Automobile parts

In traditional auto parts development, a design solution typically goes through a repetitive cycle of "design → mold making → trial molding → mold modification" before it reaches mass production. Each trial-and-error iteration entails costly mold modifications and weeks of time. The emergence of AI digital simulation is now completely breaking this inefficient pattern, equipping manufacturing with a "digital brain."

I. A Speed Revolution: From "Three Days" to "Three Minutes"

Traditional high-fidelity simulations often take hours or even days to complete a single calculation. AI digital simulation, powered by machine learning models, can deliver performance predictions in seconds to 5 minutes—a speed increase of over 10 times. For example, Sumitomo Riko, a globally leading auto parts manufacturer, used the Ansys SimAI platform to shorten the simulation cycle of rubber bushings from over 1 hour to just 5 minutes. For injection molding, the Cadmould AI Solver can even compress result feedback time to one-thousandth of traditional methods.

II. "Trial and Error" in the Virtual World, "Get It Right the First Time" in the Physical World

AI's accuracy has been greatly enhanced. During the design phase, AI-assisted mold flow analysis and structural simulation can predict defects in the injection molding process—such as short shots, weld lines, and warpage—in advance, and automatically optimize critical designs like gate positions and cooling channels. This means that before the mold steel is even cut, all issues have already been resolved in the digital world, significantly reducing the number of trial runs and mold modification costs.

Furthermore, digital twin technology allows the entire production line to be simulated in a virtual environment—whether equipment layout is rational, AGV logistics paths are unobstructed, and energy distribution is optimal—all "previewed" before decisions are made. Chen Hsong Group adopted this very approach for an automotive bumper production line project for a parts manufacturer, identifying bottlenecks and risks in advance and avoiding costly physical-world rework.

III. Breaking Down Experience Barriers and Making Knowledge "Replicable"

Traditional simulation and design rely heavily on the personal experience of senior engineers—a bottleneck and a risk. AI simulation tools, however, significantly lower the barrier to entry, allowing even non-experts to get started with ease. More importantly, systems like automotive mold intelligent design systems embed a company's design standards and process knowledge into AI, automating and intelligently streamlining the design workflow. What once required over ten days of manual design can now be drastically compressed, while reliance on individual experience is reduced.

IV. Empowering the Entire Chain: From "Manufacturing" to "Intelligent Manufacturing"

The advantages of AI digital simulation extend across the entire product lifecycle. In lightweighting, AI-assisted topology optimization can explore innovative structures with high stiffness-to-mass ratios—something traditional manual iteration can hardly achieve. In equipment maintenance, AI-based predictive maintenance models can reduce equipment fault response time by 85%, comprehensively improving production, quality, cost, and delivery metrics.

In summary, AI digital simulation is propelling auto parts manufacturing from "repeated trial and error" toward "getting it right the first time." It is not just an efficiency tool, but a strategic capability that reshapes product quality, accelerates innovation, and reduces costs. In the era of new energy and intelligent vehicles, whoever masters this capability first gains the edge in defining the future of the industry.