By analyzing the characteristics of the die component manufacturing industry, this paper points out that the current lack of shared and coordinated manufacturing resources, as well as defects in the manufacturing model, hinder the balanced development of the die industry. To address this issue, based on the current collaborative manufacturing model and the development of cloud manufacturing, a cloud manufacturing collaborative service model platform for the die industry is proposed. The platform's architecture, basic principles, and key technologies are analyzed. Through a case study, it is shown that the platform can promote more reasonable resource allocation and higher utilization rates, providing die companies with professional technical and production services, thereby enhancing their competitiveness.

The saying "Modern industry is driven by dies" highlights the crucial impact of dies on manufacturing. First, the quality and level of dies directly affect the quality of the final products launched into the market. Second, dies have a wide range of applications, including industrial, defense manufacturing, and medical devices. The development of dies reflects, to a certain extent, the development of manufacturing and serves as an indicator of technological advancement, innovation, and development capabilities. As a pillar industry of manufacturing, the die industry is listed as a key development industry [1-3].

Many industrial parks in various cities house numerous small die component processing plants, and some large enterprises have their own supporting workshops or plants. Generally, these are small in scale, with high industry concentration and regional characteristics. This is the current status of China's die manufacturing enterprises. Weak technological innovation capabilities and low informationization levels result in the inability of many die manufacturing enterprises to respond quickly to market demands, leading to long product development cycles and insufficient market competitiveness [4].

1. Analysis of Die Industry Characteristics

The following analysis addresses the problems in the manufacturing capabilities and operational models of current die companies.

1.1 Manufacturing Capability Analysis

Different industries and companies have different product requirements, leading to a wide variety of die products and an even greater number of die components. The processing equipment and manufacturing processes of die companies also vary, making it difficult to form a die industry cluster [5].

Due to the high investment in equipment and poor sharing, utilization rates remain low, hindering the development of die companies. In addition, excessive competition among die manufacturing companies has reduced profit margins, leading some companies to compromise die quality and lifespan to gain market share. This disrupts the market, harms customers, and damages the die industry. In the long run, die manufacturing companies focus on survival, neglecting improvements in technical levels and the application of information technology, hindering innovation and making it difficult to build a foundation for enterprise transformation and upgrading, thus failing to meet the demands of the high-end product market.

1.2 Operational Model Analysis of Die Manufacturing Enterprises

1.2.1 Order Sources

Die component processing enterprises are typically order-oriented manufacturing enterprises. From receiving an order and signing a contract to delivery, enterprises need to complete a series of processes for new product design and manufacturing, including design, process preparation, material procurement, and manufacturing. The repetition rate of each order is extremely low, and production is almost one-off, with each production having a trial nature. The randomness of orders and tight delivery deadlines put significant pressure on the design and manufacturing of die processing enterprises. Some orders serve the new product development of parent companies or upstream enterprises; these are task orders or mandatory orders. This requires die companies to spend more time and resources developing new dies, resulting in an inability to respond to market demands in a timely and effective manner.

1.2.2 Narrow Information Channels

Most small and medium-sized die enterprises have limited information sources, relying solely on salespeople to communicate with and receive orders from demanders of specific die components. Narrow market understanding channels lead to inaccurate market assessments. Insufficient informationization levels and a lack of industry technical, process, and market information hinder the development of die enterprises. Working in isolation and passively accepting orders makes enterprises increasingly lack the vitality to respond to the market.

1.2.3 Insufficient Development and Manufacturing Capabilities

According to research, due to the shortcomings in personnel quality, software, and equipment of small and medium-sized die component processing enterprises, die development is a time-consuming process. It takes one or two months to develop a die with an ordinary structure, while complex dies can take half a year or even longer. Therefore, if die enterprises rely solely on their own capabilities without external assistance, it is difficult to quickly and accurately seize market opportunities [5]. Most die enterprises lack independent R&D capabilities, high-precision processing and measuring equipment, and management capabilities. Under the "Internet+" thinking and with the technical support of cloud manufacturing, how to utilize advanced manufacturing technologies in the manufacturing process, integrate surplus resources, reduce redundant investment, improve resource utilization, reduce energy consumption, and reduce emissions to achieve resource sharing and collaborative management [6] has become a critical issue that China's manufacturing industry urgently needs to address. That is, it is necessary to explore a high-quality, efficient, and low-consumption development model for small and medium-sized die component processing enterprises.

