Aluminum casting & mold design services

In the manufacturing industry, particularly in the supply chains for automotive parts and industrial machinery, the procurement of aluminum die-cast parts is a lifeline where the balance between quality and cost is critical. However, the business environment is becoming increasingly severe due to recent labor shortages, soaring raw material costs, and pressure for decarbonization. Amidst this, “Smart Factory transformation” utilizing IoT (Internet of Things) and AI (Artificial Intelligence) is rapidly progressing at aluminum processing sites, breaking away from traditional on-site management that relied on “craftsmen’s intuition and experience”.

This article explains how Smart Factory transformation in aluminum die casting specifically contributes to quality improvement and cost reduction. We will unravel the mechanisms and quantitative effects of how digital technology achieves both the reproduction of “Japanese quality” and “stable, low-cost procurement,” not only in Japan but also at overseas production bases such as Vietnam. We hope this information provides a new perspective for executives considering the selection of suppliers or the restructuring of supply chains.

Aluminum die casting is a technology that fills molten aluminum alloy into molds at high speed and high pressure, but the process is extremely delicate. More than 50 fluctuating parameters—such as molten metal temperature, mold temperature, injection speed, injection pressure, and cooling time—are intricately intertwined, and even a slight deviation directly leads to defects such as porosity (air bubbles) and misruns.

Traditionally, these parameter adjustments relied on the empirical rules of skilled technicians. However, the number of employed persons in Japan’s manufacturing industry has decreased by about 13% from 12.02 million in 2002 to 10.45 million in 2022, making technology succession a serious issue. Even at overseas bases, training skilled workers typically requires a period of 5 to 10 years, which has become a bottleneck for quality stabilization.

Smart factory transformation means visualizing and controlling these uncertain elements as “data.” According to a survey by the Ministry of Economy, Trade and Industry, the percentage of companies collecting and utilizing data in the domestic manufacturing industry has reached about 68%. However, complete digitalization in harsh environments like casting sites (high temperature, dust) was considered technically difficult. Yet, with the evolution of sensor technology and communication infrastructure, data acquisition in 1/100 second units is now possible.

At the core of the smart factory is real-time monitoring using IoT sensors. Waveforms of mold internal temperature and injection pressure are recorded for every single shot.

For example, AI instantly compares the current shot data with the “reference waveform” when a good product was produced. If the injection pressure deviates even by 0.5% from the reference value, that product is automatically excluded from the line as a “potential NG.” Even more important is “predictive maintenance.” AI analyzes minute changes in equipment vibration data and current values to make predictions such as “the plunger tip will reach its wear limit in 500 more shots.” This makes it possible to reduce downtime by up to 30% caused by sudden equipment stoppages.

Visual inspection, which traditionally relied on human eyes, is also being revolutionized by AI. The latest image processing systems detect minute cracks and dents in 0.1mm units with over 99.9% accuracy. While human detection rates drop due to fatigue, AI maintains a constant quality standard 24 hours a day, 365 days a year. In one pioneering case, a company succeeded in reducing inspection personnel from 5 to 1 while bringing the outflow defect rate close to zero.

For procurement managers, cost is likely the primary concern. Smart factory transformation contributes not only to quality control but also to direct cost reduction.

In aluminum die casting, the occurrence of defective products means not only a waste of raw materials (aluminum ingots) but also a loss of energy costs for re-melting. Melting furnaces need to maintain high temperatures of 600°C to 700°C, accounting for about 60% of total manufacturing energy. If the defect rate can be improved from 3% to 0.5% through condition optimization by AI, it results in significant cost reductions in both material and energy fees, which is returned to the unit price.

If a defect occurs in the market, the true value of the smart factory is tested. If there is a traceability system that applies a QR code or marking to each product and links all data at the time of manufacture (melting temperature, injection conditions, inspection results, person in charge, etc.), the time required for cause investigation can be shortened from several weeks to a few minutes. Since the affected range can be accurately identified, losses such as recalls can be minimized, which is also a huge risk hedge for the procurement side.

A concern in overseas procurement, especially in Vietnam, is “quality variation.” However, with a smart factory base, physical distance is no longer an issue.

It is possible to connect the Japanese mother factory and the Vietnam factory via the cloud to monitor and guide parameters in real time. Skilled Japanese technicians fine-tuning casting conditions at a Vietnam factory 4,000km away via tablet terminals—such a system has already become a reality. This makes it possible to maintain a quality assurance (QA) level equivalent to that in Japan while enjoying Vietnam’s cost benefits (labor costs, electricity costs, etc.).

AI also functions as an educational tool. AI learns from vast amounts of past trouble response data and presents guidance to operators when an abnormality occurs, such as “In a similar past case, the problem was solved by raising the mold temperature by 5°C.” This enables even less experienced local staff to make judgments close to those of veterans, raising the skill level of the entire organization. This is a factor directly linked to long-term supply stability.

Smart factory transformation in aluminum processing is not just a trend but is becoming a prerequisite for highly balancing quality, cost, and delivery (QCD). Through the utilization of IoT and AI, more than 50 variable elements are controlled, realizing significant reductions in defect rates and energy costs, as well as stable operation through predictive maintenance.

Especially in overseas bases like Vietnam, digital technology is a powerful tool that breaks down the barriers of “distance” and “experience.” In selecting suppliers, checking not only for low quoted prices but also “whether a quality management system utilizing data is established” and “whether traceability can be secured in case of trouble” is the first step toward building a resilient supply chain.

At Daiwa Aluminum Vietnam, we fuse the latest technology with years of cultivated casting expertise to promise the delivery of safe and competitive products to our customers.