SSpirit AeroSystems builds large aircraft structures like fuselages and wings for commercial airplanes, defense platforms, and business jets. It designs, fabricates, and supplies these major aerostructures to customers such as Boeing and Airbus, who then integrate them with other parts to complete airplanes. The company stands out because it spun out from Boeing as an independent supplier and operates at a scale focused on large aluminum and composite structures, with growth in defense and aftermarket services beyond commercial airframes. Its goal is to provide a stable, end-to-end supply of aerostructures, expand its defense and aftermarket offerings, and ensure reliable production across its key programs, including possible alignment with Boeing to stabilize the 737 supply chain.
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5,001-10,000
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Wichita, KS
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2005
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AI in aerospace manufacturing: quality, Drawings and data. AI in aerospace manufacturing: inspection, drawing and spec search, nonconformance analysis and supplier quality, built to keep controlled data in-house. AI in aerospace manufacturing works best where quality and documentation take the most time: inspection, drawing and spec search, nonconformance analysis, first article inspection and supplier quality. This guide covers each use case with published examples, the data it reads, and how to build it so controlled drawings and technical data stay inside your own network. Aerospace manufacturing runs on documented quality. Most suppliers work to AS9100D, the SAE standard that adds aviation, space and defense requirements on top of ISO 9001. Every part carries a drawing, specs, process specifications, customer flow-down clauses, inspection records and certifications, and every deviation generates a nonconformance report. Quality engineers, inspectors and planners spend a large share of their time reading and cross-checking those documents. That reading is where AI pays back. Models find the right spec paragraph, extract characteristics from drawings, group nonconformances by cause and flag likely defects in inspection images. Inspectors and quality engineers keep every acceptance decision. 1. Inspection support. Computer vision and optical character recognition already help on aerospace production lines. * Part validation at Boeing. Boeing engineers use a photo-driven AI tool that reads serial and part numbers to validate installed parts on its Renton and Everett lines, with Boeing South Carolina next. The tool covers more than 1,400 parts and cuts inspection time by more than 17 hours per airplane, according to a Boeing feature from December 2025. * Composite ultrasonic inspection research. Spirit AeroSystems worked with Argonne National Laboratory, Northern Illinois University and TRI Austin on a neural network that flags likely defects in ultrasonic scans of composite structures. The research reported a 7% cut in inspection time, according to Argonne. It is research work, and production use has not been announced. Suppliers apply the same techniques to surface defects, missing fasteners, marking verification and dimensional checks from images. The model flags; the inspector accepts or rejects. 2. Drawing and specification search. A single part can reference a drawing, several material and process specifications, a customer quality clause sheet and a purchase order with its own flow-downs. Engineers and planners ask the same questions every day: which spec governs this heat treatment, what surface finish does this note require, which revision of the process spec applies to this customer. A retrieval system over drawings, specs and flow-down documents answers those questions with citations to the exact paragraph and revision. Each indexed chunk carries its document type, revision and any export-control marking, and retrieval checks the user's access before it reads a document. 3. Nonconformance analysis. Nonconformance reports describe what went wrong, on which part, at which operation, with free-text notes from inspectors and engineers. Read one at a time, they support a material review decision. Read together, they show patterns: a feature that keeps going out of tolerance, a supplier whose lots repeat the same finding, an operation that drifts on a particular machine. Language models classify each report by defect type, feature, operation and likely cause, then group related reports across months. Quality engineers see clusters with their evidence, which speeds up root cause and corrective action work. The material review board still decides the disposition of each part. 4. First article inspection support. AS9102 Revision C sets the requirements for performing and documenting first article inspection. Much of the effort goes into listing every design characteristic from the drawing and specs, then recording the measured result for each. AI can draft the characteristic list from the drawing and notes, number each characteristic, and pull measurement results from inspection equipment exports into the right fields. The quality engineer checks the list against the drawing and signs the report. 