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  • Writer's pictureAnantaya Pornwichianwong

The Role of Computer Vision in Shaping the Future of Manufacturing

Various industries have entered the era of AI and automation, and manufacturing is no exception. Labor-saving technologies have been implemented across all industries, encompassing data analytics, AI, automation, and robotics. These advancements have resulted in heightened productivity and reduced costs.

Computer vision is a highly progressive field of AI that has seen tremendous advancements. It possesses the ability to identify and analyze visual data, effectively functioning as human eyes. Consequently, the manufacturing industry utilizes computer vision to enhance operational efficiency and increase production capacity.

According to IBM, in 2022, 71 percent of manufacturers considered computer vision as a crucial element in achieving their business goals. This statistic highlights the widespread adoption of computer vision in modern manufacturing, indicating its standardization in the industry.

In this article, we will explore the role of computer vision in shaping the future of manufacturing.

Enhancing manufacturing through computer vision

Computer vision has been widely deployed in various manufacturing sectors, primarily in two key areas: quality control and automation.

Computer vision excels at tasks such as quality control and anomaly detection, surpassing human capabilities by inspecting things at a granular level. Its precision leads to reduced human errors in these areas. Additionally, computer vision can automate tasks like quality control, machine safety assurance, and anomaly detection in production lines, achieving high and consistent performance without requiring human supervision. By maximizing efficiency, accelerating processes, and reducing costs in production, computer vision plays a vital role in enhancing overall productivity.

Examples of computer vision solutions in manufacturing

  • Quality control

One of the remarkable capabilities of computer vision is its exceptional precision at a granular level, enabling it to detect anomalies that may go unnoticed by human eyes. When combined with an anomaly or defect detection model, it becomes an incredibly high-performing, automated quality control tool for our production lines. The system scans for even the tiniest defects like scratches or stains, as well as other qualities that fail to meet the standard. This not only reduces costs and human resources but also enhances precision by minimizing human errors.

  • Automated Assembly

Computer vision solutions can be utilized to automate the process of part assembly, such as in the car or electronic device production. The system can detect and identify parts, arrange them in the correct order and orientation, and prepare them for the assembly line. Additionally, it can monitor each step of the assembly process to ensure flawless execution.

AI can model the optimal assembly steps and monitor the actual process using 3D visual inspection to determine if the steps meet the required standards. It can also promptly alert for any issues or disruptions that may arise during the assembly process.

  • Predictive maintenance

In manufacturing, machine damage that disrupts the production line can result in significant costs and missed sales opportunities due to stockouts. Computer vision offers a preventive solution by monitoring all machines, detecting anomalies, and identifying signals indicative of damage. It can be integrated with an IoT system that connects all machines, collecting data such as spare part replacements and maintenance history. This enables tracking and planning preventative maintenance at the optimal time, while also alerting the engineering team to inspect and repair the machines before any downtime occurs.

  • Security and safety assurance

Computer vision can learn safety policies and analyze past accidents in factories. It can then monitor and analyze both employee behaviors and machines to detect actions or situations that may lead to accidents. Examples include machine leakage detection and safety gear detection, ensuring that employees adhere to safety protocols. The system can promptly alert for any violations, reducing the likelihood of accidents.

Case Study: Tesla's implementation of computer vision for quality control

While Tesla is renowned for its innovative approaches, it has faced criticism regarding inconsistent product quality, which has had a negative impact on its reputation. To address this issue, Tesla decided to incorporate computer vision into its car production quality control process. As part of this initiative, Tesla recently advertised a job opening for a quality inspection engineer. This strategic decision has led to notable improvements in product quality and the restoration of the company's reliability and reputation.

The benefits of using computer vision in manufacturing

  1. Increase productivity: According to Deloitte, the integration of computer vision solutions and automation accelerates the production cycle, leading to a 12-percent increase in employee productivity and a 10-percent increase in production outcomes.

  2. Reduce costs: Computer vision aids in preventing and minimizing machine downtime. McKinsey reported that computer vision could reduce machine downtime by 50 percent, resulting in significant cost savings.

  3. Enhance quality: Computer vision, with its high precision, enables detailed product inspections, reducing human errors and the likelihood of substandard products reaching the market. This led to increased customer satisfaction, a positive reputation, and reduced quality control costs by 10-20%.

  4. Heighten security: Computer vision continuously monitors machines and detects potentially hazardous behaviors, promptly alerting authorities to reduce accidents. By accurately reducing false alerts, it enhances safety and ensures timely responses.

Challenges in implementing computer vision in manufacturing

The primary challenge in implementing computer vision in manufacturing is the need for expertise across various fields, including machine learning, software development, and computer science. Building an effective solution requires integrating the computer vision system into an IoT network to connect with machines and collect extensive data from diverse sources. Additionally, having sufficient data for efficient model training is crucial.

To develop practical and secure models that align with your business needs, it is essential to seek expert consultation and foster collaboration across multiple fields.

At Sertis, we offer a team of experts spanning various domains to collaborate on building efficient computer vision solutions tailored to your business requirements. Our goal is to help you increase profits, create a meaningful impact, and drive a better future.

Learn more about Sertis' computer vision solutions for manufacturing at

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