See How AI Helps Us Polish Customized Environmental Boxes Faster

See How AI Helps Us Polish Customized Environmental Boxes Faster

Engineer + AI. Make environmental box design, fast and good!

1. Introduction

The structural design of an environmental test chamber is a typical multi-parameter engineering problem, especially in the context of customization and fast delivery becoming more and more demanded by customers, the traditional manual modeling method seems to be inefficient and has a high error rate.

Facing this challenge, we have explored a modeling method that combines Excel design tables and Python scripts to automatically update the structural model and quickly change the drawings through AI-assisted generation of control codes, which significantly improves the design response speed and modification efficiency.

The next section describes the specific realization path in detail:

2. Modeling ideas

SolidWorks, as a 3D modeling platform, supports the control of component dimensions through global variables.

We parameterize all the key structural parameters of the test chamber and define them in an Excel table, so as to realize the design logic of “parameter is model”.

The main parameters include:

✅ Shell size (length, width, height);

✅ Inner and outer shell gap (insulation);

✅ Duct spacing and fan layout;

✅ Observation window position, door size;

✅ Wiring holes, heating/humidification components installation position, etc.

All parameters are organized by unified naming rules, which is convenient for program call and logical judgment.

3. Program control logic

We used Python to make calls to the SolidWorks API to automatically read Excel tables, modify model variables, and perform model reconstruction and saving.

What's more, we made extensive use of the DeepSeek model to generate the core control code throughout the development process.

By simply describing the goal in natural language, e.g., “read the dimensions from Excel and update the model variables”, the AI can output the complete script framework, which saves a lot of time for debugging and consulting documents.

3.1 Excel Design Table Structure

Using Excel as a parameter-driven platform not only makes design input intuitive and transparent, but also naturally adapts to the collaborative needs of production departments and procurement teams. Design engineers can enter design parameters as if they were filling out an order form, and AI will automatically build the corresponding 3D model and drawings.

This “what you see is what you get” design experience dramatically shortens the transformation path from demand to model, and really makes it possible for “what the customer thinks, the engineer can see in a minute”. These parameters are used as inputs to drive model changes.

3.2 Example of AI-generated Python code

import win32com.client

import pandas as pd

# Read Excel parameters

df = pd.read_excel("design_parameters.xlsx")

params = dict(zip(df[“parameter name”], df[“value”]))

# Connecting to SolidWorks

swApp = win32com.client.Dispatch("SldWorks.Application")

model = swApp.ActiveDoc

eqMgr = model.GetEquationMgr()

# Automatic modification of model global variables

for i in range(eqMgr.GetCount()):

    var_name = eqMgr.Equation(i).split('=')[0].strip().strip('"')

    if var_name in params:

        eqMgr.Equation(i, f'"{var_name}" = {params[var_name]}mm')

model.EditRebuild3()

model.Save()

This code is generated by AI, and the user only needs to provide the intent and Excel format definition to complete the automatic modeling control process.

With the Python script generated by the AI model, the system can automatically parse the design variables entered in Excel and call the SolidWorks API for 3D model reconstruction.

The code automatically recognizes the feature names associated with the variables and intelligently repairs the model, avoiding the common problem of “changing the dimensions but blowing up the model”.

4. Key values of AI

In this project, AI (DeepSeek) is not only an assistive tool, but also the main engineer in the code development process. Its core roles include:

✅ Automatic script generation: describing requirements in natural language can quickly output runnable code;

✅ Logic Completion and Error Adjustment: AI automatically detects common problems such as inconsistent variable naming and missing units;

✅ Iterative efficiency improvement: repeated code changes only need to fine-tune the instructions to get a complete revised version;

✅ Lower entry barrier: structural engineers with non-software background can also quickly get started with program automation with the help of AI.

This allows structural engineers to focus on the design itself, without having to delve into complex API interface development.

5. Application effectiveness

We have successfully deployed this methodology in a variety of customized environmental test chamber projects, including:

✅ High and low temperature test chambers (change of dimensions, duct layout, infusion structure)

✅ Salt spray test chamber (automatic generation of saturated pipes);

✅ Composite light aging equipment (window change, sample holder change)

6. Conclusion and outlook

This combination of Excel parameter tables, Python scripts and AI assistance has been gradually applied to several customized projects of QWT, and the engineers' most direct feelings are:

1, change the map faster, fewer mistakes: customers mention a size change, a few minutes to see the new model, basically do not have to worry about hand error.

2, can focus more on the design itself: save the time of repeated operations, you can do more structure, optimize performance.

Next, we will also try:

1, so that the AI better understand the design intent: for example, the engineer said “increase the air volume”, AI can help find the relevant parameters to adjust.

2, design data directly to the production: eliminating manual input, to avoid errors.

3, the engineers' past experience precipitation: AI assistance, the good design ideas into a newcomer can also refer to the tips.

In the future, we will continue to explore the combination of human and intelligence, and continue to exceed customer expectations is our goal!

Author's Profile: Wang Chao

Photovoltaic industry for 10 years, plowing environmental test chamber structure design and optimization, good at structural innovation and process improvement to solve complex design problems.

Leading the completion of high and low temperature, hot and humid, light and other types of equipment mechanical design and process optimization, many times successfully improve the reliability and assembly efficiency.

In the future, he will rely on the team to promote the equipment to high precision, intelligent step forward, to provide better quality solutions for customers in the industry.