5. Supplier quality. Primes and Tier 1 suppliers receive certificates of conformance, material certs and inspection data from their own supply base. AI checks incoming certs against the purchase order and spec requirements, flags mismatches in material, heat lot or revision, and builds a running view of each supplier's findings. Supplier quality engineers focus their audits and visits where the data points. Wondering where AI fits your roadmap? Get a directional read in 30 minutes - no pitch, no commitment. 6. Work instructions and process planning. Manufacturing engineers turn drawings and specs into routings and work instructions for the shop floor. When a drawing revision lands, every affected instruction needs updating. A language model can compare the old and new drawing revisions, list the changed characteristics and notes, and draft updates to the instructions and inspection plans that reference them. The manufacturing engineer reviews each change and releases it through your normal change control. The same index helps new staff. An operator or inspector can ask why an operation is done a certain way and get the spec paragraph and engineering note behind it, which shortens training on complex parts. How to measure results. Each use case has a natural measure that quality and engineering teams already understand: * Inspection support: inspection hours per part or per aircraft, and defects caught before they leave the station. * Spec search: time to answer a spec question, sampled before and during the pilot. * Nonconformance analysis: time from NCR to root cause, and the recurrence rate of grouped defects. * First article inspection: engineer hours per FAI report, and the number of characteristics corrected in review. * Supplier quality: cert mismatches caught at receiving, and audit hours per supplier. Agree on a baseline before the pilot so the results speak for themselves. AI in Aerospace Manufacturing at a Glance. The table maps each use case to the data it reads, the control status that data usually carries, and who decides. | Use case | Data it reads | Typical control status | Who decides | | Inspection support | Part images, scans, markings | Can reveal controlled design details | Inspector | | Drawing and spec search | Drawings, specs, flow-downs | Often ITAR or EAR controlled, always customer proprietary | Engineer or planner | | Nonconformance analysis | NCRs, MRB records, corrective actions | Customer proprietary, may reference controlled features | Material review board | | First article inspection | Drawings, specs, measurement exports | Inherits the drawing's control status | Quality engineer | | Supplier quality | Certs, conformance records, supplier findings | Commercially confidential | Supplier quality engineer | Keeping controlled data in-house. Drawings and specs for defense articles count as technical data under 22 CFR 120.33, and releasing them to a foreign person counts as an export even inside the United States. Commercial drawings carry customer proprietary terms. An AI system that reads them needs the same boundary and access rules your document control system already applies. Most aerospace suppliers meet that need with a self-hosted model on their own servers, an air-gapped network for the most restricted programs, or a private cloud tenant approved by their export compliance team. Its ITAR compliance software for AI is built that way, with markings carried into every answer and a per-query audit log. Where to start. Pick one use case with a clear owner and data you already hold. Nonconformance analysis on the last two years of NCRs is a common first step, because the data sits in one system and the quality team can judge the groupings quickly. Spec search for one product family follows naturally, and inspection models come later once labelled images are collected. Aerospace suppliers that also serve industrial customers can extend the same stack across their wider manufacturing AI work. Running quality and engineering on controlled drawings and specs? NeuralChainAI builds private AI for aerospace manufacturers, from spec search to nonconformance analysis, inside your own boundary. See its AI in aviation and aerospace work or book a strategy session. Frequently asked questions on AI in aerospace manufacturing. AI in aerospace manufacturing supports inspection, drawing and specification search, nonconformance analysis, first article inspection and supplier quality. Boeing, for example, uses a photo-driven AI tool to validate part and serial numbers on its production lines. In every use case an inspector, engineer or review board keeps the acceptance decision. Stop guessing whether AI fits your problem. 30 minutes with a senior consultant. Walk away with a one-page scoping summary either way.
