The process ensures the automated production and inspection of GV12 valve-lifters to maintain quality standards, with a focus on detecting chip formation on the workpiece surface. A batch of workpieces, i.e., parts that are utilized for the assembly of more complex products, is automatically produced and inspected to ensure the quality of the parts. The integration of data collection, compression, and analysis facilitates efficient monitoring and decision-making throughout the production cycle.
Main Machinery Involved:
    Milling Machine
    Measuring Machine (KEYENCE) for optical measurement of part diameter
    Independent Lift (LIFT) for transporting parts through the measuring machine
    Robot for handling the workpieces.
This process is characterized by the following steps:
Firstly, the production run details are fetched from the Manufacturing Execution System (MES) to initiate the manufacturing process. Then each GV12 valve-lifter is crafted from raw material using a milling machine. Throughout this process, various metrics such as vibration, noise, energy consumption of machine parts, and tool positioning data are continuously monitored and logged for analysis. After manufacturing, each valve-lifter undergoes measurement using a specialized measuring machine (KEYENCE). This machine conducts an optical measurement of the part diameter and generates a detailed point-cloud representation of the part's surface with an accuracy of 0.001 millimeters. The measurement data obtained from this step can be extensive and is stored in the process log for further analysis. Given the potentially large volume of measurement data generated per workpiece, a data compression task is employed to efficiently store this information in the process log. The stored data from manufacturing and measurement processes are analyzed to assess the quality of each produced valve-lifter. A key focus of this analysis is the detection of chip formation on the surface of the workpieces. By scrutinizing the collected data, the system determines whether chips have formed and evaluates their severity. This analysis outcome plays a crucial role in decision-making regarding the disposition of the workpieces.


Process description: 
The process ensures the automated production and inspection of GV12 valve-lifters to maintain quality standards, with a focus on detecting chip formation on the workpiece surface. A batch of workpieces, i.e., parts that are utilized for the assembly of more complex products, is automatically produced and inspected to ensure the quality of the parts. The integration of data collection, compression, and analysis facilitates efficient monitoring and decision-making throughout the production cycle.
Main Machinery Involved:
    Milling Machine
    Measuring Machine (KEYENCE) for optical measurement of part diameter
    Independent Lift (LIFT) for transporting parts through the measuring machine
    Robot for handling the workpieces.
This process is characterized by the following steps:
Firstly, the production run details are fetched from the Manufacturing Execution System (MES) to initiate the manufacturing process. Then each GV12 valve-lifter is crafted from raw material using a milling machine. Throughout this process, various metrics such as vibration, noise, energy consumption of machine parts, and tool positioning data are continuously monitored and logged for analysis. After manufacturing, each valve-lifter undergoes measurement using a specialized measuring machine (KEYENCE). This machine conducts an optical measurement of the part diameter and generates a detailed point-cloud representation of the part's surface with an accuracy of 0.001 millimeters. The measurement data obtained from this step can be extensive and is stored in the process log for further analysis. Given the potentially large volume of measurement data generated per workpiece, a data compression task is employed to efficiently store this information in the process log. The stored data from manufacturing and measurement processes are analyzed to assess the quality of each produced valve-lifter. A key focus of this analysis is the detection of chip formation on the surface of the workpieces. By scrutinizing the collected data, the system determines whether chips have formed and evaluates their severity. This analysis outcome plays a crucial role in decision-making regarding the disposition of the workpieces.

Rules for mermaid js flowcharts:
The graph must use the LR (Left to Right) direction.
Each mermaid js node must have the following structure:
id:type:shape and text
    id - it is a unique identifier. Integer from 1 to n. Each node has a unique identifier
    type - defines the type of the element regarding to BPMN 2.0 notation. 
        possible types are: start event, end event, task, exclusive gateway and parallel gateway. 
        Based on the type of the node following shapes and texts are to be used:
        startevent: ((startevent))     i.e., id:startevent:((startevent)) 
        endevent: ((endevent))	     i.e., id:endevent:((endevent)) 
        task: (task label)             i.e., id:task:(task label)
        exclusivegateway: {x}          i.e., id:exclusivegateway:{x} 
        parallelgateway: {AND}         i.e., id:exclusivegateway:{AND} 

All nodes that have occurred more than once should have following structure: id:type: (i.e., 2:task: ) by futher occurrence. 
It is strictly prohibited to use only id (i.e. 2) as a reference.   

    all elements are connected with each other with the help of the direction.
        direction: -->
    if there are some conditions or annotations it is necessary to use text on links (i.e., edge labels)
        edge label: |condition or annotation|
    edge label is always located between 2 nodes: id:exclusivegateway:{x} --> |condition or annotation|id:task:(task label)

Considering provided process description and a set of custom rules create a valid mermaid.js graph.


graph LR
1:startevent:((startevent)) --> 2:task:(Fetch production run details from MES)
2:task: --> 13:exclusivegateway:{x}
13:exclusivegateway:{x} --> |Proceed with production| 3:task:(Manufacture GV12 valve-lifter using milling machine)
3:task: --> 4:task:(Monitor and log metrics)
4:task: --> 5:task:(Measure part diameter using KEYENCE)
5:task: --> 6:task:(Generate point-cloud representation of part's surface)
6:task: --> 7:task:(Store measurement data in process log)
7:task: --> 8:task:(Compress data for efficient storage)
8:task: --> 9:task:(Analyze data to assess quality)
9:task: --> 10:task:(Detect chip formation on workpiece surface)
10:task: --> 11:task:(Evaluate severity of chips)
11:task: --> 13:exclusivegateway:{x}
13:exclusivegateway:{x} --> |End production| 12:endevent:((endevent))