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| System Name | RBMK-1000 |
|---|---|
| Processor | AMD Ryzen 7 5700G |
| Motherboard | ASUS ROG Strix B450-E Gaming |
| Cooling | DeepCool Gammax L240 V2 |
| Memory | 2x 8GB G.Skill Sniper X |
| Video Card(s) | Palit GeForce RTX 2080 SUPER GameRock |
| Storage | Western Digital Black NVMe 512GB |
| Display(s) | BenQ 1440p 60 Hz 27-inch |
| Case | Corsair Carbide 100R |
| Audio Device(s) | ASUS SupremeFX S1220A |
| Power Supply | Cooler Master MWE Gold 650W |
| Mouse | ASUS ROG Strix Impact |
| Keyboard | Gamdias Hermes E2 |
| Software | Windows 11 Pro |
New manufacturing processes powered by AI and big data reduce defects that can go unnoticed by human inspection, resulting in improved product quality and efficiency. With the advent of the AIoT era, ASUS has embraced new technologies and methods to develop advanced manufacturing capabilities. At the end of 2019, ASUS expanded the company's AIoT business unit to be able to develop more solutions for industries and, in the process, renamed it the AIoT Business Group (AIoT BG). After consideration and planning around the three major aspects of successful manufacturing - design power, technical ability and continuous profitability - ASUS transformed operations to achieve the flexibility, speed, productivity and quality required for supply-side Industry 4.0 upgrades.
Detecting defects by hand is a major pain point and cause of inefficiencies in manufacturing processes. By investing in smart manufacturing solutions that utilize AI for producing metal peripherals, fans, printed circuit boards and other computer components as well as for system assembly, ASUS was able to remove efficiency bottlenecks and reduce losses resulting from misjudgment of manufacturing defects by factory employees. Moving forward, ASUS will continue to use artificial intelligence and big data to statistically classify different types of quality defects, determine their causes and improve processes at the source of defects to further improve and push the boundaries of manufacturing quality.
"ASUS currently has hundreds of suppliers, and whenever we are able to improve quality-inspection processes, suppliers are receptive and willing to make changes," said Jackie Hsu, Senior Vice President, Co-Head of Open Platform BG & AIoT Business Group said. "This is a win-win situation for ASUS and the entire industry, which has always attached great importance to product quality."
AI visual inspection system
In the manufacturing industry, it is a common practice to replace manual visual inspection with automatic optical inspection (AOI). However, optical inspection is inefficient for mechanical metal parts manufacturers. Manual visual inspection often requires viewing product surfaces from multiple angles to see the defects due to the reflection of light. It is extremely important to grasp the optical and component surface characteristics to obtain complete and correct defect data.
Optical inspection is one of the core technologies of AIoT Business Group, which uses machine learning, deep learning and artificial neural network technologies to train the AI detection model correctly. "Automatic optical inspection accuracy in general is about 80-90%, which means that more than 10% of defects may be misjudged, and manual visual inspection accuracy is about 90%," said Albert Chang, Corporate Vice President, Co-Head of AIoT Business Group. "At present, ASUS has enabled AI to greatly improve its accuracy to 98% after learning."
AI waveform detection system
Fans are a key part of many computers and consumer electronics, cooling components and helping to extend product life. To ensure fan quality, manufacturers relied on inspectors who were able to detect problems with fans just by listening to them. Training highly skilled personnel for this important position took from three to six months, and inspectors would occasionally experience short- or long-term ear fatigue and other occupational factors that negatively impacted worker health and reduced the problem detection rate.
To solve this difficult problem, ASUS introduced the AI Wave Signature System, which analyzed the sounds of correctly operating fans and used them to develop a sound signature. This sound signature was then used to train AI models to quickly identify high quality fans. The AI Wave Signature System can be combined with testing of a product's electric current, voltage, vibration and other characteristics during inspection to ensure overall product quality. Additionally, the system can be applied to monitor production equipment in real time to avoid factory downtime. For example, by monitoring equipment motors in the factory using the AI Wave Signature System, operators will be notified immediately if a motor begins behaving abnormally. The motor can then be repaired before it fails completely, avoid production halts and accompanying losses.
Reproducibility as the ASUS AIoT business model
This year, the ASUS AIoT Business Group has set aggressive targets with fan and mechanical parts suppliers and is expected to acquire 30 smart-inspection projects. "The original intention and top priority of the business group is to promote common upgrades in the industry and assist the improvement of the supply chain to face international competition and continue to accumulate experience," said Jackie Hsu, Senior Vice President and Co-Head of the Open Platform and AIoT business groups.