Boeing has uncovered hundreds of millions of dollars in additional liabilities at Spirit AeroSystems following its $8.4 billion acquisition of the fuselage supplier in December 2025. Spirit's assumed liabilities now exceed identifiable assets by roughly $1.9 billion, including $1.52 billion tied to below-market customer contracts. Boeing repurchased Spirit's factories after selling them in 2005, reversing an outsourcing strategy following safety failures including two fatal 737 MAX crashes and the 2024 Alaska Airlines door-plug incident. Despite these challenges, Boeing reported operational improvements in the second quarter, with revenue rising 8% to $24.56 billion and free cash flow turning positive at $631 million. The company reduced fuselage defects by 45% through new inspections at Spirit's Wichita facility. Boeing maintains a $715 billion backlog and targets $10 billion in annual free cash flow long-term.
Boeing may be building the ecosystem for its next commercial aircraft. Former Spirit AeroSystems engineering and technology chief Sean Black will lead development of the manufacturer's future production system, according to report September 10, 2026 10:05 Boeing has begun laying some of the industrial foundations for its next commercial aircraft, even as the company remains years away from formally launching a new single-aisle program. Sean Black, who has led Boeing's Wichita operations since the reintegration of major parts of Spirit AeroSystems, has been selected to lead development of the company's future production system, according to The Air Current. The outlet reported that the role will be critical as Boeing prepares the groundwork for an all-new single-aisle aircraft. Boeing has not announced such a program or disclosed when it intends to make a decision on its next commercial aircraft. Black's background makes the appointment particularly notable. Before Boeing reacquired major Spirit operations, he served as Spirit AeroSystems' chief technology officer and senior vice president of engineering, research and technology. He subsequently took responsibility for the Wichita operation as it returned to Boeing. His new assignment suggests that work surrounding Boeing's next aircraft is extending beyond studies of aerodynamics, propulsion and configuration to another fundamental question: how an all-new airplane would actually be produced. Breaking with 737 legacy. That issue carries particular significance for Boeing. The company's last major decision on a new single-aisle aircraft came in 2011, when it chose to re-engine the 737 rather than proceed with a clean-sheet design in response to the Airbus A320neo. The decision produced the 737 MAX, extending an aircraft architecture whose origins date to the 737-100, which first flew in 1967. Although successive generations introduced major changes in engines, avionics, wings and systems, the basic configuration imposed constraints on how far the design could evolve. Boeing will eventually face a different decision. Rather than finding another way to extend the 737, the manufacturer is expected at some point to develop a new aircraft to address the market currently served by the MAX and potentially compete further into the larger end of the single-aisle segment. No configuration has been selected publicly, and Boeing has not said that its next commercial aircraft will be a direct one-for-one replacement for the 737 MAX. The eventual design could cover a different range of sizes and missions from today's family. The competitive environment has also changed substantially since Boeing selected the MAX. Airbus has expanded the upper end of its single-aisle offering with the A321neo and its longer-range derivatives, while Boeing's largest version, the 737 MAX 10, has yet to complete certification. Production becomes part of aircraft development. Preparing a new production system early could allow Boeing to make a break with its recent past. A clean-sheet aircraft provides an opportunity not only to introduce a new airframe and propulsion system, but also to reconsider how structures are designed, assembled and moved through factories. Boeing has experimented with digital engineering and new manufacturing methods on other programs, including the T-7A Red Hawk trainer. Lessons from those projects could influence how the company approaches a future commercial production system, although Boeing has not disclosed which technologies would be adopted for its next airliner. Black arrives at the assignment with direct experience of another important part of that industrial equation. Spirit's Wichita operation produces major Boeing structures, including 737 fuselages, and its reintegration gives Boeing direct control over manufacturing activities that had been separated from the company for two decades. Boeing has also committed US$1 billion to its Wichita operations over three years, covering facilities, equipment, training and production capabilities. Addressing manufacturing early could allow Boeing to develop the aircraft and its production system in parallel, rather than treating production primarily as a later stage of the program. Decision still ahead. Boeing is still concentrating on its existing commercial programs, higher production rates and certification of the 737 MAX 10. The company expects FAA approval in the coming months, which would allow it to turn its attention to the huge backlog accumulated for the variant. Meanwhile, CEO Kelly Ortberg has said the company needs further technological progress, particularly in propulsion, before it commits to a new commercial aircraft. Ricardo meier. Journalist covering the aerospace industry for over 20 years, with a focus on data and long-term industry trends.
