AI-Powered Optimisation
Discover multi-objective optimal process settings through intelligent experimentation and advanced analysis.
Smarter experiments. Better decisions.Optimal performance.
Use structured production data, AI-powered Design of Experiments (DoE) and recommendations to optimise machine settings for your production requirements.
- Find the best conditions
- Run fewer experiments
- Improve quality and efficiency
- Achieve lasting results
- Lower machine downtime
Find optimal conditions, faster.
Optima uses AI-powered Design of Experiments (DoE) and optimisation to help you discover the optimal operating conditions for your machines and processes.
Understand how process parameters impact performance, identify opportunities for improvement, and achieve better results with fewer experiments.
AI-Guided DoE
Identify the most valuable next experiments using AI, reducing unnecessary trials and accelerating optimisation.
Guided Optimisation Workflow
Follow a structured, step-by-step journey using P-Diagrams, experiment design, analysis and optimisation, without complex setup.
Reduce Costs and Resource Usage
Reduce material waste, energy consumption and operational costs through optimised process settings.
Data-Driven Decisions
Make confident decisions backed by experimental evidence, predictive models and operational data.
Optima use case: a CNC machine produces scrap and a poor surface finish. Its feed, speed and depth settings are defined as factors in an Optima project; Optima generates a design of experiments; each experiment is run on the machine and the result added back; analysis shows which AI optimisation learns from the recorded results and recommends parameters; when not satisfied, AI-guided DoE suggests further experiments, which are recorded and optimised again; once satisfied, the optimal parameters are collected in a results table and published to the machine, which then runs with less scrap, a better finish and a shorter cycle.
TaktMind Optima · Walkthrough
From Process Challenges to Optimal Performance.
Discover how OPTIMA transforms real manufacturing challenges into data-driven improvements through intelligent experimentation, AI-guided optimisation and actionable recommendations.
The problem
This machine isn't performing.
High scrap, inconsistent quality, and too much guesswork in the setup.
Surface finish is poor, scrap is high, and the process is being adjusted run after run without confidence. Operators change feed, speed and depth based on experience, but there is no structured way to understand which settings actually improve performance.
01 · Define
Turn the process into an Optima project.
Define the factors, outputs and constraints that matter.
The current machine settings become controllable factors inside Optima. Feed rate, spindle speed and depth of cut are captured alongside the output response you want to improve, such as surface finish, cycle time, energy or yield. Constraints and operating boundaries keep experimentation practical and safe.
02 · DoE
Generate a structured design of experiments.
Replace trial and error with planned experimental runs.
Optima automatically creates a Design of Experiments (DoE) based on the defined factors and ranges. Each run tests a purposeful combination of settings, so you gather the most useful information with fewer trials than manual experimentation.
03 · Record experiments
Run the experiments and record the results.
Every result goes back into Optima.
The generated runs are executed on the real machine, and the measured result of each one is recorded in Optima.
04 · AI optimisation
AI learns from the results and recommends the settings.
Optima models the completed runs and predicts the best operating point.
Once the first experiment set is complete, Optima's AI learns how feed, speed and depth of cut affect the result, then recommends the combination of parameters most likely to hit the target.
05 · AI-guided DoE
Not satisfied? AI suggests the next experiments.
The suggested runs are recorded and optimised again.
If the recommendation is not good enough, or more confidence is needed, AI-guided DoE suggests only the most informative next experiments. They are added to the DoE, run and recorded, and AI optimisation learns again, until the result is satisfactory.
06 · Publish the results
Satisfied? Publish the optimal settings to the machine.
The results table goes live, and the machine proves it.
Once the result is satisfactory, Optima brings the optimal parameters together in one results table and publishes them to the machine. A run on the machine confirms the predicted surface finish, and the settings become the new standard.
The result
Same machine. Better results.
Less scrap, a better finish and a shorter cycle.
From trial and error to intelligent optimisation: process knowledge turned into the best settings for the machine.
Product UI is a simplified illustration. Values shown are demo data.
Traditional approach vs Optima approach.
Move beyond trial and error with intelligent, AI-powered process optimisation.
| Capability | Traditional approach | With TaktMind Optima |
|---|---|---|
| Optimisation approach | Reactive adjustments based on trial and error, experience and assumptions. | Structured, proactive optimisation driven by experimental evidence and predictive modelling. |
| Experimentation strategy | Fixed experiment plans, with additional trials often chosen through engineering judgement. | Adaptive experimentation that evolves based on results, focusing effort where it adds the most value. |
| Multiple objectives | Quality, cost and productivity are often optimised separately, creating trade-offs. | Multi-objective optimisation evaluates competing goals to identify balanced operating conditions. |
| Engineering knowledge | Expert knowledge is often undocumented and depends heavily on individual experience. | Captures engineering knowledge through P-Diagrams, process constraints and structured experimentation. |
| From results to action | Analysis and implementation are often disconnected, requiring manual interpretation and handover. | Connects experimentation, analysis, parameter recommendations and validation in one guided workflow. |
Customer success
15% lower energy usage
and higher quality
“Takt Optima helped us find the optimal process conditions in a fraction of the time. We've reduced energy usage, improved quality and made the process far more stable.”
- 15%Lower energy usage
- 12%Reduced OPEX
- 18%Higher quality yield
From trial and error to intelligent optimisation.
Replace fragmented experimentation and manual analysis with a guided, AI-powered approach that helps you optimise faster, reduce unnecessary experiments and make confident decisions.