Automation Solutions for Discrete Manufacturing Based on Siemens PLCs

1. Introduction

Discrete manufacturing encompasses various industries such as automotive, electronics, and machinery, characterized by production processes composed of independent steps, where products are typically completed through discrete operations like assembly and processing. With the increasing market demand for product personalization and shorter product life cycles, discrete manufacturing enterprises face multiple challenges in enhancing production flexibility, reducing costs, and ensuring quality.

Siemens Programmable Logic Controllers (PLCs), known for their stable and reliable performance as well as strong integration capabilities, have become the core control platform for automation in discrete manufacturing. According to market research data, Siemens PLCs hold over 30% market share in the global discrete manufacturing sector, particularly in high-end applications such as automotive and electronics manufacturing.

The direct value brought by automation solutions to discrete manufacturing includes: a 20-35% increase in production efficiency, a 15-25% improvement in product quality consistency, over 50% enhancement in production flexibility, and a 10-20% reduction in energy consumption. However, the implementation process must address various technical challenges such as control system integration, data interoperability, and flexible production.

2. Siemens PLC Product Series and Selection

PLC Series Suitable for Discrete Manufacturing

Siemens offers a comprehensive line of controllers for discrete manufacturing:

·S7-1200 Series: Suitable for small discrete manufacturing equipment and standalone automation, featuring cost-effectiveness.

·S7-1500 Series: Suitable for medium to large manufacturing systems, providing high-performance computing and rich communication capabilities.

·ET200SP: Distributed I/O controller, ideal for space-constrained and modular design scenarios.

PLC Series	CPU Performance	Storage Capacity	Communication Functions	Typical Application Scenarios
S7-1200	Mid-low end	Up to 150KB	Basic PROFINET	Small assembly equipment, standalone automation
S7-1500	Mid-high end	Up to 32MB	Rich industrial Ethernet protocols	Production line control, multi-axis motion control
ET200SP	Flexibly configurable	Up to 1MB	Distributed PROFINET/PROFIBUS	Modular devices, space-constrained scenarios

Key Factors for Controller Selection

Choosing the appropriate controller requires consideration of the following key indicators:

·Number of control points (I/O scale)

·Program complexity and CPU performance requirements

·Communication needs (protocols, quantity, bandwidth)

·Number of motion control axes

·System scalability requirements

3. Core Control System Composition for Discrete Manufacturing

Control Layer Architecture Design

A typical discrete manufacturing control system adopts a three-layer architecture:

![Discrete Manufacturing Control System Architecture] (This is a schematic diagram of the three-layer control system architecture, including management layer, control layer, and field layer)

·Management Layer: MES/ERP systems responsible for production planning and resource management.

·Control Layer: S7-1500 as the main control PLC, coordinating various workstation controllers.

·Field Layer: S7-1200 or ET200 as workstation controllers, directly controlling actuators.

Industrial Network Topology

Discrete manufacturing often employs PROFINET industrial Ethernet as the backbone network, constructing the following network topology:

·The control layer uses a redundant ring network structure to ensure high availability.

·Field devices adopt star or line topology to reduce complexity.

·Critical devices use IRT (Isochronous Real-Time) communication to ensure determinism.

4. Automation of Core Business Processes in Discrete Manufacturing

Material Feeding and Processing Workstation Control

The material feeding system typically consists of robotic arms, conveyor belts, and positioning devices. The PLC control program must handle multiple sensor signals and coordinate the actions of various actuators:

// Automatic feeding workstation control program example (SCL language)FUNCTION_BLOCK "MaterialFeeding"VAR_INPUT    Start : BOOL;              // Start signal    MaterialSensor : BOOL;     // Material detection sensor    PositionReached : BOOL;    // Position completed signalEND_VAR

VAR_OUTPUT    Conveyor : BOOL;           // Conveyor control    GripperOpen : BOOL;        // Gripper open    GripperClose : BOOL;       // Gripper close    MoveToPickPos : BOOL;      // Move to pick position    MoveToPlacePos : BOOL;     // Move to place positionEND_VAR

VAR    Step : INT := 0;           // Current step    Timer : TON;               // TimerEND_VAR

