Case studies

A flight safety visual inspection system, critical to ongoing operations, had become outdated. While it still functioned, it lacked modern features such as intuitive camera layouts and the ability to record and display video simultaneously. Inspectors were forced to work around these limitations, slowing assessments and increasing cognitive burden. The original vendor no longer supported the platform, leaving no clear upgrade path.

At an organizational level, leadership had set a goal to deploy AI in targeted areas to automate and improve processes. This inspection was selected as a pilot candidate because it was well-suited to digital transformation: the process already relied on camera-based input, inspections often consumed significant time without uncovering defects, and there was untapped potential to capture high-quality data for future analysis.

Before development began, R Sigma conducted a full review of the hardware. Issues previously thought to originate in the camera systems were determined to be software-related, confirming that the optical hardware remained sound. Instead of replacing the entire platform, a lightweight, custom application was then developed and installed directly onto the inspection hardware. This update remapped camera views to match their physical arrangement, enabled simultaneous recording and display, and required no changes to infrastructure, networking, or operator workflows.

A high-precision assembly process required technicians to manually align and install sensitive components within a large workspace. Positional tolerances were sub-millimeter, and the work often demanded extended reaching, twisting, or working at height. While experienced teams performed the task reliably, the process carried ergonomic risks, relied heavily on verbal coordination, and lacked objective traceability.

The organization sought to improve safety and consistency while reducing component damage during installation by introducing robotic automation to the process.

Through process mapping and task analysis, R Sigma determined that many of the challenges attributed to human error actually stemmed from communication gaps and limited visibility of critical interfaces. Rather than moving directly to robotics, R Sigma recommended an instrumentation-first approach; deploying automation-grade sensors and communication tools to support current operators while generating the data needed for future robotic integration.

A sensor suite, including LiDAR, load cells, and RGB/depth cameras, was integrated into the workflow. Data from these sensors was displayed on a technician-facing dashboard, providing real-time visual overlays of alignment, distance, and contact points.

In a production environment, 3D models of large cast and machined parts were already in active use across engineering, manufacturing, and inspection. These models reflected years of refinement and embedded operational knowledge, but they lacked machine-readable metadata. As a result, inspection relied on manual checklists, and quality teams faced friction in linking process data back to models.

The organization aimed to accelerate adoption of Model-Based Definition (MBD) and create a demonstrable digital thread across functions.

Instead of re-authoring models from scratch, a targeted augmentation strategy was applied. Process-relevant Part Manufacturing Information (PMI) was extracted from existing inspection checklists and procedures, then added to the CAD models, indexed, and expressed using the Quality Information Framework (QIF). These semantic overlays were applied directly to the trusted geometry, making critical features machine-readable without disrupting established workflows. In parallel, a local machine-to-datalake connection was established, allowing inspection results to be archived, indexed, and linked to geometry in real time.