2. Research and Application Status of Cloud Manufacturing Model


In January 2010, Li Bo Hu et al. [7] first clearly proposed the concept of cloud manufacturing, analyzed its operation model, and divided the cloud manufacturing platform architecture into physical resource layer, virtual resource layer, core service layer, application interface layer, and application layer, and conducted relevant research on its key technologies. Zhang Lin et al. [8, 9] proposed the hierarchical structure of the cloud manufacturing system and compared cloud manufacturing with several manufacturing models such as agile manufacturing, networked manufacturing, and service-oriented manufacturing, summarizing the characteristics of cloud manufacturing; Yin Sheng et al. [10] conducted research on the integration model of outsourced processing resources in a cloud manufacturing environment; Tai De Yi et al. [11] proposed a cloud manufacturing implementation model based on phased cooperation; Wang X V et al. [12] analyzed the service process, demand model, and cloud manager logic flow of cloud manufacturing, and proposed a cloud manufacturing system architecture supporting interoperability. Yang Chen et al. [13] proposed an application process model for cloud simulation resource management; Li Qiang, Xu Yan et al. [14, 15] proposed models and technologies for using cloud manufacturing in the die industry; Dan Zhao Xue et al. [16] conducted in-depth research on the organizational structure and role description and key technologies of the die cloud manufacturing platform; Li Bo et al. [17] analyzed the resource capabilities of the die industry cloud manufacturing platform and related technologies such as platform credit assessment; Xiao Gang et al. [18] proposed a cloud manufacturing (CMIA) application model oriented towards industrial alliances.

Based on the analysis of the current situation of the die industry and the application research of the cloud manufacturing platform, and based on the idea of the application service provider service model [19], this paper proposes a global service solution for a cloud manufacturing collaborative manufacturing model for die component processing enterprises based on CMIA, as shown in Figure 1. Based on CMIA for system development, it reduces the analysis of manufacturing service granularity and reduces the difficulty of developing and maintaining cloud manufacturing application systems. It enables the system to better meet the complex and changing market environment, thereby improving system flexibility. It can accurately and quickly integrate the relevant service resource information of member enterprises joining the dynamic alliance into an information network, so that the effective service resources among member enterprises are effectively integrated under the network environment, which is more conducive to the reasonable allocation of various resources. The cloud manufacturing collaborative service model application system uses strategy-driven agents, which allocate different service resources using corresponding resource matching algorithms according to different cloud service requests, improving resource utilization and the operational efficiency of die enterprises, thereby promoting flexible development of die enterprises and enhancing their responsiveness to the market.

3. Die Cloud Manufacturing Model Supporting Collaborative Services

Establishing a collaborative manufacturing service platform for small and medium-sized mold and parts processing enterprises based on an industry alliance not only integrates various service resources but also encapsulates resource information according to the needs of the entire mold life cycle, breaking geographical limitations on mold resource use and establishing a dedicated mold service resource database. This allows mold enterprises in different regions, regardless of size, to share service resources in the database through the cloud manufacturing collaborative service platform. When new market opportunities arise, services can be published on the platform promptly, forming the most advantageous service resource aggregate at the fastest speed. Service resource providers complement each other's strengths, forming strong alliances to actively respond to market demands, achieving efficient, high-quality, and low-cost service markets. This also brings benefits to mold enterprises providing service resources and promotes further enterprise development [20]. The mold cloud manufacturing model supporting collaborative services can not only integrate mold manufacturing capacity resources and improve mold manufacturing efficiency and shorten mold development cycles but also provide mold enterprises with enterprise portals, networked manufacturing, and e-commerce services. Integrating with networked manufacturing and service technologies and cloud computing, it encapsulates, intelligently manages, and centrally manages various manufacturing service resources, providing safe, reliable, and stable services based on the needs of the design and production activities in the mold manufacturing process and subsequent remanufacturing process. Alliance enterprises in the system can complete service publishing, service acquisition, and even service supervision, satisfying both common services and personalized manufacturing services. For mold enterprises, this domain-oriented planning and manufacturing service model can improve their mold development and manufacturing levels and technological levels, enabling information interoperability and allowing them to grasp market trends promptly. This actively promotes the correct understanding of the market, market share capture, and profit acquisition, further enhancing the overall technological level of mold alliance enterprises [21].

