Metrology is manufacturing is migrating its role from quality control to being integral to the manufacturing processes whether it be near-line or in-line. Utilizing the metrology data immediately to adapt manufacturing processes is becoming the new norm with ‘time-lagged off-line quality control no longer serving the new dynamic employed in state of the art manufacturing following the Industry 4.0 ethos.
Current trends in advanced manufacturing metrology include:
- Tactile measurement systems migrating to high point density optical measurement
- Automated solutions replacing manual measuring systems
- Relocation of measuring systems from quality room to manufacturing floor
- Quality measurement v statistical process control
Optical measurement solutions offer a full-field measurement rather than the discrete point and individual feature measurement offered by tactile systems and provide highly visual representations of measured data with easier interpretation of actionable data for rapid problem resolution.
Automated metrology solutions allow higher part inspection sampling rates, in many application 100% parts inspection can be achieved, providing high quality data without time-lag or operator influence resulting in higher level of confidence in manufacturing process quality.
While the trend was initiated more than 20 years ago with the tactile coordinate measuring machines (CMM), designed for quality control labs, being relocated closer to production processes by encapsulating the CMM in a protective enclosure the emergence of shop-hardened shop-floor CMM and allowed acceleration of the practise. Today’s generation of advanced automated metrology system are specifically designed for shop-floor usage, many involving well proven industrial robots, with the incorporation of optical sensors offering much reduced inspection times significantly increased throughput. Large field of view sensors provide significantly reduced part programming complexity and time since far fewer robot poses are required and feature extraction from generated point cloud is becoming an automatic software function.
Statistical Process Control vs. Quality Measurement
Traditional optimization of manufacturing processes, based upon statistics, assumes that “Quality of the finished product depends on production scattering”. Classical SPC works with a limited number of control features with defined sources of deviation:
- Short-term stability of the process (“noise”)
- Long-term trending (e.g. “temperature change”)
- Specific events ( e.g.“material supplier switch”)
- Control charts showing variance, tolerance limits, process capability etc.
- Output: Warnings based on chosen control features
SPC offers limited ability to perform root cause analysis of collected data.
Analysis of part quality using complete surface data obtained from automated optical inspection systems, either integrated into the production process or near-line provides real-time visualization of deviations using color plots providing an intuitive understanding of complete part metrology and comprehensive detection of local effects.
Further detailed evaluations can be performed as necessary on individual points, features, part sections etc.
Quality Measurement provides reliable part qualification with rapid root cause analysis.
The goal for advanced manufacturing operations is to optimize manufacturing processes to assure product quality.
Process Control + Quality Measurement + Root Cause Analysis + Problem fixing = Process Optimization
Process Optimization Requires Quality Measurements
Discrete point measurement can perform process control only as it supplies an incomplete metrology description of the part – even if 100% of parts are measured. Process Optimization requires a change of philosophy making quality measurements available as fast as possible to improve process knowledge and accelerate process optimization.
One next step in the evolution of metrology’s role in advanced manufacturing is ‘Digital Assembly’ whereby Quality Prediction of the full assembly process can be digitally visualized before the physical product assembly occurs.
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