the customized automation solution
Your partner for
Process- & Measurement technology
We combine physical expertise with modern measurement technology to develop reliable solutions for complex measurement tasks.
Increased efficiency & quality assurance
By automating measurement and inspection processes, you can save valuable time and resources. Our technologies enable faster data acquisition and analysis, resulting in more efficient production. At the same time, automated testing helps ensure consistent product quality and meet customer requirements. Thorough inspection and validation reduce the risk of defective products and costly recalls.
Precision and accuracy
Our measurement technology & test engineering solutions ensure precise measurements for the control of automated processes. High-quality sensors accurately monitor temperature, pressure, fill level and flow rate, optimize systems and detect potential problems at an early stage. Our solutions ensure reliability, improved product quality and increased efficiency.
Cost savings & customer satisfaction
By optimizing your processes and reducing waste, you can cut costs and increase your competitiveness. Our solutions are designed to minimize your production costs and maximize profitability. By improving the quality of your products and delivering consistent results, you can increase customer satisfaction and build long-term customer relationships.
Measurement Starts with Understanding
What we do…
Complex measurement tasks require more than just the right sensor. We combine physical and mathematical expertise with modern sensor technology, data processing, and automation to develop reliable solutions even where standard methods reach their limits.
Typical applications include measuring component dimensions, determining position and orientation, and detecting defects, contamination, or deviations. We analyze the underlying process, identify the relevant influencing factors, and use these insights to develop a suitable measurement and evaluation concept.
From machine vision and point clouds to sensor fusion, mathematical modeling, and simulation, we create robust, reproducible, and industrial-grade solutions for quality assurance, process analysis, and component detection.
your industry, our expertise
Target group-oriented automation solutions from Vimotech
Steel and aluminum production
Automotive
Aerospace
Packaging industry
The basis
Process analysis & Measurement concept
Before a measurement system can be developed or a process optimized, it must be clearly defined what exactly needs to be measured, evaluated, or improved. Together with you, we therefore analyze the existing process, relevant influencing factors, sources of interference, and technical constraints.
We break down complex processes into manageable subproblems and build a solid understanding of the underlying physical relationships. In doing so, we consider potential sources of error throughout the entire measurement chain – from mechanics, component variation, and environmental conditions to sensor technology and data evaluation.
Based on this analysis, we define measurands, tolerances, permissible measurement uncertainties, and performance parameters. We also take into account requirements for repeatability, reproducibility, and traceability, while clearly distinguishing between actual process variation and measurement deviations.
Where appropriate, we define references, measurement standards, and suitable KPIs. Depending on the application, we follow established standards and methods such as ISO 10012, ISO 14253, and Measurement Systems Analysis (MSA).
This creates a reliable technical foundation for sensor selection, the measurement concept, and subsequent acceptance testing – as well as an objective basis for reliably demonstrating improvements.
First mechanic. then magic.
Designing Mechanics & Sensor Systems Correctly
The quality of a measurement system is not determined by the evaluation algorithm alone. What matters is that mechanics, sensor technology, and software are considered as one complete system. Errors caused by unsuitable mechanics or inappropriate sensors can only be compensated for to a limited extent through software later on.
We therefore begin by analyzing the mechanical conditions of the measurement process. This includes component positioning and fixation, stiffness, repeatability, motion sequences, wear, temperature, vibration, and other environmental influences. This makes it possible to assess early on what level of measurement accuracy can realistically be achieved under actual production conditions.
Based on this analysis, we select the appropriate measurement principle and sensor technology. We consider not only the nominal accuracy of a sensor, but also factors such as measurement range, resolution, measurement speed, surface properties, installation conditions, environmental influences, calibratability, and data processing.
For optical measurement methods, we also take optics, lighting, viewing angle, and the properties of the measured surface into account. Reflective, transparent, or highly variable materials in particular can be decisive when choosing a measurement method that works reliably.
Already during the design phase, we consider how the system can later be calibrated, verified, and maintained. Measurement setups, references, and calibration options are designed so that changes caused by maintenance, wear, or mechanical modifications can be detected and compensated for.
Our principle is simple: mechanics before sensors, sensors before software. A well-designed measurement chain reduces development effort, increases robustness, and provides the foundation for repeatable and long-term stable measurement results.
A measurement system is only as good as the way its data is processed. Our goal is to provide measurement results quickly, reproducibly, and traceably – even with large data volumes and short cycle times.
To achieve this, we clearly separate data acquisition from data evaluation and structure the entire processing chain so that only the data actually required for the measurement result is processed. Where appropriate, evaluations already begin during ongoing acquisition or are parallelized to keep cycle times as short as possible.
In addition to the measurement result itself, we also store – depending on the application – raw data, relevant process data, and the evaluation version used. This makes it possible to reproduce measurements later, trace the causes of OK/NOK decisions, and further develop algorithms in a targeted manner.
We place particular emphasis on version control and traceability. This ensures that it remains documented which software version, parameters, and input data were used to generate a specific measurement result.
The results are visualized in a way that allows operators and quality assurance teams to quickly identify and understand deviations. At the same time, the data can be connected to higher-level systems and used for reporting, quality documentation, process optimization, or subsequent analysis.
This creates not only a fast measurement system, but also a reliable data foundation for quality, diagnostics, and continuous improvement.
