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Ray Karadayi

CMSC

Blue Light Laser Sensor Integration and Point Cloud Metrology

Published: Thursday, August 18, 2016 - 10:12

Dimensional measurement has been used as a part of manufacturing systems for many years. However, although shop floor measurement equipment can be used next to a manufacturing machine and its measurement data can be digitally interfaced to change some manufacturing parameters, machine tools themselves are also being utilized to do more and more of the dimensional measurement tasks.

Touch probes can be found on most manufacturing systems, which can be utilized to automatically perform dimensional measurement tasks. Several manufacturers are now introducing analog probes that can scan parts to collect fast and dense measurement data directly on the machine. This article discusses integration of noncontact blue-light laser sensors directly within the manufacturing system for fast data collection during the manufacturing process. This technique uses the point cloud generated from this sensor for measurement and metrology tasks, and ultimately generates feedback to the manufacturing system to achieve adaptive manufacturing with point-cloud metrology.

Introduction to on-machine measurement

Dimensional measurement on the machine tool has been used ever since parts have been made by automatic machining cycles. With the advancement of the machine tools, scales, controllers, and software, however, more advanced measurement routines are now possible. Bi-directional and fast interface capabilities already available on many of the machine tool controllers make it possible for metrology software to be utilized directly on the machine to process the measurement data and calculate advanced geometrical and tolerance characteristics of parts as they are being made. Having this capability directly on the manufacturing machine opens up the possibility of using metrology calculations as a feedback loop to perform important corrections within the manufacturing cycle. The strategic use of measurement programs through the manufacturing process enables “adaptive manufacturing,” resulting in improved part quality at a greatly reduced total manufacturing cycle time. The need to process faster measurement and higher density data requires integration of noncontact sensors and powerful software that can handle such data while interfacing the machine.

Measurement probes used for manufacturing machines

As the need to gather faster and more accurate data grows, new probes and noncontact sensors are being developed and integrated within machining centers, including:
• Kinematic probes. Traditionally, kinematic probes have been used for simpler applications to measure a location or a size. Although these probes produce repeatable measurement for a unidirectional measurement, such as measuring along the X axis, their trigger points vary when measuring along other directions, which are required for more complex shape measurements. This requires more time-consuming calibration of the probe and presents challenges in use with a true on-machine metrology application.
• Strain gauge probes. These probes provide very repeatable measurement data which can be used by a coordinate measuring machine (CMM)-style measurement program, which produces high-accuracy metrology data within the manufacturing machine. However, the measurement rates are slow, as the points are taken one point at a time
• Analog scanning probes. These probes collect high-density data by sweeping over the part surfaces, and have been used on CMMs for many years. Several manufacturers are developing analog probes for their manufacturing centers so that part measurement cycles can be reduced without having to sacrifice measurement accuracy. The Sprint probe seen in figure 1 is an example of an analog probe used for in-process measurements.


Figure 1: Analog scanning probe and its data density report

• Noncontact sensors. Noncontact blue light laser sensors, which are the primary focus of this article, are integrated within the manufacturing system to help collect even higher density data at shorter times. The recent surge in interest in this technique, especially in aerospace manufacturing, helped fuel innovation in the integration of laser sensors for on-machine, quick data collection. Figure 2 shows an example of a laser integration on a five-axis machining center used for composite manufacturing


Figure 2: Laser sensor integration and its point cloud


Figure 3: Keyence LJ-V7000 series blue light laser


Figure 4: Sensor coordinate system

Blue light line scanning laser

A Keyence LJ-V7000 series blue light line laser, as seen in figure 3, is used as the measurement sensor within the machining center. These sensors are traditionally used to measure features that are moving in front of it while the sensor is stationary. Integrating such a device within the coordinate system of a five-axis machine tool presents the following challenges.

• Sensor mathematical model. At each trigger, the blue light sensor produces a number of points along a line in a coordinate system attached to the sensor frame, as seen in figure 4. This coordinate system needs to be calibrated and the data transformed to merge with the machine position.


• Sensor point data. The point data gathered from the sensor model, as seen in figure 5, is used along a line with up to 800 points at each interface 50 µm apart.


