Qualified Research Activities for R&D Tax Credits in Manufacturing: 25 Examples

Qualified Research Activities in manufacturing are identified at the activity level:

If you’re searching for Qualified Research Activities (QRAs) in manufacturing, the key is specificity. QRAs for R&D Tax Credits are not “departments” or “projects” in the abstract—they are discrete activities tied to a specific business component (a product, process, formula, technique, or software used in manufacturing) and supported by a real process of technical experimentation.

Manufacturers often assume R&D Tax Credits only apply to “lab coat” work. In reality, many shop-floor and engineering efforts can qualify for Research and Development Tax Credits when they’re aimed at resolving technical uncertainty through iterative testing, trials, modeling, and analysis. The trick is separating true experimentation from routine production.

Below are 25 manufacturing examples that may satisfy the R&D Tax Credit Four-Part Test, along with boundaries that commonly disqualify activities when uncertainty and experimentation are missing.

25 manufacturing QRA examples mapped to the Four-Part Test:

Each example below is written the way an R&D Study often needs to read: an activity-level story tied to uncertainty and experimentation.

1) Reduce scrap without sacrificing performance in a critical dimension:
Qualified Purpose: improve quality/yield. Technical Uncertainty: which parameter/material/tooling drives out-of-spec scrap. Experimentation: trial builds and parameter sweeps. Technological: manufacturing engineering/statistics.

2) Develop a new automated process to replace a manual assembly step:
Qualified Purpose: improve function/performance (cycle time, repeatability). Uncertainty: robot path/fixturing/sensing needed to hold tolerance. Experimentation: pilot cell trials, vision tuning, GR&R. Technological: controls/mechatronics.

3) Change material or tooling to hold a difficult specification:
Qualified Purpose: improve quality/reliability. Uncertainty: how material response and tool wear impact geometry. Experimentation: tool coatings, feeds/speeds, heat treat trials. Technological: materials science.

4) Scale up from prototype to production while maintaining capability (Cpk):
Purpose: performance/quality. Uncertainty: which process variables shift at scale. Experimentation: pilot runs, DOE, control plan refinement. Technological: process engineering.

5) Develop a new welding procedure for dissimilar materials:
Purpose: reliability/strength. Uncertainty: joint integrity under heat input and metallurgy. Experimentation: coupon testing, destructive tests, parameter windows. Technological: metallurgical engineering.

6) Improve a molding process to eliminate sink/warp while meeting strength:
Purpose: quality/performance. Uncertainty: gate location, cooling, resin behavior. Experimentation: mold flow analysis, tool modifications, sample runs. Technological: polymer science.

7) Create a new fixture to reduce variation and improve repeatability:
Purpose: quality. Uncertainty: how clamping/locating affects distortion. Experimentation: fixture prototypes, measurement studies. Technological: mechanical engineering.

8) Develop a heat treat profile to reach hardness without embrittlement:
Purpose: performance/reliability. Uncertainty: time/temperature/quench effects. Experimentation: furnace trials, hardness/impact testing. Technological: metallurgy.

9) Implement an in-line inspection method to detect defects earlier:
Purpose: quality. Uncertainty: sensing modality and thresholds. Experimentation: sensor trials, false-positive analysis. Technological: optics/electrical engineering.

10) Design for manufacturability changes that require engineering experimentation:
Purpose: improved manufacturability/performance. Uncertainty: if design change meets load/fit while manufacturable. Experimentation: CAD revisions, prototype builds, tests. Technological: engineering design.

11) Reduce cycle time by re-optimizing machining parameters:
Purpose: performance (throughput). Uncertainty: safe parameter envelope without chatter/deflection. Experimentation: step tests, surface finish evaluation. Technological: machining science.

12) Resolve chronic field failures by tracing to manufacturing variation:
Purpose: reliability. Uncertainty: root cause mechanism. Experimentation: tear-downs, controlled variation trials, correlation studies. Technological: failure analysis.

13) Develop a new adhesive bonding process to replace fasteners:
Purpose: performance/weight. Uncertainty: surface prep/cure profile needed for strength. Experimentation: lap-shear tests, environmental aging. Technological: chemistry/materials.

14) Create a new coating process to meet corrosion targets:
Purpose: reliability. Uncertainty: thickness, adhesion, process controls. Experimentation: salt-spray, adhesion tests, bath parameter trials. Technological: chemical engineering.

15) Improve yield by redesigning a process step causing bottlenecks:
Purpose: performance/quality. Uncertainty: interaction between upstream/downstream steps. Experimentation: line trials, changeovers, data analysis. Technological: industrial engineering.

16) Develop a new forming process for high-strength alloys:
Purpose: function/performance. Uncertainty: springback and cracking behavior. Experimentation: die iterations, press trials, metrology. Technological: materials/mechanics.

17) Validate a new supplier material specification with performance testing:
Purpose: quality/reliability. Uncertainty: equivalency under load/temperature. Experimentation: comparative test matrix, statistical analysis. Technological: materials engineering.

18) Build and test prototypes for a next-generation product variant:
Purpose: new/improved product. Uncertainty: whether design meets targets. Experimentation: iterative prototypes, test-to-fail, redesign loops. Technological: engineering R&D.

19) Develop an energy-reduction process change that affects product quality:
Purpose: improve process efficiency without harming quality. Uncertainty: thermal/chemical impact. Experimentation: controlled trials, inspection. Technological: process engineering.

20) Create a new packaging method to prevent damage in transit:
Purpose: reliability/quality. Uncertainty: cushioning/retention needed for failure modes. Experimentation: drop/vibration testing, design iterations. Technological: mechanical engineering.

