Artificial intelligence (AI) has made significant strides in various fields, challenging conventional notions of computer capabilities. However, while data science research primarily concentrates on refining AI models, there are numerous challenges associated with integrating AI into industrial applications.

Knowledge-Based Engineering, with its potential to streamline the production cycle by reusing engineering knowledge and intent, emerges as a promising avenue for AI in the industry. When engineering knowledge is effectively processed and categorized, neural networks naturally emerge as potent tools for automation.

This thesis presents three case studies that demonstrate the practicality of supervised learning, particularly in the domain of neural networks, to address manufacturing automation challenges. These case studies span various stages of the manufacturing system, encompassing engineering design, production planning, and quality control phases. The first application employs supervised learning to automate the generation of engineering drawings, while the third employs optical character recognition to expedite the quality control process for complex engineering drawings. The second application centers on the estimation of fixturing clamps for welding operations in automobile parts.

In summary, this thesis strives to make a meaningful contribution to the field of design engineering and manufacturing by examining the potential of AI in enhancing processes and addressing automation hurdles. By presenting case studies that showcase the utility of machine learning models in production settings, this thesis aims to stimulate further research in this evolving field.

Artificial intelligence (AI) has advanced rapidly across engineering and industrial domains, yet its adoption in production environments is often constrained by the need for higher system reliability, limited availability of high-quality data, and the challenge of embedding tacit engineering knowledge into learning-based models. These limitations hinder the broader industrial push toward flexible, data-driven automation capable of handling increasing product variability and shorter production cycles.

This thesis investigates how learning-based methods can be designed and integrated as re-liable perception components within production workflows. Through four complementary case studies, the work demonstrates how visual representations—ranging from Computer-Aided Design (CAD)-derived images and engineering drawings to point clouds and RGB-D images—can be leveraged to address concrete industrial challenges across multiple stages of the manufacturing pipeline.

The first case study predicts fixturing clamp configurations for welding operations in automotive manufacturing by learning geometric patterns from CAD-derived representations. The second applies optical character recognition to engineering drawings to accelerate quality-control and documentation workflows. The third examines scene reconstruction and 3D object detection from point clouds, using synthetic data generation to mitigate data scarcity. The fourth develops a fast, zero-shot pose estimation approach for robotic manipulation, enabling reliable object localization in dynamic industrial environments.

Taken together, these studies show how AI methods informed by structured engineering knowledge can increase process efficiency, reduce manual workload, and help resolve persistent automation bottlenecks in modern manufacturing.