Figure 5: Close up of sensor data

• Sensor focal point. This is the point in the middle of the sensor active region, as seen in figure 6. The machine tool is commanded to bring this point to the intended position during a program execution. This is also the point where all the sensor calibration offsets are calculated.


Figure 6: Sensor focal point

• Physical attachment of the sensor. The blue light sensor is attached to the spindle of the machine by a specially designed bracket, as seen in figure 7. This allows the sensor to be kept within the tool magazine of the machine for automatic loading and unloading.


Figure 7: Blue light sensor attachment to machine tool spindle

• Blue light laser tests on different materials. Tests have been performed to study the scan data quality of the generated point clouds for different materials. A number of materials and parts have been selected which would be suitable for on-machine measurement applications:

° Shiny metal surfaces. Calibration and tooling balls that are not coated with nonreflective material has been used to test the data quality. Because these type of artifacts are used both for calibration as well as datuming spheres, point cloud quality has been tested and results were good as in the image in figure 8.


Figure 8: Scan data on a shiny calibration ball


Figure 9: Scan data on a polished carbon fiber composite


Figure 10: Scan data on a black plastic part


Figure 11: Scan data on a metal part


° Shiny composite material. Both polished and rough composite material are intended parts for the use of a blue light laser sensor. The quality of the data gathered on these parts were very good, as seen in figure 9.


° Plastic automotive part. As plastic injection mold parts are a good candidate for a noncontact scanning application, they have been tested with also good results, as seen in figure 10.


° Machined metal surface. Metal machined parts, especially for aerospace engine components such as airfoils, can also benefit from noncontact scanning with a blue light laser. Scans of this type of material also resulted in very good and clean data which can be used for point cloud analysis, as seen in figure 11.


• Latching of data with machine tool. The laser system integration with the machine has to provide precise data latching between the machine position and the laser data. This is done by using a trigger signal from an external device and capturing the machine position and the laser data with time stamps. The two sets of data are merged into a single coordinate system producing the point cloud of the surface scanned. This system allows very fast and robust data capturing between the two separate systems and is very easy to integrate with the machine tool controller, as it uses existing I/O channels. See figure 12 for an example schematic.


Figure 12: Blue light and machine tool controller data synchronization

Machine tool as CMM

Machine tools can be used to perform complex dimensional measurement functions, much like the CMM, by using a touch probe or a laser scanner. However, there are a number of factors that could change the machine tool characteristics and affect the quality of the measurement data. Unlike a CMM, machine tools are exposed to large forces during the manufacturing process. Their geometry could also change due to thermal effects, as they are located at the shop floor and generate heat by the cutting process. To perform complex dimensional measurement functions on a machining center, certain procedures have to be put in place to assure the reliability of the dimensional measurements. These procedures help monitor these changes and adapt the system parameters to achieve measurement results within the specified uncertainty budgets.

Machine tool geometry
To perform on-machine measurement on a machining center, its geometrical integrity must be monitored and a quick method has to be put in place to verify and recalibrate it. Although machining centers are made to be very rigid in their structures, their geometry can change due to temperature fluctuations and large cutting forces applied. On a standard three-axis machine, there could be 21 sources of geometric errors. Although these machines have periodic calibrations, to achieve best results for an adaptive manufacturing application it is best to monitor and correct for these errors more frequently. Because a complete calibration of a machine tool can take a long time and would not be feasible, other quick and automated methods are adapted. Figure 13a shows a ball bar artifact which can be used to measure and verify ball bar distances and report errors in the linear and squareness geometry. Figure 13b shows a simple gauge which can be applied on a five-axis table machine to calibrate the table center and rotation axes.


