Machine control software that uses a ‘black box’ approach does not provide the detailed operational data that you require to optimize and improve your manufacturing processes.
Finding the root cause of an issue is extremely difficult.
Anomaly detection based only on sensor data can be challenging.
Nowadays, software development is a labor-intensive and error-prone activity. Software maintenance (corrective or evolutive) is a potential risk because it can cause unplanned machine down-time.
Traditional coding has unpredictable lead times.
Changing software is complex due to handwritten legacy code.
Machine Control Developers are a scarce resource.
3 to 10x
Faster Development
80%
OPEX reduction
100%
Consistency
Design Level Thinking: low-code is a visual approach towards software development. With it, software & mechatronics engineers can seamlessly work together.
Code generation: the low-code models are automatically translated, bug-free, into executable software that works on hardware platforms from multiple vendors.
Commissioning: it enables engineers to control and visualize machine control data graphically, including all operational and sensor data.
Data platform: an extremely rich data set is generated automatically so that data scientists can improve OEE.
Data Engineering: UI-based data management and transformation; KPI formulas with a low-code approach.
Feature Engineering: UI-based feature formula generation, with types: Range, Time and SQL features.
ML Toolkit: creation of machine learning models in which the different ML algorithms are selected by clicking on available options; the training and usage of the model is done within the platform.
Dashboarding: more than 100 widgets and highly customizable views to analyze data trends.
Get started and request a demo to learn how AI Machine Control Toolkit can help you.
Below you will find answers to the most common questions about AI Machine Control Toolkit.
The AI Machine Control Toolkit allows the creation of prediction models that keep track of every issue detected in the different machine parts. This data is then used for model performance analysis and retraining.
Using the centralized dashboard allows an overview of the company’s metrics and performance, which results in accurate and early detection of production rate deviations, thus saving time and money.
AI Machine Control Toolkit SaaS is delivered in cloud service (e.g. AWS, Azure, etc.) and can also be deployed on-premises, in your private cloud, or data center.
Get started with a functional solution in operation within 2-3 weeks. Monthly subscription. No strings attached. Stop at any time.
With the use of the Synthetic Data Generator (SDG), one of the components inside the Machine Learning Toolkit, it is possible to train your model before production, in order to detect your production data flaws in advance. So, when the model is deployed, only minimal adjustments are needed for it to function properly.
In the digital manufacturing environment, introducing AI into the industrial operational processes has become possible due to the cloud computing capabilities of Industry 4.0. Modern manufacturers leverage this development to marry computer vision hardware along the production line with AI-powered cloud-based digital tools.
No-code solutions for AI application development help manufacturers take advantage of this emerging technology without the need to hire technical specialists or investing significant time and capital.
Tupl's software has a simple and intuitive user interface that enables existing personnel to build AI Machine applications for quality assurance in very little time with relatively small datasets and with no programming required.
Get started and request a demo to learn how AI Machine Control Toolkit can help you.
