Deep Learning Applications: Examples in Engineering & Beyond
Engineering challenges rise as industries seek new technologies. The Digital Thread can enhance product design efficiency, but it needs practical applications to hold value! Artificial Intelligence (AI), Machine Learning, and Deep Learning are a toolbox of hands-on technologies. AI, Machine, and Deep Learning provide application examples already known to the final general purpose users, such as pattern recognition for self-driving cars. However, the top Deep Learning applications can surpass "normal" applications. Deep Learning enters engineering departments to support critical concept design and decisions in manufacturing automation.
We will show how introducing digital threads, specifically big data generators like CAD design and CAE simulation, has opened the door to Machine Learning and Deep Learning. Deep Learning is an artificial intelligence discipline born from Machine Learning algorithms that can exploit the engineering data sets generated in the past.
Deep Learning techniques have become increasingly specialized. Deep Learning algorithms from Neural Concept have created a new field of engineering prediction for product design engineers. 3D deep Learning promotes digitalization in the automotive industry and all other sectors.
Deep Learning can solve complex problems in the industry, such as multi-physics optimization, where thermal, structural, and fluid dynamics considerations must be balanced simultaneously. It excels at identifying counterintuitive design solutions that human engineers might overlook due to established practices and/or cognitive biases.
We will provide a practical example of innovative automotive lightweighting components. A neural network suggested structural geometries that reduced mass while maintaining crash performance and shortening the design cycle from weeks to days. The predictions were validated against known data sets.
Let us start our journey into Deep Learning with its most general applications before delving into Engineering applications and the design of innovative components.
What are Machine Learning and Deep Learning?
Artificial intelligence is the field of computer science that creates systems that can perform tasks and functions typically required of the human brain. More specifically, Deep Learning, with specially structured artificial neural networks, mimics how the neural networks of the human brain process and learn from complex data, such as by recognizing images.
Machine learning enables systems to learn patterns from data, improving performance without being explicitly programmed. Algorithms detect underlying patterns and make predictions or decisions based on training data.
Deep Learning is a subset of machine learning. It uses neural networks to learn hierarchical representations across complex domains like vision, language, and signal processing.
Its power lies in artificial neural networks organized in a multi-layered processing information structure. In image recognition, early layers identify simple edges in a geometry, and deeper layers recognize complex objects like faces or vehicles. Called "deep" for its multiple hidden layers, a deep neural network can model complex non-linear relationships that simpler algorithms cannot.
Learning involves training on known outcomes and testing with unseen examples. High-quality training data is essential to prevent over- or under-fitting, since diverse input data directly affects model performance.
Each training iteration updates networks through backpropagation, where errors adjust connection weights. This allows neural networks to achieve tasks beyond traditional algorithms. After training, the testing phase "certifies" the reliability of Deep Learning algorithms' predictions by comparing them back-to-back (no more "learning" occurs in this phase).
Quality and Quantity of Data
Well-structured datasets are therefore crucial for Deep Learning, to avoid underfitting or overfitting.
Deep Learning benefits from large-scale data because its neural networks improve with more training examples. Cloud environments provide the necessary infrastructure, offering scalable storage and computational power to handle increasing data volumes.
Architecture of Neural Networks
Deep Learning applications provide data-driven solutions where traditional models fall short. They emulate biological neural networks used in vision and hearing.
Neural networks consist of layers of interconnected nodes that process data. They start with an input layer and are followed by hidden layers. Each layer extracts features, forming higher-level representations. Types of neural networks include feedforward, convolutional neural networks (CNNs) for image processing, and recurrent neural networks (RNNs) for sequential data.
How Deep Learning Models Operate: Learning Approaches
Deep Learning employs supervised, unsupervised, and reinforcement learning methods.
Reinforcement Learning focuses on training models through trial and error. An agent learns to make decisions by receiving rewards or penalties based on actions.
Supervised Learning involves training models on labeled data, where the model learns to map inputs to known outputs.
In Unsupervised Learning, the model identifies patterns in unlabeled data, often used for clustering and dimensionality reduction.
Machine Learning Approach: Reinforcement
Reinforcement learning is a type of AI/machine learning that involves training a model to make a sequence of decisions in an environment to maximize a reward.
Reinforcement machine learning has been successful in applications including game-playing, robotic control, and recommendation systems.
Machine Learning and Deep Learning Approach: Supervised
Supervised Learning trains predictive models using input features and known outputs. Applications include image recognition, speech recognition, signal processing, and natural language processing. The algorithm aligns input features with output labels by predicting, comparing to correct outputs, and adjusting parameters for accuracy. The trained model predicts new data, with some types allowing ongoing adaptation, like response surfaces from neural networks predicting outcomes using reduced-order models.