Figure 13a: Ball bar gauge


Figure 13b: Machine table calibration

Sensor calibrations
For an automatic in-process measurement cycle, measurement sensors such as touch, analog, or lasers are adapted and used by the machine tool very similar to a cutting tool. If a measurement feedback loop, such as a coordinate system, is to be uploaded to the manufacturing system or multiple types of sensors, such as a touch probe or a laser scanner used together, they all need to be calibrated not just to themselves but as a whole. For the blue light laser, a tool must be defined within the machine tool controller to identify it to the system. This definition can be done as a nominal definition defining the geometry of the tool; the actual calibrated values are held within the metrology software used for programming and analysis of the data. The following parameters are calculated from a calibration process and the measurement data are compensated during the program execution cycles as follows:
• Probe runout. This determines the probe tip deviation from its center line. For the touch probe, this would be the offset of the center of the tip from the center line of the machine tool. For the blue light laser, this is the offset of the focal point of the laser beam from its nominal position.
• Effective radius. Because the trigger point received by the system has a delay, depending on the measurement velocity the probe radius would be smaller than its physical size. This value is used to compensate for probe radius during measurements. For blue light laser scanners, the sensor radius is assumed to be zero.
• Lobing errors. For kinematic touch probes, the point of the trigger depends on the contact vector with the surface. This value is similar to effective radius except a different value is calculated for each approach vector. These values can be calibrated and used during the measurements by applying them based on the point’s normal vector.
• Head offsets. When utilizing a five-axis head, the actual probe position is calculated and used as the correction value during measurement cycles. This would eliminate any errors due to head misalignments. For blue light lasers, this would be the offset of the sensors focal point from its nominal intended location.
• Sensor attachment coordinates. If a noncontact laser sensor is used which produces a number of points per measurement, its exact attachment coordinate system to the machine tool must be calculated and used as a part of the calibration. Any data received from the blue light laser would be transformed through this matrix before merging with the machine position.

Metrology software
To perform the complex mathematical calculations required for metrology-based, real-time decision making, a powerful CMM software needs to be integrated within the manufacturing system. Because the system is expected to function by itself without human interaction, it also needs to work autonomously within the manufacturing process. The following characteristics are required from a software program to truly make a machine tool function similar to a CMM:
• Offline programming. A computer-aided manufacturing (CAM)-style programming environment with good machine tool virtual modeling, simulation capabilities, automatic path generation with collision avoidance, and complete geometrical fitting and tolerancing functionality is required. Programming languages such as DMIS also allow you to interface and collaborate with CMMs for efficient programming.
• Bi-directional interface. A direct and bi-directional interface is a must to analyze data in real time as soon as the measurement of a feature is completed. The calculated metrology characteristics are used as a part of the on-the-fly decision making and written back to the machine tool controller as a part of the adaptive cycle. Figure 14 shows a block diagram of such a communication between the software and the machine tool controller.

Figure 14: Measurement feedback for on-machine tool measurement

• Ability to handle high-density point cloud data. When interfacing with a laser to measure large parts, very large amounts of data will be gathered. The software, in addition to offering a live interface with the machine tool, must also be able to handle the display and interaction with such data.
• Geometric feature extractions. For on-machine geometrical feature measurements and geometric dimensioning and tolerancing (GD&T) applications, an automatic feature extraction is necessary. Most point cloud systems today are offline and need operator interaction to calculate the required features. An on-machine measurement software that will interface with a laser system should also have a robust automatic feature extraction capability.
• Ease of operation. The measurement program must be integrated into the machining center similar to any other cutting program. This allows the program to be integrated as a part of manufacturing cycles and can be automatically started by itself. A G-Code NC program is created by post-processing the DMIS measurement program and resides in the controller. This program, like any other cutting programs in controller-native language, is used as a part of the manufacturing cycle allowing multiple programs to work along with the cutting programs.

Blue light laser sensor integration with the machine tool

The laser sensor is used to create a point cloud by scanning a part and calculating metrology information about the part on the machine. Some of this information will be used to provide feedback to the machine tool controller so that the benefits of having an on-machine measurement can immediately be applied to the process. To achieve this goal, the laser sensor will have to be introduced into the system in relation to other tools and calibrated to other tools such as a drill bit or a water jet focal point, as follows:
• Laser tool definition. A nominal mathematical model of the sensor is defined and entered into the machine tool controller as one of the tools. Once the calibration is performed, the actual tool data will be uploaded to the controller or can be held by the software, as seen in figures 15 and 16.


