Digital-Driven Automation for Ship Component Manufacturing with In-Process Inspection
To tackle the issues of low machining efficiency and material waste resulting from uneven stock allowances in castings, this paper proposes an integrated machining process that combines modeling, inspection, and compensation. This approach incorporates on-machine inspection, digital modeling, and automated compensation technologies. By doing so, it achieves precise control of stock allowances, enhances machining efficiency, and minimizes manual intervention. This process addresses the demands of the intelligent transformation within the shipbuilding industry.
01 Introduction
Casting is a crucial metal forming method that plays an important role in the supply of materials for the manufacturing industry. However, several factors during the casting process—such as shrinkage deformation, deviations in the gating system, and mold wear—can lead to significant non-uniformity in the distribution of stock allowances for castings within the same batch, with variations down to the millimeter level. Research indicates that the dispersion of casting stock allowances can reach ±2 mm, which directly results in over a 30% reduction in machining efficiency. This makes it challenging to achieve effective stock allowance control using uniform process parameters in subsequent machining stages.
With the implementation of the 14th Five-Year Plan in local enterprises, the integration of next-generation information technology with manufacturing—and specifically with intelligent manufacturing—has started to reshape the global competitive landscape in industry. Leveraging on-machine measurement technology, which is driven by sensors, data acquisition, and processing, enables the use of physical feedback mechanisms. For example, probes installed on machine tools can detect the actual dimensions of parts being machined and automatically adjust machining paths based on these measurement results.
This article analyzes the transformation of automated batch manufacturing processes for ship castings in an intelligent environment. Drawing from the company’s experience in automated casting processing, it explores the deep integration of on-machine measurement, digital models, and CNC programs to develop an automated “modeling-inspection-compensation-machining” model. This approach serves as both a theoretical framework and practical reference for enterprises transitioning to intelligent manufacturing, thereby contributing to the high-quality development of the manufacturing industry.
02 Automation Architecture Design
The company is implementing process upgrades by employing intelligent methods and integrating on-machine inspection, digital twins, and interdisciplinary collaboration. This approach addresses efficiency issues stemming from three key characteristics: discrete casting allowances, non-fixed machining programs, and human intervention. As a result, we achieve greater automation, improved efficiency, and environmentally friendly outcomes.
A collaborative model consists of three dimensions: platform, technology, and implementation. This model aims to create an intelligent manufacturing production system for our manufacturing departments, facilitating large-scale operations and helping us meet our objectives. The automation architecture is illustrated in Figure 1.
03 Introduction to Key Technologies
Automated machining, a prominent feature of the Fourth Industrial Revolution, encompasses a multidisciplinary technological landscape that includes essential technologies such as digital modeling and process analysis, on-machine inspection, wear prediction, and the integration of intelligent technology with CNC (Computer Numerical Control).
Firstly, digital modeling and process analysis utilize three-dimensional digital models to compile and simulate manufacturing processes. This enhances process safety and effectively guides the actual production of parts.
Secondly, on-machine inspection and wear prediction technologies can replace the need for operator-assisted measurements during machining. This ensures that operators can stay at the machine, which effectively reduces process support time and labor intensity.
Additionally, integrating intelligent technology with CNC brings together on-machine inspection, wear prediction, and intelligent error prevention into machining programs. This integration helps minimize low-level errors and promotes the advancement of automated machining.
3.1 Digital Models and Process Analysis
Digital modeling transforms two-dimensional drawings into three-dimensional visual models, allowing for comprehensive monitoring of the entire product lifecycle—from design and development to production, manufacturing, operation, and maintenance. For instance, Airbus utilizes a digital assembly model, which has reduced the error rate during cable installations by 50%. This technology enables operators to intuitively visualize component structures and efficiently develop manufacturing processes based on machining characteristics.
Additionally, virtual commissioning technology allows for process verification and risk assessment before production begins, helping to identify potential machining issues early and shortening the R&D cycle. By leveraging the company’s cutting data network, machining parameters can be quickly aligned with similar machining conditions (such as material type, machining circumstances, machining stage, tooling used, and machine tool rigidity), thus reducing on-site commissioning time and facilitating effective part processing.
3.2 On-machine Testing
On-machine testing is an emerging technology designed to operate in real-time on machine tools, utilizing sensors for data acquisition and processing. By integrating high-precision probes and sensors, this technology captures workpiece dimensions and surface topography during machining and compares them in real-time with the digital model. This approach eliminates the need for disassembly, assembly, and transportation associated with traditional offline inspection, effectively shortening process support time and reducing the workload for operators. As a result, one person can manage multiple machines.
Additionally, feedback control based on online measurement data enables real-time corrections for process deviations such as tool wear and thermal deformation. This mechanism helps maintain dimensional accuracy during finishing and improves the yield rate of precision parts.
