### 1 Introduction

### 2 Architecture of Industry 5.0 Oriented Smart Manufacturing

### 3 Hybrid Model Based Autonomous Grinding Process Control System

### 3.1 Problem Description

### 3.2 Development of DT System for Describing Grinding Process

#### 3.2.1 Systematic Procedure for Developing the DT System

#### 3.2.2 Implementation of DT for Grinding Machine and Process

### 3.2.2.1 Analysis of Grinding Machine in Terms of Static and Dynamic Behavior

### 3.2.2.2 Calculating Mechanical/Electrical Loss of Grinding Machine with Physical Model along Force Flow

### 3.2.2.3 Realizing a Controller of DT for Grinding Machine

Analyzing motion control and logical control through ladder diagram;

Data setting for the communication of the controller with the virtual machine;

Description of the control characteristics.

Configuration of the virtual PLC.

#### 3.2.3 Implementing a DT System for Grinding Machine

Carrying out a logical mapping of the machine elements’ attributes to the operational processes. This is done by identifying the value-added (geometric, physics, behavior) machine elements;

Developing the operational process and the grinding machine’s digital model;

Developing a relational rule model that logically implements the interaction between the operational process and the machine’s digital twin attributes. This establishes the logical connection between the digital elements with their operational processes, respectively. With this dynamic interaction, the machine-process digital twin information model is established which is used for describing the machine’ dynamic behaviors.

#### 3.2.4 Calculating Grinding Force with Engineering Model for DT

_{1}: grinding wheel diameter;

*ω*

_{1}: rotation speed of grinding wheel;

*ω*

_{2}: rotation speed of brake disc; and L: center distance between grinding wheel and brake disc.

_{a}, K

_{s}, H

_{s}, and λ are transfer functions to get the depth of cut a

_{e}, grinding force, grinding stone shape, and deflection (x) respectively. The formula for calculating the effective cutting depth of grinding is as follows:

*a*

*: effective cutting depth of grinding;*

_{e}*a*

*: programmed cutting depth;*

_{p}*a*

*: grinding wheel wear;*

_{s}*x*: deflection; and

*x*

*: thermal expansions.*

_{exp}#### 3.2.5 Evaluation of the Developed DT System

*Y*

*: simulated current value;*

_{sim}*Y*

*: real current value. With n equals 25 (s) and the upper current value, the precision value:*

_{real}*η*

*= 95.01%; and for the lower current value:*

_{Upper}*η*

*= 95.37%. The deviation between the actual motor current value and the simulated motor current value within 5%. This value demonstrates the high accuracy of the DT system.*

_{Lower}### 3.3 Design of Hybrid Model for Controlling Grinding Process

### 3.4 Implementation of An Autonomous Grinding Process Control System

#### 3.4.1 Design of Framework for Monitoring Grinding Process

#### 3.4.2 Design of Autonomous Process Control System Architecture

#### 3.4.3 AI Assisted Generation of Process Control Algorithm

### 3.4.3.1 Optimizing AI Model for Deriving out Threshold Value

### 3.4.3.2 Generating Control Algorithm for Adjusting Control Parameter

### 3.4.3.2.1 Selecting Control Parameter

_{27}orthogonal array analysis were carried out to find the effects of process parameters on surface roughness and vibration characteristics during the process. With four process parameters including grinding wheel speed, workpiece speed, wheel entry speed, and coolant flow, the grinding wheel speed is the most influencing factor in terms of surface roughness and vibration as described in Fig. 19. To reduce the experiment and measurement cost, we used vibration signal as monitoring signal.

### 3.4.3.2.2 Rule Based Autonomous Control Algorithm

### 4 Implementation of An Autonomous Grinding Control System

### 5 Application of the Developed Autonomous Control System to Practice

### 6 Conclusions

Proposing the architecture of Industry 5.0 oriented smart manufacturing in consideration of the concept of Industry 4.U/5.0 with the application of generative AI, autonomous process control, digital twin, cognitive agent, 5G/6G networking;

Developing the practice oriented autonomous process control system with high reliability;

Implementing the DT system with high accuracy of more than 95% through detail analyzing the real behavior of the grinding machine along force flow, considering the mechanical and electrical losses;

Design of the autonomous process control system architecture using hybrid model and AI;

Adjusting process parameter through rule-based algorithm and data processing by monitoring process behaviors in real time;

Contributed significantly to reducing the failure rate from 12 to 5% by successfully applying the process control system to an automobile brake disc grinding manufacturing line.