3.1 Cloud Manufacturing Collaborative Service Model System Architecture

With mold franchise enterprises as the leader, the cloud manufacturing collaborative service platform as the medium, the database system and network system as the system support, and the service resource cloud as the foundation, a mold cloud manufacturing collaborative service model is constructed. The service resource cloud can provide high-quality manufacturing service resources, modern design service resources, complete analysis service resources, advanced technical service resources, modern management service resources, and strong sales service resources to the platform; it can also publish service requests through the platform. The platform, based on the service requests submitted by the client, uses in-platform bidding or intelligent selection to find matching service resources among the alliance mold enterprises for the client, providing timely on-demand services for service requests. The system architecture of the mold cloud manufacturing collaborative service model is shown in Figure 2.

This collaborative service model, led by mold alliance enterprises, integrates all service resources needed in the mold development, research, and production processes into cloud manufacturing. First, it differs from the relatively mature networked manufacturing model. All alliance mold enterprises within the platform can issue service requests. This demand can be a mature market opportunity based on market research, i.e., all activities within the mold's entire life cycle, or some phased activities, such as mold design, mold testing, mold manufacturing, technical guidance, or advanced management concepts of mold enterprises. Regardless of the service resource needs, the platform will use its knowledge base and database to intelligently retrieve, match, and recommend services, efficiently, qualitatively, and transparently providing matching service resources to the requester, meeting overall or phased services.

This collaborative service model is similar to the centralized use of dispersed resources but not entirely the same, as it not only centralizes dispersed service resource information but also clarifies the idea of decentralizing centralized service resources during execution. Therefore, the collaborative service model is more flexible, providing service resources wherever needed. "One-to-many," "many-to-one," and "many-to-many" are all service forms of the collaborative service model. This is the idea of global service, first effectively integrating dispersed service resources and then providing service resources for multiple service needs.

3.2 Basic Principles of the Global Service Idea of the Cloud Manufacturing Collaborative Service Model for Mold Enterprises

The cloud manufacturing collaborative service model emphasizes embedding resources, capabilities, and knowledge into the network environment, aiming to build a public manufacturing environment where manufacturing enterprises, customers, and intermediaries can communicate fully. User participation in the cloud manufacturing collaborative model is not limited to traditional user needs and evaluations but permeates every aspect of the product's entire life cycle. Moreover, user identities are not unique; a user can be both a service resource requester and a service resource provider or developer in cloud manufacturing, reflecting a user-participatory service manufacturing approach.

Users describe their service needs, which can also be translated into quantitative descriptions or expected output descriptions for rigorous information input. The collaborative service platform analyzes and infers the input needs information, sorting out various service resource sets that meet the needs and recommending the most matching service resource information groups identified by the platform. Service-demanding users can adopt the platform's recommended matching resource groups or filter the service resource sets and various dimensions of resource information provided by the platform based on their specific needs, independently selecting suitable service resources and feeding them back to the platform. The platform further constructs resource service combinations based on user choices and provides appropriate intelligent suggestions. Thus, recommendation, selection, feedback, combination, and suggestions, then recommendation again, are iterated, resulting in an optimized service resource set that meets the service needs of multiple parties.

