Code-Led Checks Verify Diesel and Excavator Builds in 90 s
Diesel engines and excavators are large and structurally complex, and verification before warehousing after assembly is the key gate against defective products leaving. Construction machinery is built to order, and the same model can differ in configuration between orders, which makes manual comparison even harder. The previous method was visual checking against an assembly list, item by item: heavy workload, fatigue, escapes, and no practical way to verify every unit.
The warehouse gate is the last cheap place to intercept
Once a missing or wrong part enters the warehouse with the product, it directly affects delivery quality and customer trust. Compared with adding error proofing at every assembly station, the warehouse check faces a complete machine and can confirm two levels at once: whether the configuration is correct and whether the components are present. It catches both configuration errors and missing parts.
The code calls the list and the template together
When a product enters the station, the code is read, the product is identified and its assembly list is retrieved, and AI comparison then expands against that product's configuration requirements, with comparison items switching with the list. This solves the standard carry-over problem on a mixed-model line: one station serves different products, and if the comparison standard does not switch with the product, verification is not valid.
Assembly features spread across faces, so views do
Diesel engines and excavators are complex, with assembly features on several faces, and one viewpoint cannot cover them all. The station arranges imaging views by face so every verification item is captured and compared. The station mechanism fixes the product's relative position, making imaging conditions consistent and comparison results stable and comparable, which is the precondition for repeatable judgment.
AI extracts, template compares
The chain runs code trigger, image capture, AI recognition, template comparison and anomaly alarm. AI extracts assembly configuration and component features and compares them with the standard template to decide presence. The four actions run in series at one station, so a single entry completes the whole verification. Results are recorded item by item, and anomalies trigger an alarm immediately. Compared with fixed inspection items, the comparison items here switch with the product, which better suits finished-machine warehousing with many product families and configuration combinations.
90 seconds against the inbound rhythm
The cycle is 90 seconds per unit, matching the warehousing rhythm without reducing throughput, subject to site acceptance results. The verification flow connects to the warehousing action, so the product needs no extra transfer: passing units are stored, anomalies are intercepted and returned for handling.
100 percent verification depends on the template library
The project verifies 100 percent of assembly configurations at the warehouse gate, preventing defective products from leaving, with code reading, AI recognition and template comparison running automatically instead of manual unit-by-unit checking. That capability depends on the template library. The library expands as new models arrive, and the station and algorithms adjust incrementally to suit product family iteration. Selection should weigh part structural complexity and how visible the assembly features are, customize the station and library by family, and reserve room for multi-model templates. Where product families and configuration combinations are numerous, also assess the template maintenance mechanism and the response time for switching comparison items.
What to confirm before implementation
Why 100 percent instead of sampling?
Complex structures and varied configurations mean sampling cannot cover all assembly items. Unit-by-unit verification is the only way to close the escape gap.
How is standard carry-over prevented on a mixed line?
The code retrieves the matching assembly list and template, and the algorithm switches comparison items with the list rather than fixing the standard to the station.
Does a new model need hardware changes?
Usually not. The template library expands with new models and the station and algorithms adjust incrementally; families with large structural differences need station layout re-evaluated.
What happens to an anomalous unit?
The system alarms, the unit is intercepted before warehousing and returned for handling, and it can be stored only after passing.