Data Dependency
Deep learning models rely on large datasets for training. Performance and accuracy improve with the size and diversity of the training data, as large data sets may offer the variety and complexity necessary for the model to generalize to unseen data points. Deep learning models can learn more nuanced patterns and features with more data, making them more robust in real-world applications.
Deep Learning in Natural Language Processing and Computer Vision
We will show how Deep Learning models can process language, recognize objects, and detect transaction patterns. These models enable machines to understand text, interpret visual data, and identify anomalies in user behaviors in the financial domain and other domains (transportation and communication).
Natural Language Processing
Deep Learning has improved human-computer interaction through advances in Natural Language Processing technologies. Neural networks trained on text datasets grasp context, nuance, and meaning in human language. Chatbots use Natural Language Processing, leveraging "transformer models" to maintain context, track references, and answer ambiguous questions.
Language translation technologies leverage Deep Learning to enhance machine translation quality and accuracy. By capturing linguistic patterns and idiomatic expressions, they can achieve near-human performance. Sentiment analysis employs Deep Learning to classify sentiments in text, including sarcasm and mixed emotions.
LLMs
Large Language Models (LLMs) are at the forefront of natural language processing. These neural networks analyze and produce human-like text across various languages and fields. Models like GPT handle tasks such as coding, answering technical queries, translating languages, and engaging in "reasoning." Unlike traditional NLP systems, LLMs can comprehend context, create inventive content, and adapt to specific domains. This reshapes how machines interact with human language.
Computer Vision
Computer vision for object detection is now unthinkable without the applications of Deep Learning (DL). DL algorithms detect traffic lights, recognize signs, and avoid pedestrians in autonomous vehicles. Visual recognition can also enhance machine quality inspections in manufacturing processes.
Deep learning is integral to facial recognition technology, enabling the identification of individuals by their unique features.
Applied Deep Learning Models Across Industries
Deep Learning models help doctors detect diseases, assist financial systems in tracking fraud, and support retailers in predicting customer needs and managing inventory.
Healthcare Deep Learning Applications
Deep Learning has many fascinating applications, including medical research, drug discovery, and diagnosing life-threatening diseases.
Deep Learning accelerates drug discovery by modeling protein-ligand interactions, predicting binding affinities, and generating novel molecular structures. Convolutional and graph neural networks identify patterns in compound libraries. Recurrent neural networks design molecules with optimal pharmacokinetic properties. Transfer learning techniques leverage bioactivity data to identify potential drug candidates.
Deep learning models can analyze medical images and help doctors diagnose patients by identifying cancer cells.
Deep learning models for developmental impairment detection analyze acoustic features of children's speech using convolutional neural networks. MIT's Computer Science and Artificial Intelligence Laboratory has created a computer system to detect children's language impairments. This allows the early identification of atypical phonological patterns. Recurrent neural networks process temporal speech dynamics, while transformer architectures assess linguistic structure and pragmatics. These systems extract high-dimensional feature representations from minimal speech samples, comparing them against "normative" data sets. Early diagnosis of developmental impairments in children is critical since early intervention improves children's prognoses.
Finance
The finance industry utilizes Deep Learning for fraud detection and risk assessment. Companies like PayPal apply DL models to analyze real-time transaction patterns, detecting fraudulent activities based on unusual behaviors. Deep Learning also contributes to algorithmic trading, where models predict stock price movements using historical data.
Retail
In retail, Deep Learning improves customer experiences and operational efficiency.
Amazon, for example, uses algorithms to offer personalized product recommendations.
Streaming services like Netflix improve user experience by providing personalized content recommendations based on viewing history and preferences. Netflix uses DL recommendation systems with collaborative filtering neural networks to identify content similarity. These models combine user behavior embeddings with CNNs for video features and RNNs for temporal engagement. The system optimizes multiple objectives via a multi-head attention mechanism that balances immediate engagement and long-term retention.
Beyond these examples, Deep Learning is transforming other retail areas:
- Computer vision systems for cashierless stores (like Amazon Go)
- Demand forecasting models that reduce inventory costs
- Visual search capabilities allowing customers to find products by image
- Dynamic pricing optimization based on multiple variables
- Customer service chatbots and virtual assistants
While Deep Learning impacts these "fun" fields, its footprint on engineering is particularly significant, as discussed in the next section.
Deep Learning Applications in Engineering
We will move to more engineering-oriented applications of DL algorithms employing 3D Deep Learning.
Engineers now leverage sophisticated neural network architectures to tackle complex spatial problems that were previously intractable with traditional methods.