Driven by diversified and personalized market demands, the weights of different dimensions of service resource requirements vary among users, and the types of service resources in the cloud manufacturing collaborative service platform are particularly numerous, with different parameter settings for different service resources. Therefore, the priorities, matching weights, and filtering thresholds [22] of various services are also different, which poses challenges for resource combination. Therefore, the platform sets different parameter optimization settings for different service needs. For example, for product design service needs, the platform will recommend parameter configurations with high design capability weights; for technical service needs, it will recommend parameter configurations with high technological advancement weights; and for processing and manufacturing service needs, it will recommend parameter configurations with high manufacturing resource weights. Of course, as the amount of user input needs information increases, the optimization configuration will also change. The platform will conduct detailed data analysis based on service needs and the weights of various dimensions of resources to complete the optimal resource allocation for service needs and provide it to users. Figure 3 shows the resource retrieval and matching framework of cloud services.

The optimal matching output of service demand (N) and service resources (Sj) requires a filtering threshold for definition, calculated according to formula (1). Service resources with a matching degree greater than the filtering threshold are output and arranged in descending order, fed back to the service-demanding user by the platform.

In the formula, N is the service demand, Sj is the service resource, P_M(N, Sj) is the matching degree ranking of service demand and service resources, wi is the weight considered for different dimensions, and θi is the threshold for different dimensions, and n is the number of dimensions set according to the service demand.

4. Application Analysis

The mold cloud manufacturing collaborative model makes production and enterprise organization methods more flexible and adaptable to complex and changing market environments. Especially for small and medium-sized mold enterprises, those lacking in design or manufacturing can complete their enterprise design and production tasks by calling or renting resources, capabilities, and cloud services in the cloud manufacturing system, thereby reducing the difficulty of enterprise design and manufacturing and bringing greater benefits to the enterprise. The following is a case study to explore the ideas and implementation technologies of mold development, design, and manufacturing under the cloud manufacturing collaborative service model. Figure 4 shows the collaborative service model workflow.

A semiconductor company needs to develop innovative new products based on its existing ones, requiring corresponding mold design and manufacturing. After releasing user service needs through a cloud manufacturing collaborative service platform, a service resource team centered around Company M receives the service needs after matching, selection, and optimization, and signs a contract. Company M then decomposes and assigns tasks based on the service needs and the resources within the team. Guided by the cloud manufacturing collaborative service platform, the semiconductor mold's improved design can be jointly undertaken by the university's mold technology center and the mold R&D technology center within the team. Material forming analysis and simulation analysis are conducted in the university laboratory. The mold manufacturing companies and the university mold technology center within the team receive the design data and conduct collaborative manufacturing of the mold's process parts and structural parts. Standard parts required for the mold are provided by standard parts suppliers. After all mold parts pass strict inspection, they are coordinated and transported to Company M for assembly, debugging, and mold testing. Once all work is completed, the mold is delivered to the semiconductor company, and the cloud manufacturing collaborative service platform confirms the completion of the contract.


In this case, mold design and analysis service resources are located at Company M's mold center and the university, while machining equipment and special processing/high-speed processing manufacturing resources are located at mold manufacturing companies and the university. The dispersed service resources in the mold design and manufacturing process are centralized for use through the cloud manufacturing collaborative service platform to fulfill a single service need, demonstrating the platform's "many-to-one" work model. The platform can also implement "one-to-many" and "many-to-many" work models.

Conclusion


This article discusses the distributed, geographically dispersed collaborative design and manufacturing of molds using a mold enterprise cloud manufacturing collaborative service platform. This approach allows for the full utilization of existing manufacturing resources in the mold industry, improving resource utilization; shortens mold manufacturing cycles, accelerating mold development and manufacturing processes, and generating significant benefits; and promotes technical exchange and guidance among mold enterprises, further advancing the mold manufacturing industry. The system's operating mechanism requires further research, and there is still a long way to go before a comprehensive business model can be launched.