Commenting on the introduction of smart manufacturing and AI detection solutions by major factories, Albert Chang, Corporate Vice President and Co-Head of the AIoT business group said, "In the past, AI algorithms and models were highly customized, improving the potential for AI detection solutions. Reproducibility is the goal of the next stage, enabling quick popularization and adoption along with scalability. Finally, the ultimate vision of the ASUS AIoT business group is to focus on 'full quality analysis.'"
The target of ASUS AIoT for the next three to five years will be data analysis. By investigating causes of defects, assisting the supply chain to find fundamental solutions for high yields, creating a formula for success, and accumulating long-term value, data analysis will become an important pillar of the ASUS brand.
View at TechPowerUp Main Site
Detecting defects by hand is a major pain point and cause of inefficiencies in manufacturing processes. By investing in smart manufacturing solutions that utilize AI for producing metal peripherals, fans, printed circuit boards and other computer components as well as for system assembly, ASUS was able to remove efficiency bottlenecks and reduce losses resulting from misjudgment of manufacturing defects by factory employees. Moving forward, ASUS will continue to use artificial intelligence and big data to statistically classify different types of quality defects, determine their causes and improve processes at the source of defects to further improve and push the boundaries of manufacturing quality.
"ASUS currently has hundreds of suppliers, and whenever we are able to improve quality-inspection processes, suppliers are receptive and willing to make changes," said Jackie Hsu, Senior Vice President, Co-Head of Open Platform BG & AIoT Business Group said. "This is a win-win situation for ASUS and the entire industry, which has always attached great importance to product quality."
AI visual inspection system
In the manufacturing industry, it is a common practice to replace manual visual inspection with automatic optical inspection (AOI). However, optical inspection is inefficient for mechanical metal parts manufacturers. Manual visual inspection often requires viewing product surfaces from multiple angles to see the defects due to the reflection of light. It is extremely important to grasp the optical and component surface characteristics to obtain complete and correct defect data.
Optical inspection is one of the core technologies of AIoT Business Group, which uses machine learning, deep learning and artificial neural network technologies to train the AI detection model correctly. "Automatic optical inspection accuracy in general is about 80-90%, which means that more than 10% of defects may be misjudged, and manual visual inspection accuracy is about 90%," said Albert Chang, Corporate Vice President, Co-Head of AIoT Business Group. "At present, ASUS has enabled AI to greatly improve its accuracy to 98% after learning."
AI waveform detection system
Fans are a key part of many computers and consumer electronics, cooling components and helping to extend product life. To ensure fan quality, manufacturers relied on inspectors who were able to detect problems with fans just by listening to them. Training highly skilled personnel for this important position took from three to six months, and inspectors would occasionally experience short- or long-term ear fatigue and other occupational factors that negatively impacted worker health and reduced the problem detection rate.
To solve this difficult problem, ASUS introduced the AI Wave Signature System, which analyzed the sounds of correctly operating fans and used them to develop a sound signature. This sound signature was then used to train AI models to quickly identify high quality fans. The AI Wave Signature System can be combined with testing of a product's electric current, voltage, vibration and other characteristics during inspection to ensure overall product quality. Additionally, the system can be applied to monitor production equipment in real time to avoid factory downtime. For example, by monitoring equipment motors in the factory using the AI Wave Signature System, operators will be notified immediately if a motor begins behaving abnormally. The motor can then be repaired before it fails completely, avoid production halts and accompanying losses.
Reproducibility as the ASUS AIoT business model
This year, the ASUS AIoT Business Group has set aggressive targets with fan and mechanical parts suppliers and is expected to acquire 30 smart-inspection projects. "The original intention and top priority of the business group is to promote common upgrades in the industry and assist the improvement of the supply chain to face international competition and continue to accumulate experience," said Jackie Hsu, Senior Vice President and Co-Head of the Open Platform and AIoT business groups.
Commenting on the introduction of smart manufacturing and AI detection solutions by major factories, Albert Chang, Corporate Vice President and Co-Head of the AIoT business group said, "In the past, AI algorithms and models were highly customized, improving the potential for AI detection solutions. Reproducibility is the goal of the next stage, enabling quick popularization and adoption along with scalability. Finally, the ultimate vision of the ASUS AIoT business group is to focus on 'full quality analysis.'"
The target of ASUS AIoT for the next three to five years will be data analysis. By investigating causes of defects, assisting the supply chain to find fundamental solutions for high yields, creating a formula for success, and accumulating long-term value, data analysis will become an important pillar of the ASUS brand.
View at TechPowerUp Main Site