Since the core probe for on-machine inspection is mounted on the machine tool in a manner similar to machining tools, the inspection data is generated based on the machine tool’s Cartesian coordinate system. Consequently, environmental factors that affect machine tool accuracy also influence the accuracy of on-machine inspections.
Due to the impacts of thermal deformation, environmental factors, and temperature on the machine tool itself, the real-time accuracy of on-machine inspection cannot be solely determined by device specifications. For instance, while Renishaw’s on-machine inspection probe system boasts a repeatability of ±1mm, thermal deformation of the machine tool can actually increase measurement error by 0.02-0.05mm in practice. Companies should be mindful of these factors when implementing on-machine inspection.
3.3 Wear Prediction
Wear prediction is a crucial technology in advanced manufacturing and plays an essential role in automated machining. Currently, there are two primary methods for predicting tool wear.
The first method involves installing a sensor on the machine tool spindle or another relevant location to monitor cutting vibrations. The data collected (such as vibration or current) is converted into a signal. An integrated algorithm then analyzes the tool’s vibration curve, storing valid data as a standard sample for comparison with parts under similar working conditions. If the monitored vibration curve exceeds the standard threshold, the machine tool controller issues an alarm and shuts down the system. Research indicates that this method can achieve an accuracy rate of 85% for predicting tool life, especially when working with nickel-based alloys. Although this approach necessitates considerable expertise, it is beneficial for batch part machining, significantly reducing the need for on-site operator intervention.
The second method, known as empirical prediction, estimates standard tool wear values based on the cutting wear cycle of the tool. This approach classifies feature machining programs and tools, making it quicker and requiring less hardware and software. It is often employed in single-piece or small-batch production, making it suitable for discrete manufacturing companies.
Both methods of wear prediction rely on empirical data to evaluate cutting conditions, which can be influenced by various factors such as tool overhang and variations in workpiece materials. In practical applications, false positives pose a significant challenge. Companies must balance the trade-off between prediction accuracy and timeliness, finding the optimal solution for early warnings while ensuring accurate predictions in their operations.
3.4 Integration of Intelligent Technologies and CNC
Intelligent technologies encompass on-machine measurement, tool monitoring, and intelligent error prevention. The primary application lies in the CNC program that drives the processing of machine tools. Data generated during on-machine monitoring, alongside standard tool monitoring values, is archived for future reference. Error prevention measures related to tool diameters and variable confidence intervals are integrated into the CNC program.
There are currently two common integration methods. The first method involves consolidating all detection, processing, error prevention, and supplementary information into a single CNC program. This approach results in a smaller number of programs, making it easier for technical and operational personnel to access and manage them. The second method entails modularizing various types of data. In this approach, detection, processing, and error prevention information are created as individual subroutines and then integrated into a single main program. This modular design allows for clear function recognition, enabling easy adjustments to individual functional modules and providing rapid response capabilities.
Deep integration of intelligent technology with CNC programs requires converting collected data into variables that the CNC program can recognize. A common practice is to utilize the variable range set by the machine tool, such as the R series variables in the Siemens system. However, there are risks associated with directly applying R variables during the application process. Since CNC machine tools often have built-in fixed cycles (such as drilling and milling), some R variables are also used in the background of these modules. Consequently, if R variables are directly used for converting intelligent technology data, it can lead to data abnormalities when the machine tool is operating in a built-in cycle. In severe cases, this may result in overcutting or collisions.
04 Ship Casting Application Case
We are developing a digital process system designed specifically for discrete manufacturing enterprises, using a smart manufacturing workshop as our platform. To tackle the bottlenecks we encountered during implementation, we proposed various strategies and conducted field validations. These strategies focused on essential issues related to discrete casting stock allowances, non-standard machining procedures, and the need for human intervention. Ultimately, our efforts aimed to achieve fully automated casting production.
4.1 Typical Part Selection
The gas turbine rectifier strut is constructed from K446, which is a nickel-based, precipitation-hardening high-temperature alloy known for being difficult to machine. The initial form of the strut is a casting, and the milling process involves shaping the outer geometry and creating a system of holes. Due to the inconsistencies in the casting stock allowances, operator intervention is necessary during the milling process, leading to an extended machining duration. The total processing time per part is 96 hours, which includes 46 hours dedicated to machining and 50 hours for measuring the blank, ensuring precision control, and changing auxiliary tools. Figure 2 illustrates the rectifier strut.
The implementation of intelligent manufacturing technology in the workshop has introduced new features such as on-machine measurement, automatic measurement, and automatic tool changes. These advancements have led to significant improvements in processes and a reduction in auxiliary time. For the milling of the rectifier strut profile, we incorporated automated processing alongside these new features, with the goal of enhancing processing efficiency and decreasing the operator’s workload.
4.2 Implementation Platform Construction
See Table 1 for details of the implementation platform construction for the company’s intelligent manufacturing workshop.