Furthermore, engineers use AI to analyze sensor data, predict equipment failures, design more efficient structures, and develop self-navigating technologies across aerospace, automotive, and other industries.
Robotics
Boston Dynamics robots have indeed made remarkable progress in their ability to interact with real-world environments. Their advanced robotics systems combine several sophisticated technologies, from point cloud processing as we discussed earlier, to Manipulation algorithms (specialized software enables precise grasping and handling of various objects with different shapes and properties).
When a robot like Atlas unloads a dishwasher, deep learning models are constantly processing sensor data, predicting object properties, planning movements, and adapting to unexpected situations. The robot's seemingly natural movements are the result of complex neural networks trained on massive amounts of data and fine-tuned through experience.
Engineering Applications - Aerospace
Deep learning (DL) applications in aerospace include predictive maintenance and image analysis. Companies like Airbus use DL to analyze data from aircraft sensors, identifying anomalies before conventional threshold-based systems. DL models process time-series sensor data to detect component degradation patterns. They simultaneously analyze vibration signatures, temperature fluctuations, and pressure variations across thousands of parameters.
Moreover, Deep Learning assists in satellite imagery analysis for land use classification and environmental monitoring applications.
Furthermore, using Neural Concept's models, Airbus reduced the average time it takes to predict the pressure field on the external body of airplanes from one hour to 30 ms, a speed-up of over 1000x.
Automotive
DL is crucial in the development of autonomous vehicles. Deep Learning is the driving force behind self-driving automobiles. Deep Learning technologies learn from millions of data sets in the development of autonomous vehicles. Self-driving cars use real-time data from cameras, sensors, and geo-mapping for navigation. Tesla's Autopilot uses DL for real-time object detection, lane keeping, and traffic sign recognition. Autonomous systems rely on vast amounts of data and training to learn how to act and respond.
Often those systems are based on 3D point cloud processing networks. Those are specialized neural network architectures designed to work with point cloud data - collections of points in 3D space that represent the surface of objects or environments.
Unlike standard image data (arranged in a regular grid), point clouds are unordered sets of (x,y,z) coordinates, often with additional features like color or intensity. This irregular structure makes them challenging to process with traditional convolutional neural networks.
Design Optimization with Deep Learning
Deep Learning is increasingly used in innovative product design, particularly in optimizing high-dimensional design spaces. With DL algorithms, designers explore potential design variations and identify optimal configurations.
In aerospace, for example, Deep Learning shapes wings for optimized drone aerodynamics. A neural network is trained to predict wing designs that maximize the Lift-to-Drag ratio, increasing drone autonomy.
Deep Learning also helps optimize vehicle and component structures in automotive design. Neural networks accelerate the design process by predicting the effects of design changes before physical prototypes are built, as we will see later in a use case.
Predictive Maintenance with Deep Learning
Predictive maintenance prevents equipment failures by analyzing sensor data to detect early warning signs. It enables interventions that reduce emergency repairs. Studies show that predictive maintenance can help reduce downtime and operational costs.
Deep Learning helps AI in predictive maintenance by identifying failure patterns and forecasting breakdowns. This provides timely interventions and a data-driven alternative to reactive maintenance strategies.
The Digital Thread: Integrating Deep Learning into Engineering
The Digital Thread connects data flows and integrates areas of the product lifecycle, creating a closed-loop system that tracks products from concept to disposal. Deep Learning moves engineering from reactive to proactive using predictive models to foresee issues. Deep Learning algorithms analyze data, reveal patterns, and give engineers insights for fast adjustments.
Case Study: GE Aerospace
GE Aerospace applies AI for predictive maintenance, inspections, and software development. It collects real-time data from 44,000 engines, using machine learning to detect anomalies like irregular oil filter readings and alert maintenance teams before failures occur.
AI-driven inspections analyze images from robotic systems, identifying defects in manufacturing and repairs. A new facility in Cincinnati, the Services Technology Acceleration Center (STAC), will use X-ray fluorescence spectroscopy (XRF) to detect structural variations in metal parts, preventing unnecessary replacements. AI models also support remote monitoring centers, where engineers track engine data 24/7. GE's AI-driven maintenance and inspection programs reduce downtime, optimize repairs, and extend engine life, lowering airline operational costs while maintaining reliability.
Benefits of Deep Learning in Engineering
Deep Learning introduces computational capabilities that expand engineering methods, with various benefits compared to traditional methods:
- More Accuracy: DL models improve prediction accuracy through data pattern recognition in engineering applications.
- Time Efficiency: DL reduces the time required for engineering tasks. Thus, activities like optimizing geometries or analyzing test results can be completed faster.