The order and the backlog. Airbus revealed the deal in its monthly orders and deliveries update, and it was first reported by FlightGlobal. Airbus did not identify the buyer, a common arrangement for freighter deals, where lessors and cargo carriers often ask to stay anonymous until closer to delivery. The A350F backlog now stands at 115 firm orders. Named customers over the program's life have included Atlas Air, the largest with about 20 aircraft, along with Etihad Airways, Singapore Airlines, Cathay Pacific, Silk Way West and the freight arms of CMA CGM and Air France-KLM. Some early commitments have since been trimmed or restructured as the schedule slipped. A new order for an untested aircraft carries some signal of confidence, though an undisclosed buyer limits how much can be read into it. Airbus has repeatedly said freighter demand remains firm despite the delays. A late first flight. Airbus launched the A350F in 2021 and once aimed to fly it in 2025. Supply chain problems, most visibly at the Spirit AeroSystems plant that builds the A350's center fuselage, pushed the schedule back by roughly two years. The first of two flight-test aircraft rolled out of the Toulouse final assembly line in August in a special parcel-carton livery. Airbus is aiming for a first flight around September 24, though it has said the date could slip into October. Certification and the first delivery are planned for the second half of 2027. Why airlines are waiting for it. The A350F is a twin-engine freighter derived from the A350 widebody. Airbus lists a payload of up to about 111 tonnes (245,000 lb) and a range near 4,700 nautical miles, and says it burns around 20 percent less fuel per trip than the Boeing 777F it is meant to replace. Timing matters as much as fuel burn. A tighter ICAO carbon dioxide standard takes effect for newly built aircraft in 2028, and the current 777F does not meet it, so Boeing plans to end its production. Boeing's replacement, the larger 777-8F, is not expected to enter service until later in the decade. That gap is the core of the A350F's sales case. Air cargo demand has held up, much of the world freighter fleet is built on aging 747s, 767s and MD-11s, and for a stretch of years the A350F may be the only new aircraft in its size class that an airline can order without an emissions problem waiting on delivery. The date is still a target Airbus has moved the A350F schedule several times since 2021. A late-September first flight leaves little margin before year-end, and the second-half-2027 delivery goal depends on a certification campaign the company itself calls very dense. Airbus is separately running behind on its 2026 delivery target, a reminder that firm orders and a full backlog do not by themselves keep a program on schedule. The latest aviation news and stories sent to your inbox. Tim is the owner and lead editor of AeroCorner since 2019, overseeing aviation content covering aircraft, airlines, airports, and the broader aviation industry. Through years of researching, writing, editing, and publishing aviation-focused content, he has developed extensive practical knowledge of commercial aviation and air travel. Based in Asia and a frequent traveler himself, Tim also brings firsthand passenger experience to AeroCorner's coverage. Outside of publishing, he has also explored aviation firsthand through hands-on flight training in New Zealand.
