Das Ziel
With a limited budget an three months time, Optimate aimed to prove that Machine Learning can help to predict if sheet metal parts can be optimized while keeping optimal product quality and functionality.
Die Lösung
A web demonstrator backed by a Machine Learning algorithm that easily visualizes the optimization of a metal building part and thereby serves as a proof of concept.
Die Auswirkung
A business that sets out to shape the industrial manufacturing industry through AI-based optimization technology.
Can Machine Learning help to predict if sheet metal parts can be optimized?
The Optimate team noticed that sheet metal parts are often designed in a non-optimal way. On the one hand, there was the cost factor. In the metal manufacturing industry, every piece of metal wasted is money lost. On the other hand, there was the time factor. Although optimization efforts existed before, they were not time-efficient enough to justify the complex process.
They were looking for a way to automate the manual optimization efforts that existed before. The right solution would not only save time and money but would also keep the optimal product quality.
In the scope of TRUMPF’s intrapreneurship program, the solution had to secure further investments that enable Optimate to become a spin-off company.
Optimate set out to build a design optimization web demonstrator backed by a machine learning algorithm as a proof of concept.
Choosing the right Machine Learning algorithm
The first challenge was generating labeled data for sheet metal parts because nobody had done that before. We had to collect big amounts of data, validate relevantly and sort out irrelevant data. After that, we had to choose between supervised und semi-supervised learning. In order to realistically assess which kind of ML algorithm would work best, we tested both options through hyper parameter tuning. The tests revealed that semi-supervised machine learning delivered slightly better results.
Building the web demonstrator
The web demonstrator had to be backed by the developed ML algorithm and easily visualize the optimization potential of a metal building part. Again, Optimate and Motius collaborated closely and used their combined expertise to take this final step towards the POC.
The result was a web demonstrator that represents the whole desired optimization process. It analyzes uploaded files through machine learning and visualizes the metal part’s optimization potentials through a user-friendly traffic light system.
Revolutionizing the industrial manufacturing industry
The web demonstrator successfully served as a PoC for the Optimate team and secured further investments. Their design optimization concept that successfully enabled Optimate to become a spin-off company. This means that Optimate sets out to shape the industrial manufacturing through design optimization powered by cutting-edge technologies.
The successful project is also the basis for a long-term collaboration between Optimate and Motius to further optimize the tool and establish Optimate in the market.
“We are very satisfied with the cooperation with Motius and look forward to taking the next steps together.”
Jonas Steiling
CEO, Optimate
“Besides the success of our initial project, I am excited for our long-term collaboration with Optimate to shape the industrial manufacturing industry.”
Jannik Finkbohner
Head of Think Tank Stuttgart, Motius
Want to build a product of the future?
Das Ziel
With a limited budget an three months time, Optimate aimed to prove that Machine Learning can help to predict if sheet metal parts can be optimized while keeping optimal product quality and functionality.
Die Lösung
A web demonstrator backed by a Machine Learning algorithm that easily visualizes the optimization of a metal building part and thereby serves as a proof of concept.
Die Auswirkung
A business that sets out to shape the industrial manufacturing industry through AI-based optimization technology.
Can Machine Learning help to predict if sheet metal parts can be optimized?
The Optimate team noticed that sheet metal parts are often designed in a non-optimal way. On the one hand, there was the cost factor. In the metal manufacturing industry, every piece of metal wasted is money lost. On the other hand, there was the time factor. Although optimization efforts existed before, they were not time-efficient enough to justify the complex process.
They were looking for a way to automate the manual optimization efforts that existed before. The right solution would not only save time and money but would also keep the optimal product quality.
In the scope of TRUMPF’s intrapreneurship program, the solution had to secure further investments that enable Optimate to become a spin-off company.
Optimate set out to build a design optimization web demonstrator backed by a machine learning algorithm as a proof of concept.
Choosing the right Machine Learning algorithm
The first challenge was generating labeled data for sheet metal parts because nobody had done that before. We had to collect big amounts of data, validate relevantly and sort out irrelevant data. After that, we had to choose between supervised und semi-supervised learning. In order to realistically assess which kind of ML algorithm would work best, we tested both options through hyper parameter tuning. The tests revealed that semi-supervised machine learning delivered slightly better results.
Building the web demonstrator
The web demonstrator had to be backed by the developed ML algorithm and easily visualize the optimization potential of a metal building part. Again, Optimate and Motius collaborated closely and used their combined expertise to take this final step towards the POC.
The result was a web demonstrator that represents the whole desired optimization process. It analyzes uploaded files through machine learning and visualizes the metal part’s optimization potentials through a user-friendly traffic light system.
Revolutionizing the industrial manufacturing industry
The web demonstrator successfully served as a PoC for the Optimate team and secured further investments. Their design optimization concept that successfully enabled Optimate to become a spin-off company. This means that Optimate sets out to shape the industrial manufacturing through design optimization powered by cutting-edge technologies.
The successful project is also the basis for a long-term collaboration between Optimate and Motius to further optimize the tool and establish Optimate in the market.
“We are very satisfied with the cooperation with Motius and look forward to taking the next steps together.”
Jonas Steiling
CEO, Optimate
“Besides the success of our initial project, I am excited for our long-term collaboration with Optimate to shape the industrial manufacturing industry.”
Jannik Finkbohner
Head of Think Tank Stuttgart, Motius
Want to build a product of the future?