4.3 Implementation process
In the initial phase of implementation, the goals are clearly defined as follows: to complete on-machine inspection of blank allowance, automate the compensation of high-precision features, and achieve automated operation of part milling programs. Based on these goals, the process is structured as follows: process analysis → precision verification → tool wear verification → logic control → machining simulation → compensation machining.
(1) Process analysis
Automated CNC machining process arrangement is carried out according to the machining characteristics and precision of the entire part. The part process arrangement is shown in Figure 3.
(2) Accuracy Verification
The accuracy of on-machine measurement was confirmed by comparing two methods: the measuring tool and on-machine measurement. The comparison of the test measurement data is presented in Table 2.
The data indicates that the actual error measured on the machine is 0.06 mm in the spindle direction and 0.03 mm in the vertical direction. Based on the accuracy range obtained from the tests, the sequence for the automated process of the rectifier pillar has been determined.
(3) Tool wear verification
The automated operation of this process is founded on the idea that the tool can effectively handle the standard processing requirements of a single program, and that tool wear will not significantly affect the size and quality of the part being produced. Based on the characteristics of the rectifier pillar material, a milling program has been developed, the tool wear situation has been analyzed, and a strategy for tool implementation has been established. The details of the tool wear during rough milling are presented in Table 3.
The test results align with the conclusions drawn in the literature. Using a wear threshold of 0.06 mm as the benchmark, tools exhibiting wear of less than 0.06 mm are grouped together. Additionally, programs with broken tools are separated to ensure the smooth operation of individual programs.
(4) Logical control
The accuracy of the rectifier pillar is relatively high. However, tool wear can significantly affect this accuracy, as the deviation caused by cutting wear often exceeds the acceptable size tolerance. To maintain dimensional accuracy during automatic processing, it is essential to incorporate online measurement between the rough and fine machining stages. This allows for adjustments based on the measured values.
The relevant logic for these adjustments is programmed into the CNC system. Two commonly used logical controls are implemented for the rectifier pillar parts.
During the rough machining stage, the system must include features for inputting measurement allowances and for automatic tool changes. The design control program for these functions is outlined as follows:
T=”X63”
TCH; Automatic tool change
M3 S220 AA: G0 X=-213.6+1 Y=R2 Z=R360; Allowance call address
G1 F100 Y=-R2 M8 G0 Z200 STOPRE R360=R360－0.1 IF R360>0.1 GOTOB AA
For the finishing stage, it should have the functions of measuring allowance and automatic compensation. For this purpose, the “1+1” double-layer control structure is designed as follows.
N1 T=”MT”
N2 TCH N3 G55 G0 B0 N4 X-55Y=138.9/2 N6 TRANS Z=213.6+24 N7 Z50 CYCLE977(200xx,,,1,….01,,,,,1,1) ；Measurement feature status
N8 R378=_OVR[16] ；Residue call address
N9 G0 Z1200 IF R378>0.01；Residue logic judgment
CALL “S_11_JX_340_1.MPF”
ELSE；Residue logic judgment
IF R378<(－0.1) MSG(“Residue abnormality”) GOTO BB M09 TRANS BB: M30
By integrating the residual data from the rectifier pillar detection into the CNC program, automatic compensation of feature accuracy is achieved.
(5) Processing simulation: Import the 3D model and processing program into the simulation software for measurement and cutting simulation to ensure that cutting, jumping, and measurement actions are safe and reliable.
(6) Compensation processing: By simulating the processing, we can conduct actual debugging to verify the design process and measurement feasibility. The finished parts are illustrated in Figure 4.
4.4 Effect Analysis
The automation process has successfully completed the milling sequence of the rectifier pillar, leading to significant improvements in the processing cycle. The operator no longer needs to intervene in the CNC cutting service. After implementing this automation, the actual operation time was reduced from 96 hours to 48 hours. Additionally, cutting time decreased from 46 hours to 44 hours, while auxiliary time dropped from 50 hours to just 4 hours. This reduction has greatly minimized the auxiliary time for the front-line operators.
05 Conclusion
The implementation of the typical casting automation process successfully demonstrated the feasibility of integrating on-machine detection probes with digital modeling and CAM programming. This created a comprehensive solution for ship castings that includes detection, analysis, and compensation. As a result, the casting processing has shifted from an “experience-driven” approach to a “data-driven” one. This transition has significantly reduced the need for front-line operators to intervene in the workpiece processing, providing new momentum for reforming the “one person, multiple machines” processing model.
If you want to know more or inquiry, please feel free to contact info@anebon.com
Anebon sticking to your belief of “Creating solutions of high quality and generating buddies with people from all around the world”, Anebon always put the fascination of customers to start with for China Manufacturer for China zinc die casting parts, milling aluminum plate, customized machining parts, with fantastic passion and faithfulness, are willing to offer you with best services and striding forward with you to make a bright foreseeable future.