Revolutionising Manufacturing with AI Visual Inspection

In the era of rapid technological advancements and Industry 4.0, the integration of AI into the manufacturing process has emerged as a game-changer, particularly with the development of AI visual inspection. This cutting-edge technology empowers manufacturers across a wide range of industries to enhance quality assurance, increase production efficiency, and reduce costly errors, positioning itself as an indispensable tool for the modern production line. In this article, we delve into the profound impact of AI visual inspection on manufacturing, exploring its immense value and the transformative benefits it brings. 

Visual inspection is the practice of observing and analysing an object to ensure it meets quality standards and regulations. The goal for manufacturers is to detect any visible defects or faults on products before they land in the hands of customers.  

Traditional visual inspection is a manual process carried out by a skilled human inspector delivering judgements on the quality and completeness of a part with the naked eye. The introduction of digital technologies and artificial intelligence has presented manufacturers with the opportunity to enhance the efficacy and effectivity of their inspection process and gain a competitive edge. 

Importance of Visual Inspection for Manufacturing

Visual inspection is a critical step in the manufacturing process due to the potentially prohibitive costs of  defects. Defective products could cause injury, fatality, scrapped items, waste, product recalls or reputational damage. As a result, manufacturers invest significant resources to implement tools and processes for detecting defects. 

Limitations of Traditional Visual Inspection

According to historical research, visual inspection errors typically range from 20% to 30% [1]. Although certain errors can be reduced through training and practice, they cannot be completely removed. Visual inspection comes with limitations making it one of the most common use-cases for Artificial Intelligence (AI) in the industrial world. It is therefore not surprising that global manufacturers are expected to spend $9.89 Billion on AI by 2027, according to Fortune Business Insights. 

Artificial Intelligence (AI) is a collection of many different technologies working together to enable machines to sense, comprehend, and act in ways that both mimic and go beyond human capabilities.  

AI visual inspection typically uses a camera inspection technique in conjunction with deep learning to analyse and interpret image or video data, learn patterns, and apply that learning to make accurate and efficient quality control decisions independent of humans or to assist humans. 

AI in Electronics Manufacturing 

Electronics play an indispensable role in modern everyday life. The way we live, work, and interact with the world is dependent on electronics. From communication and entertainment to healthcare and transportation, electronic devices and technologies are integrated into various aspects of our daily routines. 

The process of mounting surface components onto printed circuit boards (PCB)s can be subject to missing or incorrect component selection and orientation, resulting in batch scrappage. This is despite in-depth instructions and sufficient operator training. AI visual inspection can be employed as a process control system for in-line assembly verification of PCB components positioning and orientation, enabling early detection of assembly issues. Additional applications also include, detection of insufficient solder, icicle, shift, exceeding tin, etc. in the PCB soldering and gluing process. Other use cases include the detection of glue spill, mesh deformation, scratches, bubbles, etc. in product surface check inspections. 

AI in Medical Device Manufacturing 

Medical devices directly impact patient health and well-being. Ensuring the safety and reliability of these devices is of utmost importance. Inspection helps detect potential hazards or defects in the likes of stents, balloons, catheters and drug delivery devices, that could pose risks to patients and enables timely corrective actions to prevent adverse events.

Due to the criticality of medical devices like stents, balloons, catheters and drug delivery devices, manufactured batches must be 100% inspected for dimensional conformity and defects. Deep learning inspection models offer accurate rapid defect detection and classification to assist inspectors making the quality control decisions, whilst keeping records of decisions. 

AI in Automotive Manufacturing

Automotive manufacturing is a complex process that requires strict adherence to quality standards to ensure the safety, reliability, and performance of vehicles. Manufacturers are required to comply with these regulations to maintain their reputation and meet customer expectations.

Inspection models can be deployed for the detection of scratches, dents, cracks, and staining in surface inspections, issues with welding seams and anomalies in foundry engine block, ensuring product leaving the factory is of the highest quality. 

Improved Quality

Given the accuracy and reproducibility that deep learning brings to visual inspection, manufacturers will benefit from less false rejections, better detection rates, avoidance of product recalls and the protection of company reputation.

Reduced inspection time

AI visual inspection systems have considerable computational power for processing image/video data at rapid speeds. Inspection models can automatically detect an array of defects on multiple sample parts at one time. AI visual inspection processes can also be automated with robotics and conveyer systems for continual operation.

Reduced waste

The waste generated from manufacturing not only increases cost for companies, it also tarnishes their reputation. In a world where companies are being screened for their sustainability initiatives and commitment to reducing carbon emissions, many manufacturers are seeking to reduce waste generated along the supply chain. AI-powered inspection systems can analyse large volumes of data and detect defects or anomalies in real-time. This leads to better quality control and reduces the number of defective products that would otherwise be discarded, reducing waste and minimising the environmental impact of manufacturing.

Reduced labour costs

Maintaining a workforce of inspectors, staff-turn over, and continuous training contributes to a significant proportion of manufacturing overhead costs. AI visual inspection not only enables operational continuity when staff shortages or lack of trained inspectors occur, it also reduces the demand for skilled human inspectors.  Rather than engaging in routine inspection tasks, inspectors are deployed in higher value add tasks that benefit the manufacturer’s strategic direction.

The need for transformation is fast becoming a necessity for manufacturers’ survival. AI visual inspection is a rapidly growing technology that has the potential to transform the way quality inspection is done. 

Ultimately, implementing AI visual inspection provides considerable benefits for manufacturers by eliminating a core source of inefficiency, reducing operational costs and eliminating product defects. 

Moving forward with such a transformative step will deliver manufacturers robust quality control processes that are essential to maintaining customer trust and remaining competitive in the marketplace.

References:

1. Drury, C. G., & Fox, J. G. (1975). The imperfect inspector. In C. G. Drury & J. G. Fox (Eds.), Human reliability in quality control (pp. 11–16). London, UK: Taylor & Francis.
2. Megaw, E.D. (1979). Factors affecting visual inspection accuracy (pp 27-32) Applied Ergonomics.