Figure 15: Blue light laser offsets and directions


Figure 16: Laser tool definition into controller

• Calculate laser sensor coordinate system. The relationship of the sensor coordinate frame to the machine tool control coordinate system has to be calibrated. This is done by scanning a calibration ball at different sections. The software then fits this data to calculate the laser frame coordinate system, as seen here:

• Master ball measurement. The master ball that will be used for the calibration can be first measured by using a touch probe if one will be used together with the blue light laser sensor. Otherwise, it can be first measured by the laser sensor at the 0, 0 orientation of the head. Although having the master ball fixed within the machine center would allow automation of the whole calibration process, a master ball can also be put in a random location before starting the calibration process.
• Calibrating multiple head orientations. The laser sensor attached by the spindle on the machine can be indexed to any orientation in which the machine can move, similar to a touch probe or the cutting tool. The head orientations that will be used in a program can be calibrated to achieve more accuracy. This is done by automatically generating programs based on the desired head positions and executing these programs automatically. A calibration program for all the head positions in a scanning program can also be created automatically to quickly calibrate these positions, as seen in figure 17.


Figure 17: Generating a calibration program from a grid

The calibration process is automatic and iterative. Once a program is generated, this program is stored within the machine tool controller and can be run by the operator or as a part of a program sequence. Based on the calibration quality, the software interacts with the controller and can iterate the process. Figure 18 shows the results of a calibration program.


Figure 18: Calibration offset reports

Calibration verification

Once the sensor is installed and calibrated, a standard test similar to ISO 10360-5 can be performed to verify its calibration and calculate an uncertainty value for the whole system. For this test, a program is generated to measure a tooling ball by using the calibrated head orientations.

First, a tooling ball is measured at the 0, 0 orientation and set as the origin. Next, a program is created to measure the sphere for each of the calibrated angles. Typically, the following angles are used:
• 0°, 0°
• 0°, 90°
• 45°, 0°
• 45°, 90°
• 45°, –90°
• 45°, 180°

The measurement results are then compared to determine the largest deviation, as seen in figure 19.


Figure 19: Examples of tooling ball measurement at different head orientations

This test can be completed in less than 10 minutes and generate an uncertainty statement before running the actual measurement programs. Individual feature calculations are reported to determine the uncertainty. A final sphere calculation using all the point clouds from each head orientation can also be performed to get an accuracy statement of all the point cloud data scanned by different head positions. Following is an example of a report from this calculation:

On-machine part measurement with blue light laser

Scanning programs
A scanning program can be created offline by using a CAD model to create point cloud representation of surfaces. In the case of scanning a trim line of a part that will be manufactured, a scanning program can be created directly from the cutting program. By scanning the surfaces along these trim lines, the exact shape of the part profile is created and can be used for further analysis, as seen in figure 20.


Figure 20: Point cloud generated along a trim line of a component and a detailed view of a section

Curve extractions
After scanning a part, curves can be created from the point cloud in several ways. A 2D spline curve can be extracted automatically by specifying the section coordinates for an airfoil blade or for a mold being manufactured on a machine. Figure 21 shows a point clouds of a blade and sectional curves extracted from it. These curves can then be used to create best-fit alignments or profile reports, or to calculate the airfoil parameters.


Figure 21: Curves extraction from point cloud of a blade

Irregular curves extracted from an NC cutting program cutting path can also be dropped on to the point cloud to create the exact trimming line profile of a part. This data then can be used to correct the multi-axis machine path to make sure the part is manufactured without requiring a long setup process and to avoid scrap. Figure 22 shows a magnified error profile of a curve extracted from a point cloud of a composite aerospace component.


Figure 22: Profile error of a NC trimming path on a point cloud surface

Geometric feature extraction
Measurement of geometric features are necessary for both pre-process needs such as setting up a part coordinate frame before cutting and post-process to actually create a full dimensional inspection report of the part. Initial part setup could be accomplished by measuring a king and a queen pin on a composite part or datum spheres located around the part to create an alignment. These coordinate systems can then be used by the reposting process to adapt the cutting program to the part orientation or simply sent to the controller as a work offset. After the part is manufactured, a finished part can be scanned and its trim or edge profiles can be reported as a pre-process analysis. Any drilled holes can be extracted from the point cloud and their true positions can be reported along with any other GD&T callouts, creating a final inspection report. Unlike a manual or offline operation where an operator can interact with the software to perform these calculations, for the NC machine these feature extractions are done automatically without any user interaction.

