Enhance Quality Measurement to Top Notch by Leveraging Vision-Based AI Solution

Introduction:

Quality Measurement and analysis are paramount in any industry, and companies do invest heavily in manpower and their training to ensure products go through a very rigorous quality check process. Product consistency, meeting regulatory and industry standards, and maintaining customer satisfaction are some very basic expectations of quality measures as they define the upward and downward movement of any product. Whether the product is for B2B or B2C, quality comes first to ensure that it gives the best experience to the customer. Some companies even go beyond the quality of the product and have quality checks on the packaging too. For this kind of company, having a dedicated team to check the product and packaging quality is a substantial investment. The company needs to invest in providing regular training and make sure that employees understand the company's quality standards. Companies conduct regular trainings to keep the new joiners educated on the quality measurement process and make sure that whenever the quality inspection team does checks, they go to the detailed level of inspection and log the results in quality measure books. Even after investing a copious amount in manpower, training, and solutions, companies do not reach an adequate level of quality as traditional quality checks are manual, relying on human inspection and subjective judgement. Company planning team thinks a lot to automate the factory and the plant becomes completely automated, but quality analysis and measurement not.

Today, if you need to check the quality parameters of any metallic product, you have a team that is doing it manually and investing a lot of time. This quality check happens batch- and sample-wise, and it is burdensome to check each individual product. Let me back up my statement with an example: A company that manufactures the iron ring gear, flex plate, water pump bearing, and so on, has more than 300 parameters for each product, and they have a team of 12 people checking each parameter one by one. For the quality team and company, it is very difficult to keep up the top-notch quality.

Talking about another example where a cigarette manufacturing company has around 150 parameters for a cigarette and its package. They do these checks randomly twice per running batch and then after packaging too. It includes a complete manual process and is subject to evaluation as it is being done by humans. The QA team takes 50 cigarettes and puts them on a transparent tray to check the 150 parameters manually. Do you think that by doing it manually then defect detection would be accurate and there would not be any kind of confusion and mistake? The company has found that if the same 50 cigarettes would be inspected by 3 different persons then they submit completely different defect reports from each other as some defect are subjective to one’s understanding.

However, with the advancements in Artificial Intelligence (AI), Computer Vision (CV), Machine Learning (ML), and Deep Learning (DL), there is tremendous potential to enhance and automate quality checks. The solution would have predefined parameters, and the algorithm would inspect the product and packaging based on those parameters and the pass or fail result would be captured. Even the solution could be taken to the next level, where it could be trained for some defects that are not known and are not in the set right now. The system would learn the pattern and do the analysis.

In this article, we will explore the use cases where AI technologies can revolutionise quality checks in the manufacturing industry, specifically in manufacturing, as #Technohertz has implemented solutions and POCs to analyse how AI-based solutions can overcome the quality issues companies are facing today.

Now imagine a scenario where the camera-based AI system is performing the same check and it is completely automated. The pass and fail results are being sent to the management dashboard. A complete analysis could be drawn, which would help the company expedite the process with automation. By doing so, the company not only saves time but also a lot of money. Below are the importance and advantages of implementing an AI-powered vision-based solution to automate the quality process.

Automated Defect Detection:

AI, CV, ML, and DL technologies enable automated defect detection in manufacturing processes. By training models on vast datasets of defect-free and defective products, organisations can develop algorithms that accurately identify and classify defects in real-time. These technologies can detect subtle defects, minimise false positives, and significantly improve the efficiency and accuracy of quality checks. Ex: Manufacturing product defects (ring, cigarette, belt, nut, bolt, shaft, gears, and pulleys).

Real-time Quality Control:

AI-powered computer vision systems can be integrated into the manufacturing line to perform real-time quality control checks. Using cameras and sensors, these systems capture images or video streams of products and analyse them using ML and DL algorithms. By comparing the visual data with predefined quality standards, the systems can identify defects, anomalies, or deviations from specifications, enabling immediate corrective actions and reducing the risk of faulty products reaching the market.

Predictive Maintenance:

AI, ML, and DL algorithms can analyse sensor data from machinery and equipment to predict maintenance requirements. By continuously monitoring variables such as temperature, vibration, and performance metrics, these algorithms can detect patterns indicative of potential failures or maintenance needs. Early detection of issues allows manufacturers to proactively schedule maintenance, prevent unexpected downtime, and optimise overall equipment effectiveness (OEE).

Quality Anomaly Detection:

Anomaly detection techniques powered by ML and DL can identify subtle deviations from expected quality standards. By learning patterns from historical data, these algorithms can flag anomalies such as variations in product dimensions, colour discrepancies, or abnormal process parameters. Early detection of quality anomalies enables timely intervention and corrective measures, reducing waste, rework, and production costs.

Process Optimisation:

AI technologies can optimise manufacturing processes by analysing large volumes of data and identifying areas for improvement. By applying ML algorithms to production data, organisations can uncover patterns, correlations, and inefficiencies that may go unnoticed through manual inspection. These insights can help optimise parameters, reduce defects, improve yield, and enhance overall process efficiency.

Continuous Improvement and Root Cause Analysis:

AI, ML, and DL models can aid in continuous improvement efforts by analysing data from various sources, including customer feedback, production records, and quality inspection data. These models can identify patterns, trends, and the root causes of quality issues, enabling manufacturers to make data-driven decisions and implement targeted process enhancements. By addressing root causes, organisations can achieve long-term quality improvements and drive customer satisfaction.

Intelligent Quality Decision Support:

AI systems can provide decision support to quality control personnel by analyzing data and providing recommendations. By leveraging ML and DL algorithms, these systems can process large amounts of data quickly, identify trends, and offer insights that help in making informed decisions. This can significantly reduce human error, increase consistency, and support quality control professionals in maintaining high standards.

Following are the technologies and frameworks we used to define a valuable solution for clients.

Technology could be used to implement AI-Based Solution -

Python language

Computer vision, deep learning, machine learning, NLP, and SQL

Tensorflow, Keras, Pytorch, Ultralytics, Pandas, and Numpy

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