Modern industrial manufacturing is increasingly associated with robots, advanced software, and artificial intelligence. In reality, however, the biggest challenges still emerge where heavy machinery, large scale production, and strict quality requirements come together. In the latest episode of the “AI in Production” podcast, Jakub Orczyk talks with Łukasz Borzęcki, CEO of VM.PL, about a project delivered for a Canadian manufacturer of specialized steel doors.
Table of Contents
Industrial Manufacturing in Practice
The project we delivered for a Canadian company producing specialized steel doors used in public institutions and facilities that require the highest level of security. These included structures designed for locations such as prisons and banks, where every component must meet strict quality and certification standards.
The central challenge of the project focused on cutting openings in steel doors. The client used two different types of machines.
The first was a heavy duty, highly durable older generation machine. It operated through a mechanical cutting process using dedicated cutting heads. These machines were extremely fast and very inexpensive to operate. The cost of making a single opening was less than one cent.
The second solution involved modern plasma cutting machines. They offered very high precision and the ability to create almost any shape, including highly irregular patterns. The problem was that operating these machines was significantly more expensive. Higher energy consumption, increased maintenance costs, and longer processing times made production less cost effective.
In theory, the process worked correctly. More complex shapes were assigned to plasma cutters, while simpler ones were produced using older mechanical machines.
In practice, however, it turned out that a large number of components were classified as too complex for low cost production, even though they could actually be manufactured using a much simpler method.
A Problem No One Had Solved Before
The client had been trying to solve this issue for several years. The challenge was not limited to shape analysis itself, but also involved the complexity of the production data.
As part of the project, the client provided CAD files containing the shapes used in the manufacturing process. Analyzing these files became the foundation for developing a solution that optimized machine operations.
The engineers responsible for the technical documentation did not always create perfectly accurate geometric models. Some shapes contained small imperfections. Lines did not fully connect, points were slightly misaligned, and certain forms deviated from ideal geometric figures.
For standard algorithms, this meant only one thing: the element was classified as irregular and automatically assigned to the plasma cutting machine.
Technology Supporting Efficiency
The podcast conversation demonstrates that successful use of AI in manufacturing does not always require complex models or extensive technological infrastructure.
The key factor was understanding the production process and analyzing data in a way that allowed more accurate classification of individual components. This made it possible to increase the use of lower cost machines and reduce the workload placed on more expensive equipment. The implementation resulted in measurable savings and improved operational efficiency across the production facility.
AI in Industry Means Real Business Value
More and more industrial companies are using artificial intelligence to:
- reduce production downtime,
- lower energy costs,
- optimize operational processes,
- improve quality,
- automate the analysis of technological data.
The project presented in the podcast clearly shows that modern technologies can also support heavy industry and processes that require a very high level of precision.
The Next Stage of the Project
The conversation also touches on the future development of the solution. One possible direction is the use of AI for more advanced shape recognition and automatic recommendations for the most efficient production method.
This demonstrates how industrial digital transformation projects evolve step by step. The process usually begins with process analysis and optimization, followed by the gradual implementation of automation and artificial intelligence solutions.
Listen to the Latest Episode of the “AI in Production” Podcast
If you are interested in the practical use of technology in industry, production process optimization, and real AI implementations in factories, this podcast episode is definitely worth watching.
You will find practical insights into how modern technologies help reduce costs, improve production efficiency, and build competitive advantage in manufacturing.
A detailed case study about this project is also available on our website. It presents the implementation process, the technologies used, and the measurable business results:
Webinar on AI in Pharmaceutical Manufacturing on June 10
On June 10, we are hosting a webinar dedicated to the use of AI in the pharmaceutical industry. During the event, we will present a project that helped reduce production downtime in a pharmaceutical factory and generated savings of up to 400 thousand dollars annually for a single facility. More details are available on our LinkedIn profile.