BEGIN    CASE Step OF        0:  // Initial state - waiting for start            IF Start THEN                Step := 10;            END_IF;

                10: // Start conveyor            Conveyor := TRUE;

            IF MaterialSensor THEN

                Conveyor := FALSE;                Step := 20;            END_IF;

                20: // Move to pick position            MoveToPickPos := TRUE;

            IF PositionReached THEN                Step := 30;            END_IF;

                30: // Grasp material            GripperOpen := FALSE;

            GripperClose := TRUE;            Timer(IN := TRUE, PT := T#1S);            IF Timer.Q THEN                Step := 40;                Timer(IN := FALSE);

            END_IF;

                40: // Move to place position            MoveToPickPos := FALSE;

            MoveToPlacePos := TRUE;

            IF PositionReached THEN                Step := 50;            END_IF;

                50: // Release material            GripperClose := FALSE;

            GripperOpen := TRUE;            Timer(IN := TRUE, PT := T#1S);            IF Timer.Q THEN                Step := 60;                Timer(IN := FALSE);

            END_IF;

                60: // Return to initial position            MoveToPlacePos := FALSE;

            GripperOpen := FALSE;            Step := 0;    END_CASE;

END_FUNCTION_BLOCK

Assembly Line Synchronization and Cycle Control

Cycle control in assembly lines is a key technology in discrete manufacturing, typically using a master-slave synchronization method to ensure coordinated operation of various workstations:

![Assembly Line Cycle Control Schematic] (This is a schematic diagram of synchronization among various workstations on the assembly line)

The core functions of cycle control include:

·Synchronization signal generation and distribution

·Workstation status management and monitoring

·Exception handling and recovery for workstations

·Production data statistics and analysis

5. Application of Advanced Control Technologies

High-Speed Motion Control and Motor Synchronization

Siemens S7-1500 supports high-performance motion control through technology objects (TO), suitable for complex applications such as multi-axis synchronization and electronic cam:

// Multi-axis synchronized motion program example (S7-1500 LAD to SCL)FUNCTION "SynchronizedMotion" : VOIDVAR_INPUT    StartSync : BOOL;          // Start synchronized motion    MasterPosition : LREAL;    // Master axis positionEND_VAR

VAR_OUTPUT    SyncActive : BOOL;         // Synchronization status activeEND_VAR

VAR    Master : TO_PositioningAxis;    // Master axis technology object    Slave : TO_SynchronousAxis;     // Slave axis technology object    SyncCmd : MC_CamIn;             // Cam synchronization commandEND_VAR

BEGIN    // Configure master axis    "Master".Position := MasterPosition;        // Start synchronization    IF StartSync AND NOT SyncActive THEN        SyncCmd(Master := "Master",                Slave := "Slave",                CamTable := "CamTable_1",                Mode := 0,                Execute := TRUE);

    END_IF;

        // Check synchronization status    IF SyncCmd.InSync THEN

        SyncActive := TRUE;

    END_IF;

        // Reset execution trigger    IF SyncCmd.Busy = FALSE AND SyncCmd.Execute = TRUE THEN

        SyncCmd.Execute := FALSE;

    END_IF;

END_FUNCTION

RFID Material Tracking System

RFID technology is commonly used in discrete manufacturing for material and product tracking, integrated with Siemens RF series RFID readers and PLCs:

·Supports various RFID standards such as ISO 14443/15693

·Provides standardized FB libraries to simplify development

·Enables full-process material tracking and quality data association in production

6. PLC Programming Design Patterns and Best Practices

Modular Programming Structure

Discrete manufacturing control programs should adopt a modular design to achieve functional partitioning and code reuse:

// Modular programming structure exampleORGANIZATION_BLOCK "Main"BEGIN    // System initialization    "SystemInit"();        // Device control module    "DeviceControl"();        // Production process control    "ProcessControl"();        // Data management    "DataManagement"();        // Communication handling    "Communication"();        // Alarm handling    "AlarmHandling"();END_ORGANIZATION_BLOCK

State Machine Design Pattern

The state machine is an ideal design pattern for discrete manufacturing control, clearly describing process flows and equipment states:

