# Predicting Car-Engine Manufacturing Quality with Multi-Sensor Data of Manufacturing Assembly Process

## TL;DR

This paper introduces a framework using sensor data to predict car-engine manufacturing quality, reducing rework and improving defect detection.

## Contribution

A novel edge-deployable framework combining SAE, CSWE, and ARSR for high-dimensional sensor data analysis in manufacturing.

## Key findings

- Sparse Autoencoder reduces noise in 12,000+ manufacturing parameters.
- Class-Specific Weighted Ensemble improves defect recall by 7.72%.
- Adaptive Regime-Switching Regression lowers prediction error by 12%.

## Abstract

Car engine quality control is fundamentally hindered by extremely high-dimensional, noisy, and imbalanced multi-sensor data. To overcome these challenges, this paper proposes an edge-deployable diagnostic and predictive framework. First, a Sparse Autoencoder (SAE) maps over 12,000 distributed manufacturing parameters into a robust latent space to filter instrumentation noise. Second, for defect classification, a Class-Specific Weighted Ensemble (CSWE) tackles extreme class imbalance by aggressively penalizing majority-class bias, improving defect interception recall by 7.72%. Third, for transient performance tracking, an Adaptive Regime-Switching Regression (ARSR) replaces manual phase selection with unsupervised regime routing to dynamically weight local experts, reducing relative prediction error by 12%. Rigorously validated across three diverse public datasets (NASA C-MAPSS, AI4I, SECOM) and a physical H4 engine assembly line, the framework achieves an ultra-low inference latency of 80±3 ms, practically reducing the engine rework rate by 7.2%.