- Cost Savings: DL helps identify potential design flaws early, avoiding late-stage modifications.
- Innovation Enablement: DL enables engineers to explore larger design spaces, quickly evaluating alternatives to achieve balanced performance solutions.
Implementing Deep Learning in Engineering Workflows
Successful implementation of Deep Learning requires structured data, suitable tools, skilled personnel, and careful project selection.
Data Collection
Relevant engineering data must be gathered, cleaned, and formatted for deep learning models. High-quality, diverse datasets improve accuracy and generalization.
Tool Selection
Frameworks like TensorFlow and PyTorch support model development, while cloud computing and specialized hardware accelerate training and deployment.
Skill Development
Engineers must understand neural networks, optimization, and data handling. Training programs and interdisciplinary collaboration facilitate adoption.
Pilot Projects
Small-scale experiments validate feasibility and demonstrate value before scaling deep learning solutions.
Challenges and Considerations
• Data Privacy: Secure sensitive engineering data with encryption and strict access policies.
• Model Interpretability: Ensure transparency to validate engineering decisions and meet regulatory requirements.
• Integration Complexity: Address compatibility with existing CAD, FEA, and simulation tools.
• Continuous Learning: Update models to maintain performance and relevance as new data becomes available.
3D Deep Learning Applications
3D Deep Learning, like the Neural Concept platform, produces Deep Learning applications based on convolutional neural networks, a specialized form of artificial neural networks first inspired by computer vision. They can analyze unstructured data and digest complex data from industrial sources. But the good news is that deep learning technology can be ported to an AI web tool, enabling designers to visualize simulation results instantly, reduce design iterations, and improve safety.
Case History: Battery Deep Learning Applications
The mission here is to design battery housings that can withstand side pole crashes to maintain their integrity.
During carworthiness tests, prototypes equipped with sensors and cameras gather data. In virtual crash testing, engineers generate additional data by simulating test results. However, the wait times for virtual testing are long.
The Neural Concept software reduced response times by predicting visual 3D deformations and summary results, such as maximum displacement, in near real-time.
Unlike traditional AI models, deep learning learns geometry from unstructured (nonparametric) CAD data and physics from CAE data. With Neural Concept, engineers can predict deformations and identify critical collision events during the early phases of the project.
Other 3D Deep Learning Applications
This branch of Machine Learning has applications in engineering domains where design changes are the input data, including thermal analysis or aerodynamics for aerospace and automotive, performance mapping for compressors, and so on.
Conclusion - Future of Deep Learning
We introduced Deep Learning as a transformative tool for engineering that excels at pattern recognition and predictive analysis. Our exploration covered its diverse applications across language processing, computer vision, healthcare, finance, retail, aerospace, and automotive sectors.
We emphasized two key engineering applications: design optimization, where Deep Learning algorithms efficiently identify optimal solutions from numerous design possibilities; and predictive maintenance, where these systems detect subtle indicators of potential equipment failures before they occur. To illustrate real-world impact, we showcased how 3D Deep Learning techniques accelerate battery enclosure design by accurately predicting crash deformation patterns.
As we look forward, Deep Learning will become increasingly fundamental to engineering practices, driving greater automation, efficiency gains, and breakthrough innovations industry-wide. The trajectory suggests a future where Deep Learning capabilities will be deeply embedded in both routine engineering functions and strategic business operations.
FAQ
Where is Deep Learning used today?
Deep Learning is used in computer vision, natural language processing, autonomous vehicles, medical diagnostics, fraud detection, robotics, and predictive maintenance. Read the full article to learn more!
What are the hardware requirements for Deep Learning in engineering?
Deep Learning requires high-performance GPUs, TPUs, and fast SSDs for training, as well as high-speed interconnects and optimized software frameworks. The trained model can be lightweight and deployed on low-power CPUs. Inference can run efficiently with 1-2 GB of RAM and minimal storage.
How does Deep Learning aid structural health monitoring?
Deep Learning detects cracks, deformations, and material degradation from sensor data and images, enabling early fault detection and predictive maintenance in various civil engineering structures (bridges and buildings).
How is Deep Learning applied in robotics?
Deep Learning increases robot adaptability by enabling object recognition, motion planning, grasping, and autonomous navigation. Collaborative robots (cobots) use DL for human-robot interaction and adaptive Learning. Reinforcement learning optimizes robotic control through trial and error.
What distinguishes Deep Learning and Machine Learning?
Machine learning includes traditional algorithms while Deep Learning uses multi-layer neural networks for feature extraction and complex pattern recognition without hand-engineered features.
