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How can you use machine learning to detect quality issues in manufacturing?

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Quality issues in manufacturing can have serious consequences for your business, such as customer dissatisfaction, product recalls, and increased costs. To prevent or minimize these problems, you need to monitor and control your production processes and ensure that they meet the required specifications and standards. However, traditional quality control methods, such as manual inspection, sampling, and testing, may not be enough to cope with the complexity, variability, and speed of modern manufacturing. That's why machine learning, a branch of artificial intelligence that enables computers to learn from data and make predictions, can be a powerful tool to enhance your quality management system. In this article, you will learn how you can use machine learning to detect quality issues in manufacturing, what are the benefits and challenges of this approach, and what are some examples of machine learning applications in different industries.

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Sindhu Panaiyappan

Enthusiastic Engineer | CSPO | Six Sigma Black Belt | Jinko Solar | AES | Tesla | Dow Chemicals
Manoj Kumar R M

Senior QA | API Testing | Mobile App Testing | Web App Testing | Playwright TS | Selenium Java | Postman
Joel Agyeman-Mensah

International Data-Driven Marketing Specialist || Pan-African Internet Governance Ambassador || Web3 ‘Evangelist’

1 What is machine learning?

Machine learning is a process of teaching computers to perform tasks that normally require human intelligence, such as recognizing patterns, classifying objects, or making decisions. Machine learning algorithms can learn from data, both structured and unstructured, without being explicitly programmed. They can also adapt to new situations and improve their performance over time. Machine learning can be divided into two main types: supervised and unsupervised. Supervised learning is when the algorithm is trained with labeled data, meaning that the desired output or outcome is known. For example, a supervised learning algorithm can learn to identify defective products by analyzing images that are labeled as good or bad. Unsupervised learning is when the algorithm is trained with unlabeled data, meaning that the desired output or outcome is unknown. For example, an unsupervised learning algorithm can learn to cluster products into different groups based on their features or characteristics.

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Sindhu Panaiyappan

Enthusiastic Engineer | CSPO | Six Sigma Black Belt | Jinko Solar | AES | Tesla | Dow Chemicals
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There is also a third type: Reinforcement learning (RL). This happens when the training agent learns through trial and error through series of feedback in form of rewards & penalties. For example, Deepmind's AlphaGo is an example where RL techniques were used to teach the system to play Go at a superhuman level.

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Joel Agyeman-Mensah

International Data-Driven Marketing Specialist || Pan-African Internet Governance Ambassador || Web3 ‘Evangelist’
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While the automatic analysis of data recognizes patterns and characteristics due to the wealth of information being inputed, it is important to do more research to find out why they occur. Machine learning can support innovative designs because it draws conclusions without bias.

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Onkeshwar Achalere

QC Inspector at Curtis Instruments, A Kohler Co.
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Implement a feedback loop so that when the model detects a quality issue, it can trigger actions such as halting the production line, alerting operators, or initiating automatic adjustments. Machine learning can significantly improve the detection of quality issues by identifying patterns and anomalies that might be challenging for manual inspection. It can lead to faster response times and better overall product quality in manufacturing processes.

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Ranganath Venkataraman

Automation and Innovation | Enterprise-wide value creation | Consulting Director
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Understanding your data is a key step in defining the type of ML problem that you are dealing with. In many cases this will require committed effort with input from domain expertise.

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Fabio Ramos Muratake

Operador de Produ??o / Operador de Maquinas/ Auxiliar de Produ??o/ Operador l Embalagem / Manufatura/ OPERADOR DE ISOLADOR ( PRODU??O DE VACINAS)
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O funcionamento das máquinas, precisam ocorrer de forma sincronizada de forma natural , além do acompanhamento visual é necessário se atentar aos sons e seguindo sempre o check list antes , durante e depois do processo, Seguindo essas observa??es é possível acompanhar alguma irregularidade

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2 How can machine learning detect quality issues?

Machine learning can detect quality issues in manufacturing by analyzing various types of data, such as images, videos, sounds, vibrations, temperatures, pressures, or signals, that are generated by the production processes and equipment. Machine learning can then identify anomalies, outliers, defects, or deviations from the expected or normal patterns, and alert the operators or managers. Machine learning can also provide insights into the root causes of the quality issues, such as faulty machines, suboptimal settings, human errors, or external factors. Machine learning can also help to optimize the production processes and parameters, such as speed, temperature, pressure, or feed rate, to achieve the best quality results. Machine learning can also help to predict the quality outcomes of the products or processes, such as failure rates, reliability, durability, or performance, based on the historical and current data.

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Manoj Kumar R M

Senior QA | API Testing | Mobile App Testing | Web App Testing | Playwright TS | Selenium Java | Postman
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Definitely ML can solve Quality Issues in manufacturing industries. For example, ML can detect offset changes in cutting tools and can provide feedback to the operator to adjust the offset parameters. OpenCV Image processing will be more helpful on these kind of occasions. Likewise in post production (once the job moved from the shop floor to quality control), we can train the model with the GD&T to find out the geometric deviations. Also ML will be very helpful during the visual inspection which enhances the quality engineers task to find out the defects like porosity.

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Pedro B.

