Practical Application of Brick Machine Production Cycle Time Compression: Research on Multi-Axis Cooperative Motion Control and Process Overlap Technology

Practical Application of Brick Machine Production Cycle Time Compression: Research on Multi-Axis Cooperative Motion Control and Process Overlap Technology

Abstract The efficiency of a block production line directly depends on the optimization level of the brick machine's production cycle time. This paper addresses the problems of long waiting times between processes and low equipment utilization caused by the single-axis sequential operation of traditional brick machines, and proposes a cycle time compression scheme based on multi-axis cooperative motion control and process overlap technology. By establishing a multi-axis synchronous mathematical model, designing intelligent process overlap logic, and developing an adaptive scheduling system, significant results were achieved, including a 20%-25% reduction in production cycle time and a reduction of over 30% in non-productive time. This research provides a systematic technical path and engineering practice reference for the efficient production of brick machines.

Keywords Brick machine production cycle time; multi-axis cooperative control; process overlap technology; motion synchronization algorithm; cycle time compression; adaptive scheduling; efficiency optimization

1. Introduction

With the continuous improvement of the construction industry's requirements for block production capacity and quality, the efficiency bottleneck caused by the independent operation of each process and poor inter-axis coordination in traditional brick machines is becoming increasingly prominent. As a key indicator for measuring the overall efficiency of equipment, the compression of production cycle time requires breaking through the limitations of the single-axis operation mode. This paper focuses on two major technical directions: multi-axis collaboration and process overlap. Through a combination of theoretical modeling and engineering verification, it explores the optimization potential and implementation path of brick machine production cycle time, aiming to provide the industry with quantifiable and replicable efficiency improvement solutions.

2. Factors Affecting Brick Machine Production Cycle Time and Optimization Needs

2.1 Analysis of the Composition of Traditional Production Cycle Time
A typical brick machine production cycle includes processes such as feeding, material distribution, vibration, compaction, and demolding. These processes are executed sequentially. Among these processes:

Effective operating time accounts for only 60%-70%, with the remainder being waiting and reset time between axes;

Single-axis motion limitations lead to inherent delays in process connections;

Fixed cycle time patterns are difficult to adapt to the production needs of different block specifications.

2.2 Technical Bottlenecks in Cycle Time Compression
The limit of motion acceleration under the rigid constraints of the mechanical structure;

The timing conflicts and interference risks of multi-axis actions;

The response lag of the hydraulic and electrical systems.

3. Multi-axis Cooperative Motion Control Technology Scheme

3.1 Construction of Multi-axis Synchronization Mathematical Model
Based on kinematic and dynamic equations, a multi-axis cooperative model including the vibration axis, feeding axis, and pushing axis is established:

θ_i(t) = f_i(t) + Σk_ij·[θ_j(t) - θ_j_des(t)]

Where θ_i is the actual position of the i-th axis, f_i is the independent motion function, and k_ij is the inter-axis coupling coefficient.

3.2 Synchronization Control Algorithm Design
Master-Slave Synchronization Strategy: The vibration axis is the master axis, and the other axes are slave axes. The phase difference is adjusted through real-time position feedback.

Cross-coupling Compensation: Feedforward control is used to compensate for dynamic interference between axes.

Adaptive Gain Adjustment: PID parameters are optimized in real time according to load changes.

3.3 Engineering Implementation and Verification
The synchronization control module is embedded in the servo drive system. Test results show that:

Multi-axis synchronization accuracy reaches ±0.1mm;

Inter-axis response delay is reduced to within 50ms.

4. Process Overlap Technology Application Mechanism

4.1 Overlap Logic Design Principles

Non-conflict verification: Ensure overlapping processes do not interfere with each other spatially or temporally;

Resource competition management: Prioritize shared actuators (e.g., hydraulic cylinders);

Quality control constraints: The overlapping process must not affect the density and appearance of the blocks.

4.2 Intelligent Overlap Strategy

Vibration-feeding overlap: Start the next feeding cycle in the later stages of vibration;

Demolding-laying overlap: Simultaneously execute the laying preparation action during the return phase of the demolding mechanism;

Adaptive overlap window: Dynamically adjust the overlap time (0.5-1.2s) according to the block size.

4.3 Risk Control Measures
Set up safety buffer zones to prevent mechanical collisions;

Automatically switch to sequential mode when overlap fails;

Monitor system load in real time to avoid overload.

5. Adaptive Scheduling System Development

5.1 Scheduling Architecture Design

The system adopts a three-layer structure:

Decision Layer: Generates the optimal cycle time scheme based on block specifications and the process library;

Coordination Layer: Parses the process sequence and allocates axis motion commands;

Execution Layer: Drives the servo system and hydraulic unit to complete the actions.

5.2 Core Algorithm Implementation

Dynamic Programming Optimization: Solves the process sequence with the minimum cycle time as the objective function;

Real-time Trajectory Correction: Adjusts the motion curve based on sensor feedback;

Energy Consumption Balancing Module: Achieves a balance between efficiency and peak power.

5.3 Human-Machine Interface
Provides functions such as visualized cycle time monitoring, one-click parameter adjustment, and fault diagnosis prompts to reduce operational complexity.

6. Experimental Verification and Effect Analysis

A technical upgrade was implemented on a certain type of block production line. Comparative data is as follows:

Indicator | Before Upgrade | After Upgrade | Improvement

Single Cycle Time (s) | 12.5 | 9.8 | -21.6%
Daily Production Capacity (blocks) | 28,000 | 35,200 | +25.7%
Inter-axis Waiting Time Percentage | 31% | 18% | -41.9%
Unit Energy Consumption (kWh/thousand blocks) | 42.3 | 36.1 | -14.7%
Mold Wear Rate (g/10,000 cycles) | 115 | 92 | -20.0%

6.2 Stability Test Results

After 72 hours of continuous operation, the cycle time fluctuation rate was <2%;

Adaptation time for switching between different block specifications was ≤3 cycles;

System failure rate decreased by 40%.

7. Engineering Application and Promotion Value

7.1 Applicable Scenarios Analysis

New high-end block production line construction;

Energy-saving and efficiency-enhancing upgrades of existing equipment;

Flexible production needs for multiple varieties and small batches.

7.2 Economic Benefit Assessment
Taking a production line with an annual output of 10 million blocks as an example:

Annual increase in block production is approximately 1.8 million blocks, resulting in an additional revenue of approximately 1.5 million yuan;

Energy saving and consumption reduction benefits are approximately 250,000 yuan/year;

Investment payback period is approximately 8-12 months.

7.3 Industry Promotion Path
Formulate the group standard "Technical Specification for Multi-Axis Collaboration in Brick Machines";

Develop modular upgrade kits to lower the threshold for transformation;

Establish a library of typical application cases to provide technical reference.

8. Conclusion and Outlook
This study confirms that through the systematic application of multi-axis collaborative motion control and process overlap technology, the production cycle time of brick machines can be reduced by more than 20%, while simultaneously improving energy efficiency and equipment stability. Future research directions include:

Artificial Intelligence Optimization: Introducing deep learning to predict the optimal overlap window;

Digital Twin Integration: Verifying the cycle time scheme in advance through virtual debugging;

Standardization Expansion: Promoting technology adaptation to different brands and models of brick machines.

This achievement provides practical and feasible technical support for the efficient and intelligent transformation of block production