AI – PRODUCTION – CREATIVITY
In today’s fast-paced manufacturing world, where every minute counts, I faced a challenge every mechanical engineer knows well: manually processing a large number of technical drawings. Imagine a stack of over 5,000 DXF files, each full of unnecessary elements such as text labels, crosshairs in holes, and various layers that only slow down the laser cutting process. Manual cleaning? That would take forever, and mistakes would be inevitable. Instead, I turned to artificial intelligence – specifically, Grok from xAI – and in a short time received Python code that automates it all. The result? A staggering 1000x time saving, with production-ready drawings neatly stored in the “clear” folder. In this article, I share my experience, steps, and tips so that you too can leverage AI for similar tasks. Trust me – programming is no longer just for developers; it’s for anyone with a problem to solve.
In metal construction manufacturing, the DXF format (Drawing Exchange Format) is the standard for CAD drawings. But these drawings often arrive “dirty”: filled with layers containing text (e.g., dimensions, notes), crosshairs marking hole centers, or even leftover lines from previous revisions. For laser cutting, all of this must be cleaned – otherwise, the machine wastes time on unnecessary moves, or worse, cutting errors occur.
In my case, I had over 5,000 such files. Opening them manually in AutoCAD or similar software, deleting unnecessary elements, and saving them? That would take days, maybe weeks – monotonous beyond belief. And then there’s the human factor: fatigue, mistakes, inconsistency. I knew there was a better way – automation. But I’m not a programmer by profession. That’s where Grok came in.
Grok, the AI model from xAI, is perfect for such tasks because it understands context and can generate code based on your descriptions. I didn’t have to write code from scratch – I simply described the problem: “I need a Python script that reads DXF files, deletes text layers, removes crosshairs in holes, and saves cleaned versions to a new folder.” Of course, it didn’t go perfectly on the first try. The first attempts had issues – for example, the code didn’t recognize all element types or deleted too much. But that’s part of the process: iterate! I asked Grok for corrections, added details like “use the ezdxf library” or “process only specific layers,” and after a few interactions (less than an hour), I had working code.
The key to success: a good prompt. Be specific – describe the input files, the desired changes, and the output. If there are errors, test a small sample and come back with feedback. Grok learns from your corrections, so you quickly reach a solution. In my case, the code processed files in batches using Python’s ezdxf library to manipulate the DXFs.
import os
import ezdxf
input_folder = 'input'
output_folder = 'clear'
if not os.path.exists(output_folder):
os.makedirs(output_folder)
def clean_dxf(file_path, output_path):
doc = ezdxf.readfile(file_path)
msp = doc.modelspace()
# layers to remove (adjust to your layer names)
layers_to_delete = ['TEXT_LAYER', 'DIMENSIONS', 'NOTES']
for layer in layers_to_delete:
if layer in doc.layers:
entities = msp.query(f'*[layer=="{layer}"]')
for e in entities:
msp.delete_entity(e)
doc.layers.remove(layer)
# example: remove "crosshairs" inside holes (adjust logic as needed)
for circle in msp.query('CIRCLE'):
center = circle.dxf.center
radius = circle.dxf.radius
lines = [ln for ln in msp.query('LINE')
if ln.dxf.start.distance(center) <= radius and ln.dxf.end.distance(center) <= radius]
for ln in lines:
msp.delete_entity(ln)
doc.saveas(output_path)
print(f"Cleaned: {output_path}")
for filename in os.listdir(input_folder):
if filename.lower().endswith('.dxf'):
input_path = os.path.join(input_folder, filename)
output_path = os.path.join(output_folder, filename)
clean_dxf(input_path, output_path)
print("Processing complete!")The result? What would have taken weeks manually is now done in hours. A 1000× time saving is no exaggeration – instead of spending days on monotonous work, I can focus on more important tasks such as process optimization or innovation. This demonstrates the power of AI: even without deep programming skills, you can create tools that speed up production.
Yet, people are still hesitant. Why? Fear of mistakes – what if the code deletes important parts? Lack of control – the feeling that automation is “taking over.” Resistance to change – we’re used to manual work, even if it’s tedious.
Next article: How AI Helps Predict Failures in Manufacturing. Until then — code boldly! 🚀