The image in figure 23 shows point clouds and extracted features from a National Aerospace Standard (NAS) part. In this example, first the three tooling balls are scanned and spheres are extracted. An alignment is created from these spheres to match the CAD model. The calculated alignment is uploaded to the controller as a work offset and the rest of the part is scanned. Ultimately, the all of the geometrical features are extracted and their dimensional reports are created.


Figure 23: Blue light measurement of a standard NAS part

The image in figure 24 shows the polar form plot of one of the circles extracted as a result of this process.


Figure 24: Detailed profile report of an extracted circle

Figure 25 shows the positional report of one of the circles extracted.


Figure 25: Position report of a circle extracted from the point cloud

Adaptive manufacturing with point cloud data

To achieve a self-adapting manufacturing process, dimensional measurement programs can be utilized at several different stages of the manufacturing cycle. During pre-processing, part location and orientation can be measured and used as a work offset to help part cutting. The part surface profile can be measured before the final finishing process to calculate tool wear and offset correction parameters for specific surfaces. At post-process final inspection, a CMM-like complete measurement program can be executed to generate a final metrology analysis of the finished product for final inspection reports and statistical trend analysis.

Manufacturing of large and non-rigid parts, like most aerospace components, present many challenges. Composite parts could be hard to fixture to produce a smooth cutting process. In addition to their large and usually non-rigid structures, these parts could also be deformed coming from the heat treatment process or distorted by the holding fixtures. Trimming a composite part with a water jet machine in particular requires the profiles along the trimming lines to be within certain tolerance for a smooth finish and to avoid possible crashes.

By using a blue light laser scanner, a process can be put in place to not only set up and prepare the part easily for the cutting process, but actually automate it so that it could be performed with minimum interaction with the operator. This process includes:

• Scan datum balls to create a coordinate system. Tooling balls used for part alignment are scanned and their spheres are extracted from the point cloud. These are used to create an alignment according to part setup requirements, as seen in figure 26.


Figure 26: Tooling ball scanning and geometry extraction from point cloud

• Scan trimming and drilling paths as defined from the cutting program. Refer again to figure 22, which shows an example of the magnified profile error over a cutting tool path.
• Apply reposting to fit the cutting programs to part location and shape. The original cutting programs are processed through a reposting process and a new cutting program is generated. Once reposting is done, the regenerated cutting tool program be simulated for verification before running on the machine, as seen in figure 27.


Figure 27: Simulation of a reposted program with the actual part orientation and shape

• Perform the trim and drill operation. The new adapted cutting program can now be executed to produce the part.
• Post-process inspection. A final scanning program is run on the finished part to generate a complete part measurement report.

NC Program reposting

NC program reposting is the process of regenerating an NC cutting program based on the measured part orientation and part shape. This process takes a nominal cutting program as an input and applies the actual part information from the measurement results such as the point clouds generated by scanning a part. Figure 28 shows the process of reposting a cutting program.


Figure 28: Reposting of a NC program code by using blue light laser data

A cutting program reposting helps solve two problems during a manufacturing process:

1) Adapting five-axis parameters. A cutting tool location, usually the bottom of the tool, can easily be handled by applying a work offset within the controller for both part location and part rotation. However, when the tool orientation must match the actual part rotation, its G-Code program must be reposted to update the tool orientation as well. A grinding tool where the contact surface has to be at a precise orientation with the part, or a water jet where the cutting vector must be precisely aligned are examples of this. Although some machine tool controllers can automatically adjust this orientation by using the work offset, in most cases this feature is not present or very difficult to use. NC program reposting for a five-axis machine with a program using the A, B, C angles of the head or I, J, K vectors of the tool can recalculate these parameters, maintaining the tool orientation with the part. Figure 29 shows a cutting path and head adapted to an actual part orientation.


Figure 29: Adapting tool vector and cutting path to measured part orientation

2) Adapting machine motion to part shape. Sheet metal and composite parts that need to be trimmed may be out of their actual shape due to fixturing of non-rigid structures or the heat treatment process. A part’s residual stresses also change its profile after a portion is removed. In particular, trimming of these parts with water jet cutting takes a long time to prepare or causes part defects. But measuring the part profile and reposting the cutting program to the actual part shape provides a very quick and inexpensive method which can also be automated. Figure 30 shows how an intended straight cut is made to fit the actual part shape through this process.