21) Implement a new ERP-integrated traceability approach requiring technical experimentation in manufacturing software:
Purpose: improved quality/compliance. Uncertainty: data capture and system behavior under production constraints. Experimentation: staged rollouts, validation tests. Technological: computer science (manufacturing software).

22) Troubleshoot a tooling wear problem by testing coatings and geometries:
Purpose: improve quality/cost. Uncertainty: which wear mechanism dominates. Experimentation: controlled runs, microscopy, tool life curves. Technological: tribology.

23) Develop a new calibration method to improve measurement system accuracy:
Purpose: quality. Uncertainty: method bias and repeatability. Experimentation: GR&R studies, calibration trials. Technological: metrology.

24) Optimize a chemical process bath to stabilize outputs:
Purpose: quality/performance. Uncertainty: concentration/temperature/pH interactions. Experimentation: lab sampling, control chart experiments. Technological: chemistry.

25) Improve tolerance stack-up by iterating GD&T and process capability:
Purpose: quality/function. Uncertainty: which dimensions drive assembly failure. Experimentation: tolerance analysis, pilot builds, measurement feedback. Technological: mechanical engineering.

Important: these examples become QRAs only when they reflect genuine technical uncertainty and a process of experimentation. Simply “trying harder” in production doesn’t qualify.

The boundary: manufacturing work that should not automatically be treated as QRAs:

Manufacturing organizations can overclaim when they label ordinary operations as R&D. Activities that often fail the Four-Part Test include:

• Routine production runs with no experimental plan (even if complex)
• Standard quality inspections that don’t test a hypothesis or new method
• Cosmetic/aesthetic-only changes (color, branding, non-functional finish) without technical uncertainty
• Ordinary maintenance, repairs, and machine servicing
• Simple debugging or rework that follows known playbooks (no experimentation)
• Copying an existing process “as-is” to a new line without technical challenges
• Basic training, documentation cleanup, or administrative project management

A practical filter: if the team already knows the method and the outcome is reasonably predictable, it’s likely not a QRA.

Evidence should follow the activity: what to save to support your R&D Study:

Good documentation doesn’t replace the technical story, but it anchors it. For manufacturing QRAs, the most persuasive support tends to be contemporaneous and tied to trials and decision-making.

Common support documents include:
• CAD revisions, drawings, and engineering change orders (ECO/ECN)
• Process sheets, routings, and work instructions showing experimental changes
• Prototype build records and pilot run summaries
• Inspection reports, CMM data, capability studies (Cp/Cpk), GR&R
• Test plans, test reports, failure analyses, and corrective action records
• Machine parameters, run logs, SPC charts, scrap/rework records
• Engineering emails/notes showing hypotheses, constraints, and conclusions

This evidence becomes the backbone of an R&D Study and helps substantiate Qualified Research Activities and related Qualified Research Expenses (QREs) such as wages, supplies consumed in trials, and certain contractor costs.

How QRAs flow into a defensible R&D Tax Credit claim (QREs, Form 6765, and the R&D Study):

Once you’ve identified QRAs at the activity level, the rest of the R&D Tax Credit process becomes far more measurable:

• Tie each activity to a business component (product, process, technique, or manufacturing software)
• Build technical narratives that map cleanly to the Four-Part Test
• Map people to the work: engineering, quality, tooling, production support (when engaged in experimentation)
• Calculate Qualified Research Expenses: wages for direct and first-level supervision/support, supplies consumed in experimentation, and eligible contractor costs
• Compile an R&D Study that organizes projects, uncertainties, experiments, and results
• Support preparation of Form 6765 using traceable QRA and QRE logic

This approach improves IRS readiness and typically reduces the end-of-year scramble that causes under-claiming (or over-claiming). When done well, R&D Tax Credits can produce meaningful tax savings, an R&D Tax Credit refund (where applicable), and better business cash flow—often turning innovation into a more self-funding engine.

AI R&D CTOs in manufacturing: replacing manual year-end reconstruction with continuous capture:

Many manufacturers miss Research and Development Tax Credits not because they lack QRAs, but because tracking is too time-consuming. This is where the AI R&D CTO category is emerging—combining R&D Tax Credit Services with AI-driven capture and organization, without relying on manual, retrospective interviews alone.

An AI R&D CTO (or Virtual CTO / AI Chief Technology Officer) can function as an AI Technology Advisor and AI Technical Advisor focused on:

• Continuously tracking Qualified Research Activities through structured prompts and check-ins
• Managing time-survey workflows so employees don’t have to reconstruct months of effort
• Capturing Qualified Research Expenses signals by aligning labor, contractor work, and supplies used in trials
• Organizing the timeline of experimentation and mapping supporting documents to each activity
• Producing a draft R&D Study package that’s faster to validate and refine with your tax team

In manufacturing, this “capture mechanism” is especially valuable because the best evidence is created on the floor and in engineering systems during trials—not months later. Done correctly, it can reduce claim friction for smaller teams while improving precision and audit readiness.

Separately, beyond tax, an AI R&D CTO can also support stronger Innovation Management and AI Product Strategy by improving visibility into technical barriers, lessons learned, and competitive benchmarking—helping manufacturers make better product and process decisions with fewer resources.

Next step: estimate your manufacturing R&D Tax Credit and strengthen technical leadership:

If you’re a manufacturer doing prototyping, process improvement, automation, tooling iteration, material trials, or yield improvement, you may already have Qualified Research Activities that support R&D Tax Credits. The opportunity is turning those activities into a documented, compliant claim—while also upgrading how your organization captures and learns from experimentation.

Learn more about how an AI R&D CTO can enhance knowledge to world-class standards while seamlessly gaining R&D tax credits, and get an estimate of how much your R&D Tax Credit could be by selecting a button below.