Intel corporation (20240184274). TOOL ANOMALY IDENTIFICATION DEVICE AND METHOD FOR IDENTIFYING TOOL ANOMALIES simplified abstract

TOOL ANOMALY IDENTIFICATION DEVICE AND METHOD FOR IDENTIFYING TOOL ANOMALIES

Organization Name

intel corporation

Inventor(s)

Mohammad Mamunur Rahman of Gilbert AZ (US)

Omesh Tickoo of Portland OR (US)

Nilesh Ahuja of Cupertino CA (US)

Ergin U Genc of Portland OR (US)

Julianne Troiano of Scottsdale AZ (US)

Ibrahima Ndiour of Portland OR (US)

TOOL ANOMALY IDENTIFICATION DEVICE AND METHOD FOR IDENTIFYING TOOL ANOMALIES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240184274 titled 'TOOL ANOMALY IDENTIFICATION DEVICE AND METHOD FOR IDENTIFYING TOOL ANOMALIES

Simplified Explanation

The method described in the abstract involves using image data of tools in a printed circuit board (PCB) manufacturing process to identify anomalies. This is done by inputting the image data into a machine learning module, which extracts tool feature image data, classifies the image data into different phases of the manufacturing process, and determines any anomalies based on the classified data.

• The method involves obtaining image data of tools used in the PCB manufacturing process.
• The image data is then inputted into a machine learning module.
• The machine learning module extracts tool feature image data from the input.
• It classifies the image data into different phases of the manufacturing process.
• Based on the classified data and the tool feature image data, the module determines any anomalies in the tools.

Potential Applications

This technology can be applied in various industries where automated manufacturing processes are used, such as electronics manufacturing, automotive manufacturing, and aerospace manufacturing.

Problems Solved

This technology helps in early detection of anomalies in the manufacturing process, leading to improved product quality and reduced downtime.

Benefits

The benefits of this technology include increased efficiency, cost savings, and improved overall product quality.

Potential Commercial Applications

Potential commercial applications of this technology include quality control systems for manufacturing processes, predictive maintenance systems, and automated inspection systems.

Possible Prior Art

One possible prior art in this field is the use of computer vision and machine learning techniques for quality control in manufacturing processes. These techniques have been applied in various industries to improve efficiency and product quality.

What are some questions that are not answered by this article?

How accurate is the anomaly detection system in identifying tool anomalies?

The article does not provide information on the accuracy rate of the anomaly detection system in identifying tool anomalies. Further research or testing may be needed to determine the system's reliability.

Are there any limitations to the types of anomalies that can be detected using this method?

The article does not mention any limitations to the types of anomalies that can be detected using this method. It would be important to understand the scope of anomalies that can be effectively identified by the system.

Original Abstract Submitted

a method for identifying a tool anomaly of an printed circuit board (pcb) manufacturing process comprising a plurality of phases, the method comprising the steps of: obtaining image data of at least one tool of the pcb manufacturing process; inputting the image data to a machine learning module, the machine learning module configured to perform the following steps: extracting, from the image data, a tool feature image data of the at least one tool; classifying the image data into a phase of the plurality of phases; and determining, based on the classified image data and the tool feature image data, an anomaly state of the at least one tool.