# A Comprehensive Digital Model Approach for Adaptive Manufacturing Systems
## 2 Materials and Methods  
### Methodology  
Overall, this methodology provides a systematic approach to creating a digital model of an adaptive manufacturing system, ensuring its accuracy, validity, and functionality. Each block contributes to the development of a robust and reliable digital representation that can be used for analysis, optimization, and decision-making in the context of adaptive manufacturing. Figure 1 is a scheme of the proposed methodology. The methodology consists of four main parts. The first two parts are depicted in Figure 2.  
Figure 1: Scheme of the proposed methodology. Figure 2: Methodology of creating adaptive manufacturing system blocks A and B. Block A--Model Preparation (Input Data, Structure, Relationships, Entity Functions):  
In this phase, the initial groundwork for the digital model is laid. This involves gathering the necessary input data, defining the structural components of the model, establishing relationships between these components, and determining the functions or behaviors of the entities within the system. It is the foundational step that sets the stage for creating a comprehensive digital representation of the adaptive manufacturing system. This phase involves preparing the adaptive manufacturing system's model. It defines suitable production and product, sets the digital model's goal, selects software tools, characterizes data and parameters, establishes data structure and relationships, defines entities and their roles, identifies influential events, visualizes decision options, and plans scheduling. Steps:  
A1: Definition of suitable production and product for adaptive manufacturing system:  
This step involves determining the types of production and products that are appropriate for the adaptive manufacturing system. This decision considers the nature of the system and its capacity for adaptability to various production requirements. A2: Definition of the digital model's goal: Here, the primary purpose and objectives of creating the digital model are defined. This could include goals like optimizing production processes, analyzing system behavior, or testing different scenarios. A3: Selection of software support: This step focuses on selecting the software tools that will be used to build the digital model. The choice of software is crucial as it affects the capabilities and functionalities of the model. A4: Characterization of data and parameters: The data and parameters relevant to adaptive manufacturing are identified and described. This could involve aspects such as production metrics, resource availability, demand variability, and other factors that influence the system's behavior. A5: Data structure and relationships between data: In this step, the structure of the data is designed, and the relationships between different data elements are established. This forms the basis for how information flows and interacts within the digital model. A6: Definition of entities in the digital model and their roles: Entities, which could represent various components of the manufacturing system, are defined along with their roles and functions in the digital model. A7: Identification of events influencing the operation of the manufacturing system: Events that can affect the functioning of the manufacturing system are identified. These events could include changes in demand, availability of resources, breakdowns, etc. A8: Visualization of planned decision-making alternatives: Different decision-making scenarios are visualized, which could involve considering various production strategies, resource allocations, or response plans to changing conditions. A9: Planning and scheduling: The planning and scheduling aspects of the adaptive manufacturing system are designed. This could include strategies for adjusting production schedules in response to changing demands or resource availability. The data and parameters of adaptive manufacturing were characterized, and the structure and relationships among them were analyzed.