Overview: This article explores the integration of AI in electronics manufacturing. It discusses various machine-learning techniques and their applications, focusing on enhancing quality control and predictive maintenance.

The Industrial Revolution aimed to establish an interconnected system in which automated production methods could communicate with one another and humans through message exchanges. Large quantities of data have been made accessible as an outcome of the automation and monitoring of production processes. This data availability presents opportunities to enhance production processes for developing various applications through artificial intelligence (AI).

What is artificial intelligence?

AI is defined as the capacity of a computer system to identify patterns and implement actions based on available data and statistical models. AI has demonstrated exceptional performance in various settings, such as voice and pattern recognition algorithms, industry monitoring processes, fault detection, forecasting, and the healthcare sector. The Internet of Things (IoT) is one of the primary computational trends influencing AI.

Technologies of AI

AI-powered quality control revolutionizes manufacturing processes, significantly improving defect detection accuracy, efficiency, and cost-effectiveness. The key technologies of artificial intelligence include:

  • Machine Learning (ML)
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Generative Models
  • Expert Systems

This article discusses current machine learning techniques that concentrate on data used in predictive maintenance and product quality control of manufacturing electronics. Fig. 1 depicts the role of AI, machine learning, and deep learning techniques.

Fig. 1 Illustrating the role of artificial intelligence, machine learning, and deep learning. Source: MDPI

Machine Learning

Machine learning is a subset of AI that allows computers to acquire decision-making capabilities and enhance task performance without human intervention by developing algorithms and models.  ML models are trained on a set of examples to make predictions on newer or unseen data, which is referred to as training data. Machine learning is further classified, as shown in Fig. 2.

Fig. 2. Classification of machine learning Source: MDPI

Types of Machine Learning

Numerous machine-learning algorithms have been proposed, which include:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Supervised Learning 

It uses labeled data to train models where each example is linked to a target variable or outcome. The model can predict new, unobserved data by learning to map input features to the corresponding target values. Popular algorithms of supervised learning include:

  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machines (SVM)
  • Naive Bayes
  • K-Nearest Neighbors (KNN)
  • Gradient Boosting (e.g., XGBoost, LightGBM)
  • Neural Networks

Unsupervised Learning 

It finds patterns in unlabeled data and is often used for clustering and dimensionality reduction. These techniques are advantageous for investigating and comprehending the fundamental structure of data, revealing patterns, and producing insights without needing labeled examples.

  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • Dimensionality Reduction Techniques

Reinforcement Learning 

It is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives rewards or penalties based on its actions. The goal is to learn a policy that maximizes cumulative rewards over time.

AI-Powered Quality Control in Electronics Manufacturing

The electronic industry is increasingly based on machine learning to enhance manufacturing processes, improve yield, and increase efficiency. Various applications of AI-based machine learning in product quality control and predictive maintenance are discussed.

Process Optimization

Process optimization is necessary in industrial settings to improve efficiency and decrease expenses. AI can identify manufacturing process inefficiencies by analyzing real-time data and suggesting enhancements. ML models analyze enormous amounts of data from equipment sensors, production lines, waste reduction, and environmental controls to optimize process parameters in real time. The limitations of traditional methods are overcome by AI, which provides a more comprehensive and proactive data analysis.

Defect Prediction and Detection

The defect prediction process includes monitoring the manufacturing process to predict the quality of the produced parts, which is essential for preventing unplanned and expensive breakdowns. This process analyzes sensor data to detect warning signals suggesting potential industrial equipment failures. This enables the extension of the useful life of systems by enabling timely preventive interventions to prevent costly failures.

Fault detection is an essential component of the quality control process, as it facilitates the determination of whether a product should be authorized or rejected. It uses computer vision to conduct automatic defect inspections on manufactured components. Defects that can be recognized from AI images include surface defects, abrasion defects, background texture defects, and electronics connectors defects.

Traditional methods have reduced the ability to identify failures due to the absence of real-time data accurately.

Quality Assurance

Product Quality Control guarantees that manufacturing standards have been met and that products satisfy consumer expectations. Product quality can be monitored by AI, which can identify defects or anomalies during the manufacturing process and implement corrective measures in real time. This can enhance profitability, reduce waste, and enhance customer satisfaction. Subjectivity and delay in fault detection are two potential drawbacks of traditional quality control techniques. By providing immediate analyses of production data, AI avoids these constraints.

Predictive Maintenance

In the electronics industry, maintenance is essential for the precise operation of machines. Predictive maintenance is predicting the probability of a machine or plant requiring maintenance and the chance of a failure. AI models can detect wear and tear indications or potential malfunctions by analyzing sensor data in machines' operational conditions in real-time. 

Failure prediction and remaining useful life (RUL) prediction are the two categories they can be classified into. Failure prediction is concerned with identifying potential failures, whereas RUL prediction is concerned with the remaining time until a failure occurs.

Cybersecurity

Cybersecurity is essential in the electronic industry to safeguard industrial systems and data from cyber threats. Anomaly detection and behavior analysis are AI techniques that can assist in the real-time identification of potential security breaches and suspicious activities, thereby enabling the implementation of timely responses that reduce risks. 

AI-powered quality control transforms manufacturing by offering improved accuracy, efficiency, and adaptability in defect detection. As the technology matures, it will likely become an essential tool for manufacturers to maintain high product quality standards while optimizing their production processes.

Summarizing the Key Points

  • AI-driven quality control improves defect detection accuracy through real-time data analysis, ensuring products meet high standards with reduced wastages.
  • Machine learning techniques, including supervised and unsupervised learning, help analyze data, predict failures, and optimize production processes effectively.
  • AI techniques safeguard industrial systems from cyber threats, enabling timely responses to potential breaches and protecting sensitive data and operational integrity.

Reference

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https://doi.org/10.3390/ai1020008

Johanesa, Tojo Valisoa Andrianandrianina, Lucas Equeter, and Sidi Ahmed Mahmoudi. “Survey on AI Applications for Product Quality Control and Predictive Maintenance in Industry 4.0.” Electronics 13, no. 5 (March 4, 2024): 976.
https://doi.org/10.3390/electronics13050976

Dini, Pierpaolo, Lorenzo Diana, Abdussalam Elhanashi, and Sergio Saponara. “Overview of AI-Models and Tools in Embedded IIoT Applications.” Electronics 13, no. 12 (June 13, 2024): 2322. https://doi.org/10.3390/electronics13122322