electrode logistics handling, electrode storage, electrode inspection, and workpiece clamping, thereby significantly improving EDM efficiency.

Automated electrode clamping and positioning scenario

Automated Production Line Solutions for Electrical Discharge Machining

5. Intelligent Digital Factory for Molds

The intelligentization of the digital mold manufacturing ecosystem requires the integrated application of IoT technologies, big data analytics, virtual simulation, and other advanced smart technologies to replace human decision-making. Within this ecosystem, intelligence represents the pinnacle of the pyramid, necessitating collaborative research and development among experts from the mold industry and fields such as data IoT, the industrial internet, artificial intelligence, and industrial software, in order to create solutions that meet the智能化 requirements of mold design and development.

Within the process nodes of a digital mold manufacturing plant, logistics is the core—acting as the vital link that runs throughout the production floor. The intelligentization of logistics builds upon automation by enabling the automated transport of workpieces from one station to another; however, achieving such automated transfer under complex conditions—including multiple workpieces, multiple logistics routes, and varying transit times—requires the integration of IoT-based data acquisition, advanced logic algorithm development, and big-data analytics to support autonomous decision-making. This is precisely what constitutes the realm of logistics intelligentization.

The following describes an intelligent electrode logistics scenario: the logistics scheduling system is responsible for transporting electrodes from the electrode machining unit to the electrode warehouse, while the EDM machining process, in turn, retrieves electrodes from the warehouse for electrical discharge machining. After analysis by the decision-making system, the logistics scheduling system first temporarily places completed electrodes on the storage racks at the machining unit, then prioritizes retrieving electrodes requiring EDM from the warehouse and automatically transports them to the EDM equipment, thereby ensuring uninterrupted operation of the EDM process. At the same time, during the transfer process, orders that can be consolidated are combined for transport, reducing redundant travel distance and conserving energy.

Intelligent Logistics Scenarios for Electrodes

The recycling of electrodes is another intelligent application within the electrode warehouse. By employing an automated matching algorithm that compares newly processed electrodes with in-stock, previously used electrodes, the system performs a shape comparison between new electrodes requiring processing and spent electrodes that have already undergone electrochemical machining. This enables automatic identification of electrode blanks that can be reused. A robotic arm then redefines and renumbers these reusable electrodes, reintroducing them into the automated electrode-processing workflow to achieve the circular utilization of spent electrodes.

 

Electrode Warehouse for Electrode Recycling

Summary ： The mold industry has its own digital management methodologies; however, due to the inherent characteristics of mold production—namely, frequent design changes, numerous quality anomalies, and extensive rework—the on-time delivery of molds is often difficult to achieve. By integrating project-based manufacturing management principles with the inherent advantages of digitalization in mold production, the on-time delivery rate can be significantly improved.

As is well known, molds are the “mother of industry” and serve as the upstream link in the value chains of many products. Even long before a product’s launch date, mold production is already underway; therefore, ensuring on-time delivery of molds plays a critical role in the timely release of end products. For a mold-making company, a high on-time delivery rate significantly enhances its competitive edge within the mold industry.

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