Global A&D industry revenue is expected to begin to recover this year following a challenging 2020, according to Deloitte. Though the commercial sector has been disproportionately more impacted than the defense sector, strengthening and preserving innovation across the entire industry depends on its continued willingness to adapt to smart manufacturing technologies.
Embracing the digital transformation is key to growing out of volatility during this recovery period—and the defense industry’s success with Quality 4.0 tech proves how they support resilience in the face of uncertainty.
Recent events boosted worldwide digital transformation efforts. However, moving further into the global recovery period, these digital mechanisms are not just a necessity but also a standard. The demonstrated value of data is one of the primary drivers of this shift. For A&D, there’s value in creating a digital thread to incorporate into the digital fabric of the supply chain that connects to each stage of the manufacturing process. Developing this strategy through digitalization unlocks strengths that provide stability in the face of disruption.
Quality 4.0 strategies employ a data-driven approach to quality processes. But these methods require high-quality precision data to be effective. A metrology-grade blue light 3D scanner enables the collection of such data, working as a digitizer for critical aerospace components.
The resulting digital twin provides opportunities for updating antiquated inspection processes.
For example, traditional thickness measurement methods often involve costly destructive techniques like cutting up high-value parts, such as turbine blades. Digitalizing this inspection process saves time and money, and the resulting data creates a digital record of the part’s validation that accompanies it for life.
Successful A&D manufacturers currently accelerate inline production inspection using batch processing systems that enable the automatic robotic transfer of parts from an automated 3D scanner integrated with a programmable logic controller interface station. The machine uses advanced computation to quickly determine if critical parts will pass inspection.
Enhancing the inspection process through this form of automation reduces the time necessary to inspect smaller components.
However, for larger parts and assemblies, A&D manufacturers achieve the same improvement with larger, automated 3D scanning and inspection solutions or autonomous guided vehicles equipped with blue light 3D scanning systems.
Automation helps manufacturers manage the quality process by reducing repetition, creating a repeatable process and eliminating human error.
A&D companies adopting automation strategies are gaining advantages and strengthening the industry’s rebound while protecting its future—by using technology to stay ahead of foreign competition.
Quality 4.0 initiatives use data to measure quality and analyze, predict and solve business challenges. For example, performing trend analysis using 3D measurement data reveals patterns that provide intelligence to accurately predict trends for planning and correcting manufacturing processes. After identifying trends, machining issues, surface defects and geometrical anomalies, this data becomes digital documentation that follows the part for life. The exposed insights prompt successful business decisions.
By tracking minor deviations across production processes and addressing the root cause before creating out-of-spec parts, manufacturers predict and solve problems before they arise.
The industry can move forward smarter, with better information derived from better tech. As advancements in quality processes continue to reshape the relationship between technology and humans, they’ll also continue to cut costs. By automating tedious processes, Quality 4.0 tech provides humans with more capacity to focus on advancing engineering, quality and safety.
Adopting advanced data collection methods and strategies will be a relevant contributor to the aerospace industry’s recovery, perseverance and resilience.
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