GEFERTEC launches arcTitan, a Sealed-Chamber WAAM System Built for Titanium. Berlin-based company GEFERTEC has launched arcTitan, a wire-arc additive manufacturing (WAAM) system built specifically for titanium processing, combining a fully enclosed inert gas atmosphere with a plasma-based deposition process. The company says the system achieves stable, reproducible process control by holding oxygen levels inside the build chamber to between 10 and 15 parts per million throughout the entire build. Purpose-built for a difficult material Titanium's high strength-to-weight ratio and corrosion resistance make it a preferred material for aerospace and energy applications, but its sensitivity to atmospheric contamination during processing has long complicated additive workflows. GEFERTEC's answer is a sealed build chamber flooded with shielding gas, removing the need for external enclosures or auxiliary gas systems, paired with a plasma-based arc process the company says allows tighter control over heat input and more consistent part quality. "The greatest challenge in the additive manufacturing of titanium is ensuring reproducible process conditions," said Johannes Zuckschwerdt, GEFERTEC's managing director. "With the arcTitan, we have therefore developed a system in which the build chamber, welding process and peripheral equipment have all been designed specifically for this material." GEFERTEC says every relevant subsystem, from gas management and sensor technology to wire feeding and parameter control, was engineered around titanium's specific requirements rather than adapted from a general-purpose machine. The arcTitan offers a 2 x 0.7 x 1 meter build envelope and a maximum deposition rate of 3 kg per hour, and GEFERTEC describes the system as modular, allowing configurations to be tailored to individual end users. GEFERTEC positions arcTitan as being of particular interest to aerospace and energy companies, two sectors where titanium's weight and corrosion advantages are most commercially valuable and where certification standards make repeatable process conditions especially important. Part of a four-machine WAAM family GEFERTEC positions arcTitan as one tier in a broader machine lineup rather than a one-off product. The company's current range spans four systems: the arc10X, a versatile industrial platform for general WAAM production; the arc80X, built for larger components with build volumes up to 8 m^3; arcTitan, purpose-built specifically for titanium, aluminum, and nickel-based alloys; and arcLab, a smaller, budget-oriented system aimed at research and training environments. All four share a common feature set, including GEFERTEC's 3DMP process monitoring, automated ignition-error correction, and Siemens SINUMERIK ONE control, and the company describes the whole range as CE-certified, scalable, and built to integrate into existing manufacturing workflows. That structure suggests arcTitan isn't a standalone experiment but the reactive-material tier of a segmented product strategy, where customers select a machine based on build size and material rather than buying one general-purpose system and configuring it after the fact. GEFERTEC has not disclosed when arcTitan will be commercially available or which customers, if any, are already testing the system. Specializing the Machine to Win the Material GEFERTEC's strategy with arcTitan is to stop treating titanium as an add-on option box and instead build a standalone system engineered end to end for one material. That matters because titanium's biggest AM adoption barrier is process repeatability under regulatory scrutiny, and aerospace and energy buyers are more likely to qualify a dedicated system than a general machine with a titanium mode. Other WAAM specialists have followed the same logic of narrowing focus to win high-value titanium work. Cranfield University spin-out WAAM3D built its reputation on exactly this kind of material-specific engineering, showcasing a titanium tank for aerospace manufacturer Thales Alenia Space that saved 200 kg of material, cut costs by 65%, and reduced CO2 and energy use by 80% compared with conventional processes. Norsk Titanium took a similar path from the metal-supplier side rather than the machine-builder side: Spirit AeroSystems partnered with Norsk Titanium to produce titanium parts for Boeing aircraft using Rapid Plasma Deposition, a wire-fed DED variant, demonstrating the approach at scale for large aerospace structures. GEFERTEC's bet is that owning the full system, rather than the material expertise alone, is what turns titanium WAAM from a research capability into a qualifiable production process. 3D Printing Industry is inviting speakers for its 2026 Additive Manufacturing Applications (AMA) series, covering Energy, Healthcare, Automotive and Mobility, Aerospace, Space and Defense, and Software. Each online event focuses on real production deployments, qualification, and supply chain integration. Practitioners interested in contributing can complete the call for speakers form here. Explore the full Future of 3D Printing and Executive Survey series from 3D Printing Industry, featuring perspectives from CEOs, engineers, and industry leaders on the industrialization of additive manufacturing, 3D printing industry trends 2026, qualification, supply chains, and additive manufacturing industry analysis. Featured image shows arcTitan, a Sealed-Chamber WAAM System Built for Titanium. Image via GEFERTEC.
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Industries
Company Size
5,001-10,000
Company Stage
IPO
Headquarters
Wichita, KS
Founded
2005
Find jobs on Simplify and start your career today
Don't see your dream role? Check out thousands of other roles on Simplify.Browse all jobs