Chemical Engineer | Black Belt
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Machine learning can be a powerful tool for detecting quality issues in manufacturing processes. The steps involved in using machine learning for quality control in manufacturing: 1-Data Collection; 2-Data Preprocessing; 3-Feature Engineering; 4-Model Selection; 5-Training the model; 6-Validation and Tuning; 7-Deployment; 8-Monitoring and Maintenance; 9-Feedback loop; 10-Interpret ability and Explain ability. It's essential to collaborate with domain experts throughout these steps to ensure that the model aligns with the specific requirements and constraints of the manufacturing process. Additionally, the success of a machine learning application in manufacturing often depends on the quality and representativeness of the data used.

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3 What are the benefits of machine learning for quality control?

Machine learning can offer numerous advantages for quality control in manufacturing, such as improving accuracy and consistency of inspection and measurement, increasing efficiency and productivity, enhancing coverage and frequency of quality control, enabling real-time and proactive control, and supporting continuous improvement and innovation. This is achieved by reducing human errors, biases, and subjectivity; automating inspection and measurement tasks; inspecting or measuring every product or process instead of relying on sampling or testing; providing instant feedback and alerts; providing data-driven insights and recommendations; and enabling experimentation and optimization of production processes and parameters.

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Francisco Perea Nú?ez

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Si tienes digitalizada una especificación o imagen de una fabricación estándar de un producto, puedes reducir el número de discordancias con todas las piezas del mismo tipo, descartando por IA aquéllas no válidas y siendo esto supervisado por calidad.

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4 What are the challenges of machine learning for quality control?

Machine learning can also present certain challenges for quality control in manufacturing. For instance, there must be enough data available to train and validate the algorithms, and expertise and skills such as data science, engineering, and domain knowledge are necessary to design, develop, and deploy the machine learning solutions. Additionally, the machine learning solutions must be compatible with existing production systems and equipment. Last but not least, trust and acceptance of the machine learning solutions must be established by operators, managers, customers, and regulators.

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Ranganath Venkataraman

Automation and Innovation | Enterprise-wide value creation | Consulting Director
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The crux of these challenges can rest in communication. An effectively articulated vision can win you allies like domain experts to advise on data and operators to adopt your operationalized models. Failure to communicate can erode trust, relationships, and (if manually intensive) the quality of data used for training the model.

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khaled seif

Printing Foreman at Coveris Group
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Now there are fault detection cameras which focus on defects during running the machine with Andon light to give the signals to warn operators for any defects and this with an advanced monitor camera beside and also we have on spot camera which detects colour variation during running .

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Miftachul Munif

Quality Assurance, Food Safety Practitioner, and F&B Business Consultant for Transforming and Simplifying the Business Process
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In more industrial business models, such as offset printing, toys, and product quality testing by visual, dimension, color, and shape, machine learning can solve the problem of bias quality if testing is done manually. The challenge for food business models is a deep kind of organoleptic test every batch to make sure the raw material, ingredient, and production processing conform to a high standard of sensory. Today, this testing is still an organoleptic test by a person with a specific high sensory score.

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Francisco Perea Nú?ez

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El tiempo a invertir en digitalizar todo lo que quieras controlar. Es realmente una inversión a corto, medio y largo plazo, pero es un tiempo a dedicar por parte de producción y de calidad. Ese tiempo invertido se lo restas normalmente a otras tareas que también son importantes, a no ser que se contrate a alguien expresamente para éso.

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5 What are some examples of machine learning applications for quality control?

Machine learning applications for quality control can be found in various industries and domains, such as automotive, electronics, and food. In the automotive industry, machine learning can be used to detect and classify defects in car parts, such as scratches, dents, or cracks, by using computer vision and image processing techniques. It can also help to predict and prevent failures in car components, such as engines, brakes, or batteries, by using sensor data and predictive analytics techniques. For electronics production, machine learning can help to inspect and measure the quality of products like printed circuit boards, chips, or displays by using optical character recognition and machine vision techniques. Additionally, it can optimize the soldering process of electronic components by using thermal imaging and deep learning techniques. Finally, machine learning can assess and monitor the quality of food products like fruits, vegetables, or meat with color, shape, texture, or spectral analysis techniques. It can also detect and prevent food contamination with hyperspectral imaging and anomaly detection techniques.

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Ranganath Venkataraman

Automation and Innovation | Enterprise-wide value creation | Consulting Director
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Machine learning is also used to maintain motor fuel properties in refineries within target ranges while balancing cost pressures by optimizing operating conditions

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Francisco Perea Nú?ez

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Invertir tiempo ahora es ganarlo para el futuro, ya que la IA se va a implantar es una herramienta muy eficaz para los que no hemos dedicado toda la vida al control de la calidad de cualquier tipo. Es una gran ayuda y viene para quedarse

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How can you use machine learning to detect quality issues in manufacturing?

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2

3

4

5

6

1 What is machine learning?

2 How can machine learning detect quality issues?

3 What are the benefits of machine learning for quality control?

4 What are the challenges of machine learning for quality control?

5 What are some examples of machine learning applications for quality control?

6 Here’s what else to consider

Manufacturing Engineering

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How can you use machine learning to detect quality issues in manufacturing?

1

2

3

4

5

6

1 What is machine learning?

2 How can machine learning detect quality issues?

3 What are the benefits of machine learning for quality control?

4 What are the challenges of machine learning for quality control?

5 What are some examples of machine learning applications for quality control?

6 Here’s what else to consider

Manufacturing Engineering

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