How AI Can Speed Up Quality Control in Manufacturing

The Challenge

In manufacturing, quality control is a mission-critical part of the production process. Even a minor deviation from an engineering drawing can lead to a defective batch, component failure in the field, or, in the worst-case scenario, a safety incident at the customer site.

That is why:

• Quality control takes place at every stage of production, from incoming inspection of raw materials and blanks to final inspection of finished parts.
• Critical geometric features such as lengths, diameters, tolerances, and angles must match the engineering drawing with precision down to hundredths of a millimeter.
• In some cases, 100% inspection is required, especially for high-value or safety-critical parts used in aerospace, energy, and automotive manufacturing.
• Even when manufacturers rely on sampling inspection, quality teams still have to complete dozens or hundreds of measurements every day.

In most cases, this work is carried out by a quality engineer or inspector. Their role is to review the engineering drawing, manually identify the key dimensions, prepare an inspection sheet, measure the relevant features using calipers, micrometers, or other tools, document the results, and decide whether the part meets requirements.

This process is time-consuming and labor-intensive. It requires a high level of technical expertise and close attention to detail, while also leaving room for human error. A critical dimension may be missed, a tolerance may be interpreted incorrectly, or a drawing note may be overlooked.

Another challenge is that manual quality control does not scale efficiently. As production volumes grow, inspection capacity does not grow at the same pace, especially when manufacturers are already dealing with a shortage of qualified personnel.

Which Stages Can Be Accelerated With AI?

If quality control is digitized end-to-end, manufacturers can significantly improve inspection speed and reduce labor costs.

So which stages can be automated, and how? Let us look at the quality control workflow step by step:

• Preparing an inspection sheet from an engineering drawing, which traditionally requires an engineer to manually identify the critical parameters, can be handled by AI that can read drawings and extract numerical dimensions automatically.
• Measuring the part itself can be supported by AI systems powered by advanced computer vision.
• Recording results and making a pass/fail decision can also be automated once the earlier stages are digitized, by defining acceptable tolerance ranges and comparing measured values against the specification.

When companies are looking for the fastest way to improve quality control, the biggest near-term opportunity is usually the first stage: replacing manual work involved in creating the inspection sheet. The later stages require a more advanced implementation, including computer vision model training and deployment on cameras or inspection systems. The final decision-making stage also depends on the first and second stages being in place.

Quality Control Automation

Automating the creation of an inspection sheet from an engineering drawing, including all of the parameters that need to be checked, can be done with AI capable of recognizing drawings and extracting numerical dimensions.

What could such a solution look like? A typical workflow would include the following steps:

• Upload engineering drawings in multiple formats, such as PDF, TIFF, DWG, and others.
• Extract geometric parameters using a trained LLM (Large Language Model) capable of identifying numerical dimensions, tolerance callouts, and specification notes shown on the drawing.
• Generate an inspection sheet with a measurement table that shows which parameter must be checked, where it appears on the drawing, and what tolerance is allowed.
• Export the output in the required format, such as PDF, Excel, or via API into an ERP or MES system for further integration.

With an automated inspection sheet in place, the engineer’s role is reduced to performing the actual measurements, while the routine preparation work is handled automatically.

By implementing AI at the inspection preparation stage, manufacturers can reduce the number of errors and missed values because AI can extract all relevant geometric parameters directly from the engineering drawing. As a result, product quality improves.

This is especially important in manufacturing, where even a small deviation can be costly. Large language models (LLMs) are particularly effective when working with engineering drawings because they can interpret complex notation, identify critical parameters, and understand the structure of the document.

At the same time, manufacturers can reduce labor requirements and free up valuable engineering time, which is especially important in a market where qualified specialists are in short supply. One engineer can oversee a larger number of parts without compromising inspection quality, using time that was previously spent on manual preparation and control.