Figure 30: Adapting machine motion to actual part shape

Reverse engineering

In some applications, reverse engineering of the actual part shape is necessary to create a custom cutting program. A part that needs to be reworked, such as a repaired blade that needs to be finished on a five-axis machine, might require a custom cutting program. In this case, measurement data such as the point clouds or extracted curves and geometrical features can be exported to a CAM system, which can then create a custom cutting program for the part’s exact location and shape.

For parts that had been repaired by welding material or parts being manufactured on additive/subtractive machines, the actual welded or generated sections can be digitized and the actual curves exported to a CAM system, which can create a custom fitting cutting-tool path for the part’s exact shape. Figure 31 shows an example of an airfoil repaired by welding and finished through a process like this.


Figure 31: An airfoil finished after reverse engineering

Conclusions

A blue light laser sensor can be installed on a machining center by directly loading it, similar to a cutting tool. This integration is very quick and inexpensive, and it can be adapted to multi-axis machining centers. Having a point cloud metrology capability with a measurement software on an NC machine tool, especially for large and non-rigid parts, has several great benefits:
• Reduce setup time and cost. By integrating a closed-loop measurement system with a blue light laser on the machine, the setup and preparation time is drastically reduced.
• Reduce setup costs. Dedicated expensive holding fixtures are not necessary as the part setting and program fitting is done by using the measurement data.
• Reduce dependency on external equipment. Having an on-machine measurement capability both with the touch probe and a blue light laser eliminates the need to bring external measurement equipment to the machine volume, such as laser trackers or portable arms, and can eliminate the need to move the part to an external measuring machine.
• Cutting program fitting. Using the point-cloud data generated directly on the machine, a cutting program can be reposted and adapted to the actual part location and shapes.
• Automated closed-loop manufacturing. As the blue light sensor can be held within the machine tool magazine, the measurement and cutting process can be automated with the exception of manually connecting a data interface cable.
• Reduction on additional repair and scraps. Parts are manufactured adaptively by using the readily available metrology data, which ends up reducing the scrap generated or the need to repair parts.
• Increase product quality. Measuring a part on the machine without having to remove it and adjusting machining parameters based on the measurement results permits the manufacture of high-precision parts. Knowing the part’s dimensional quality and metrological characteristics before removing it from the machine has great benefits and improves the overall performance of a manufacturing facility.
• Manufacturing system factory control. Adaptive measuring systems work with the machining center as a peer-to-peer secured interface, but they are also on the network for measurement data collection and reporting. Measurement results from machines used in a manufacturing facility can be collected in a database and used to monitor the overall factory performance. Evaluating and comparing this kind of data allows better decision making and helps plan for future manufacturing strategies.

In conclusion, today’s competitive manufacturing environment demands the best performance and the best quality at the lowest possible cost. State-of-the-art machine tools and controllers enable integration of a blue light laser sensor and a metrology software within the manufacturing system and is an important part of a modern manufacturing facility.

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About The Author

Ray Karadayi

With a B.S. degree in mechanical engineering, an M.S. degree in mechanical engineering, and a Ph.D., Ray Karadayi is president and CEO of Applied Automation Technologies (AAT) Inc. Karadayi is also the AAT representative in the CMSC.

Comments

Great info

Thanks Ray for this very technical, in depth article. Although most of it flew over my head, it's exciting to read that this sort of technology is making it's way into manufacturing.

On Machine Measurement

There can be no doubt that the use of "on machine" measurement can lead to great improvements in quality. However, to rely upon these measurements for final acceptance is a dangerous path. When generous tolerances are involved, the issue is probably only a minor annoyance but, as tolerances become tighter and tighter, the need for an independant measurement becomes more and more a necessity.

In all likelihood, the on machine system will greatly reduce the number of independant measurements required, but will never eliminate them.

From an operational standpoint, it would appear logical that a properly implemented scanner has inherent advantages over other technologies. It should be possible to easily build and maintain system safeguards at a much lower cost than for other technologies.