![State Machine Design Pattern] (This is a schematic diagram of a typical workstation state machine)

Key points for implementing a state machine:

·Use enumerated types to define clear states

·State transition conditions are clear and unique

·Each state includes entry actions, hold actions, and exit actions

·Supports switching between manual and automatic modes

7. Case Study: Actual Application in Discrete Manufacturing

Automotive Parts Production Line Transformation

A certain automotive parts manufacturer used Siemens PLCs to achieve automation transformation of the production line:

·Control System: S7-1516 as the main control PLC, with 6 S7-1200 distributed across various workstations.

·Network Architecture: PROFINET backbone network, PROFIBUS connecting frequency converters.

·Key Technologies:

oRFID-based product tracking system

oIntegration of visual inspection systems

oFlexible production recipe management

Transformation results: 35% increase in production efficiency, 80% reduction in quality defect rate, and product changeover time reduced from 4 hours to 20 minutes.

8. Digital Transformation and Future Development

TIA Portal and Digital Engineering

TIA Portal, as Siemens’ unified engineering platform, provides a complete solution for discrete manufacturing from virtual debugging to on-site implementation:

·Supports unified engineering for PLCs, HMIs, drives, safety, and other systems

·Virtual debugging and simulation validation shorten project cycles

·Template library management promotes best practice reuse

·Version control and team collaboration features

Edge Computing and Cloud Platform Integration

Modern discrete manufacturing is integrating traditional automation systems with cloud platforms to achieve data-driven production optimization:

// Edge computing data processing example (Node-RED)[

  {    "id": "f6f2187d.f17ca8",    "type": "s7 in",    "z": "3045ad74.c52c02",    "name": "Read PLC Production Data",    "endpoint": "S7Connection",    "mode": "single",    "variable": "DB100.ProductionData",    "diff": true,    "x": 270,    "y": 380,    "wires": [      [        "2b93a1f2.986c3e"      ]

    ]

  },

  {    "id": "2b93a1f2.986c3e",    "type": "function",    "z": "3045ad74.c52c02",    "name": "Data Processing",    "func": "// Calculate OEE\nconst availability = msg.payload.RunTime / msg.payload.PlannedTime;\nconst performance = msg.payload.ActualCount / (msg.payload.IdealRate * msg.payload.RunTime);\nconst quality = msg.payload.GoodCount / msg.payload.ActualCount;\nconst oee = availability * performance * quality;\n\n// Add calculation result\nmsg.payload.OEE = oee;\n\nreturn msg;",    "outputs": 1,    "x": 450,    "y": 380,    "wires": [      [        "f8824908.57b6c8"      ]

    ]

  },

  {    "id": "f8824908.57b6c8",    "type": "mqtt out",    "z": "3045ad74.c52c02",    "name": "Send to Cloud Platform",    "topic": "production/metrics",    "qos": "1",    "retain": "false",    "broker": "CloudBroker",    "x": 650,    "y": 380,    "wires": []  }

]

Siemens MindSphere industrial IoT platform seamlessly integrates with edge devices and on-site PLC systems to enable production data analysis and remote monitoring.

9. Conclusion

Automation solutions for discrete manufacturing based on Siemens PLCs can effectively enhance production efficiency, product quality, and system flexibility through the integration of advanced control technologies, optimized program design, and the application of digital tools. Key factors for successful implementation include:

1.Reasonable system architecture design and equipment selection

2.Standardized programming methods and modular design

3.Comprehensive data collection and management mechanisms

4.Continuous optimization of maintenance and upgrade strategies

With the integration of new technologies such as 5G and artificial intelligence, automation in discrete manufacturing will further evolve towards intelligent manufacturing. Future Siemens PLC solutions will focus more on openness, interoperability, and intelligent analysis capabilities, helping manufacturing enterprises maintain a competitive edge in digital transformation.

When planning discrete manufacturing automation projects, enterprises should design system architecture from a long-term perspective, ensuring that current production needs are met while reserving space for future upgrades to intelligent manufacturing, gradually achieving the evolution from automation to digitalization and then to intelligence.