Testing technology
Processing Sensor Data & Ensuring Traceable Storage
The wisdom of the crowd
Capturing & Intelligently Analyzing Point Clouds
Lasers, cameras, and optical 3D sensors can generate large volumes of spatial measurement data within a very short time. The real challenge, however, begins afterwards: extracting reliable, reproducible, and process-relevant information from the point cloud.
Depending on the application, we determine component dimensions, position and orientation, surface deviations, or geometric features from the acquired data. Both known and only partially known geometries can be evaluated and related to one another.
Accurate calibration and transformation of coordinate systems are particularly important. If the sensor or component is moved, we link the measurement data with the machine kinematics and take positioning accuracy, mechanical changes, and possible drift into account. After maintenance work or due to wear, the system can be recalibrated in a targeted manner.
Large point clouds are reduced and structured so that only the information actually required is processed. Noise, outliers, and irrelevant areas are handled selectively to reduce processing time and data volume without compromising the required measurement quality.
Depending on the complexity, processing is performed directly on the PLC or on an industrial PC. Raw data and results can be stored and visualized, ensuring that measurements remain reproducible and that deviations can be clearly traced by operators and quality assurance teams.
This turns spatial measurement data into actionable information for quality inspection, component detection, positioning, wear analysis, and process optimization.
Camera systems provide a highly flexible way to capture components and processes without physical contact. Typical applications include detecting material and surface defects, classifying components, dimensional inspection, presence checks, and determining position and orientation.
However, a reliable vision system depends on much more than the camera itself. Resolution, optics, lighting, installation conditions, component positioning, and surface properties all have a major influence on how stable and reproducible the evaluation will be. That is why we consider the complete system from the very beginning of the design process.
Depending on the application, we use classical image processing, geometric methods, or data-driven approaches. Our goal is always a robust and transparent evaluation that continues to work reliably even under changing production conditions.
Another key focus is identification and traceability. We not only integrate cameras for reading QR or DMC codes, but can also provide the corresponding marking systems used to label components directly within the production process. Both the marking system and the inspection camera are integrated with your control system as well as higher-level production or customer systems.
This enables components to be uniquely identified, production data to be assigned automatically, recipes to be loaded, and process or quality information to be documented seamlessly.
Raw images, measurement results, and relevant process information can also be stored, allowing decisions to be traced later and defect patterns to be analyzed in detail. In this way, a camera becomes a reliable tool for quality assurance, identification, traceability, and process automation.
See more. Decide confident.
Machine Vision
More sensors. More truth.
Sensorfusion
A single sensor usually captures only part of the actual process. By combining multiple independent measurement variables, we can validate measurement results, reduce uncertainty, and gain additional information about the component or process.
For example, a geometric measurement can be complemented by temperature, force, displacement, pressure, or vibration data. This makes it possible to account for influences that remain hidden when using only a single measurement method, such as thermal expansion, mechanical loads, or changing process conditions.
What matters is not the number of sensors, but how meaningfully they complement one another. We analyze which measurement variables actually provide additional information and combine them using suitable mathematical models and evaluation methods.
The result is more robust, accurate, and meaningful measurement data – especially in complex processes where individual sensors reach their limits.
When a process is sufficiently understood, its behavior can often be described using a simplified mathematical or physical model. This is where we start: we reduce complex relationships to the influencing factors that are actually relevant to the specific task.
With these models, we can investigate process parameters, predict system behavior, and virtually test planned changes before implementing them on the real machine. This is particularly valuable for thermal or very slow processes, where tests that would take hours in reality can be evaluated much faster in a model.
Our goal is not to reproduce every physical detail with maximum complexity. What matters is an accurate enough, transparent, and computationally efficient model that supports sound technical decisions. This also follows the principle of reduced-order modeling, where computational effort is deliberately reduced without losing the relevant system behavior.
The models can be used for parameter optimization, feasibility studies, variant comparisons, virtual testing, or preparation for commissioning. Simulation and virtual commissioning help identify errors and optimization potential before changes are made to the real system.
For development tasks, a deliberately simplified model is often sufficient. Where it provides additional value, however, the model can be further developed and integrated into ongoing operation – for example to predict process duration and process behavior, optimize recipes, or support operator decisions.
This turns physical process understanding into a practical tool for evaluating changes faster, reducing development risks, and systematically optimizing processes.
Simulate first. Test later.
Modelling and Simulation
The Vimotech concept for success
Measuring & testing technology taken further
Robustness and durability as well as flexibility and adaptability
The devices must be able to withstand the demanding conditions in industrial environments, such as high temperatures, humidity, vibrations and chemical influences. Furthermore, the technology should be easy to integrate into different applications and systems and be able to adapt to changing requirements.
Data acquisition and analysis
In addition to the acquisition of measurement data, the analysis and interpretation of this data is also of crucial importance. Advanced analysis tools can help to detect patterns, identify anomalies and reveal optimization potential.
Conformity and certification - integration of Industry 4.0 technologies
Especially in regulated industries such as the steel and aluminum industry, it is important that the measurement technology complies with the applicable norms and standards and possibly has certifications such as ISO 9001 or NADCAP approvals. The integration of Industry 4.0 technologies such as IoT (Internet of Things), artificial intelligence and machine learning enables even more intelligent and predictive use of measurement and testing technology in automation.