Das Ziel
With a limited budget an three months time, Optimate aimed to prove that Machine Learning can help to predict if sheet metal parts can be optimized while keeping optimal product quality and functionality.
Die Lösung
A web demonstrator backed by a Machine Learning algorithm that easily visualizes the optimization of a metal building part and thereby serves as a proof of concept.
Die Auswirkung
A business that sets out to shape the industrial manufacturing industry through AI-based optimization technology.
Can Machine Learning help to predict if sheet metal parts can be optimized?
The Optimate team noticed that sheet metal parts are often designed in a non-optimal way. On the one hand, there was the cost factor. In the metal manufacturing industry, every piece of metal wasted is money lost. On the other hand, there was the time factor. Although optimization efforts existed before, they were not time-efficient enough to justify the complex process.
They were looking for a way to automate the manual optimization efforts that existed before. The right solution would not only save time and money but would also keep the optimal product quality.
In the scope of TRUMPF’s intrapreneurship program, the solution had to secure further investments that enable Optimate to become a spin-off company.
Optimate set out to build a design optimization web demonstrator backed by a machine learning algorithm as a proof of concept.
Choosing the right Machine Learning algorithm
The first challenge was generating labeled data for sheet metal parts because nobody had done that before. We had to collect big amounts of data, validate relevantly and sort out irrelevant data. After that, we had to choose between supervised und semi-supervised learning. In order to realistically assess which kind of ML algorithm would work best, we tested both options through hyper parameter tuning. The tests revealed that semi-supervised machine learning delivered slightly better results.
Building the web demonstrator
The web demonstrator had to be backed by the developed ML algorithm and easily visualize the optimization potential of a metal building part. Again, Optimate and Motius collaborated closely and used their combined expertise to take this final step towards the POC.
The result was a web demonstrator that represents the whole desired optimization process. It analyzes uploaded files through machine learning and visualizes the metal part’s optimization potentials through a user-friendly traffic light system.
Revolutionizing the industrial manufacturing industry
The web demonstrator successfully served as a PoC for the Optimate team and secured further investments. Their design optimization concept that successfully enabled Optimate to become a spin-off company. This means that Optimate sets out to shape the industrial manufacturing through design optimization powered by cutting-edge technologies.
The successful project is also the basis for a long-term collaboration between Optimate and Motius to further optimize the tool and establish Optimate in the market.
“We are very satisfied with the cooperation with Motius and look forward to taking the next steps together.”
Jonas Steiling
CEO, Optimate
“Besides the success of our initial project, I am excited for our long-term collaboration with Optimate to shape the industrial manufacturing industry.”
Jannik Finkbohner
Head of Think Tank Stuttgart, Motius
Want to build a product of the future?
Das Ziel
With a limited budget an three months time, Optimate aimed to prove that Machine Learning can help to predict if sheet metal parts can be optimized while keeping optimal product quality and functionality.
Die Lösung
A web demonstrator backed by a Machine Learning algorithm that easily visualizes the optimization of a metal building part and thereby serves as a proof of concept.
Die Auswirkung
A business that sets out to shape the industrial manufacturing industry through AI-based optimization technology.
Can Machine Learning help to predict if sheet metal parts can be optimized?
The Optimate team noticed that sheet metal parts are often designed in a non-optimal way. On the one hand, there was the cost factor. In the metal manufacturing industry, every piece of metal wasted is money lost. On the other hand, there was the time factor. Although optimization efforts existed before, they were not time-efficient enough to justify the complex process.
They were looking for a way to automate the manual optimization efforts that existed before. The right solution would not only save time and money but would also keep the optimal product quality.
In the scope of TRUMPF’s intrapreneurship program, the solution had to secure further investments that enable Optimate to become a spin-off company.
Optimate set out to build a design optimization web demonstrator backed by a machine learning algorithm as a proof of concept.
Choosing the right Machine Learning algorithm
The first challenge was generating labeled data for sheet metal parts because nobody had done that before. We had to collect big amounts of data, validate relevantly and sort out irrelevant data. After that, we had to choose between supervised und semi-supervised learning. In order to realistically assess which kind of ML algorithm would work best, we tested both options through hyper parameter tuning. The tests revealed that semi-supervised machine learning delivered slightly better results.
Building the web demonstrator
The web demonstrator had to be backed by the developed ML algorithm and easily visualize the optimization potential of a metal building part. Again, Optimate and Motius collaborated closely and used their combined expertise to take this final step towards the POC.
The result was a web demonstrator that represents the whole desired optimization process. It analyzes uploaded files through machine learning and visualizes the metal part’s optimization potentials through a user-friendly traffic light system.
Revolutionizing the industrial manufacturing industry
The web demonstrator successfully served as a PoC for the Optimate team and secured further investments. Their design optimization concept that successfully enabled Optimate to become a spin-off company. This means that Optimate sets out to shape the industrial manufacturing through design optimization powered by cutting-edge technologies.
The successful project is also the basis for a long-term collaboration between Optimate and Motius to further optimize the tool and establish Optimate in the market.
“We are very satisfied with the cooperation with Motius and look forward to taking the next steps together.”
Jonas Steiling
CEO, Optimate
“Besides the success of our initial project, I am excited for our long-term collaboration with Optimate to shape the industrial manufacturing industry.”
Jannik Finkbohner
Head of Think Tank Stuttgart, Motius