Explore how integrating Artificial Intelligence (AI) and Machine Learning (ML) into manufacturing processes can help organizations meet quality control needs, such as defect detection and waste reduction.
As manufacturing processes become increasingly complex, organizations are more and more adopting AI-based systems to optimize their operations, and one aspect of manufacturing that AI can aid is quality control.
Quality Control (QC) ensures that products meet the required standards, and an AI-based system can help identify defects and reduce waste. However, using AI for quality control also presents challenges. Introducing AI and ML into a company’s manufacturing processes requires significant investment, integration, and training.
Here’s what companies need to know before using AI for quality control in the manufacturing sector.
Benefits of Using AI for Quality Control in Manufacturing
AI can help improve key quality control processes in the following ways:
Reduces human errors.
While a human employee might miss an issue due to fatigue or haste, a properly trained AI QC will detect any error, allowing human employees to take appropriate corrective actions. Moreover, AI can potentially detect issues that humans might struggle to identify, such as tiny product defects.
Improves brand reputation.
Customers expect high-quality products, and product defects can lead to frustration and negative reviews. AI can identify product flaws before the product is shipped to the customer.
Helps comply with regulations.
Many parts and products must adhere to strict regulations, and AI assists companies in achieving this by flagging any compliance issues or other problems.
Reduces waste
Machine Learning can help prevent defective ingredients and parts from entering downstream manufacturing.
Eliminating these faulty inputs results in fewer rejected products due to inferior quality parts, thus reducing waste.
Keeps goods viable
AI can monitor environments to ensure that products are stored and transported safely. For example, AI can track the temperature of a food storage space to ensure conditions remain safe.
Four Use Cases of AI in Manufacturing Quality Control
The potential use cases of AI in quality control within manufacturing are as follows:
1. Ensuring Consistency
Supply chains are complex, and quality can vary significantly due to raw materials, suppliers, and other factors.
AI and Machine Learning (ML) can consolidate data and identify patterns that lead to lower quality inputs, among other uses.
2. Enabling Analysis
Some manufacturing issues are simply the result of one-off accidents, but others may indicate more widespread problems.
AI can highlight patterns of manufacturing errors, which employees can then use to address the underlying issue.
3. Predicting Supply and Demand
Manufacturers need to accurately calculate supply and demand to ensure their business will have enough materials to produce goods and meet customer needs.
AI can model delivery times and potential market demand, which can help avoid delays in the supply chain and ensure a sufficient number of products are always available.
4. Integration with Automation
Organizations can integrate their existing quality control systems with automation and robotics to enhance the speed of creation, digitization, and decision-making for accepting or rejecting products.
Finally, A quality control system can employ automation to reroute defective items for further inspection and direct viable items to robots that are equipped to package and ship the finished products.
