Cutting Tool Monitoring AI&quot; Documentation Download

PROBLEM

Is Your Cutting Tool Management Done Correctly?

Managing consumable tools is crucial in the field of machining. However, effectively assessing their condition can be challenging, and abrupt issues may arise with cutting tools. Inability to manage tool conditions accurately can potentially lead to the following problems.

We want to achieve unmanned overnight operation, but we are concerned about the risk of defective products.

Overlooking minor chipping and continuously producing defective products.

It is difficult to estimate tool wear, and the optimization of replacement frequency is not possible.

SOLUTIONS

This can be resolved with Cutting Tool Monitoring AI

MAZIN's Cutting Tool Monitoring AI analyzes the current flowing through the motor for each operation, checking for abnormalities and tool wear values. AI suggests the appropriate tool replacement timing, freeing you from the challenges of tool management.

AI detects abnormalities and controls operations.

→Minimizes the risk of defective products.

It accurately detects even fine chipping.

→AI detects abnormalities and Prevents continuous discharge of defective products operations.

AI visualizes tool wear in numerical values.

→You will know the right replacement time.

ABOUT

Cutting Tool Monitoring AI

MAZIN's AI measures and learns the current values flowing through the motor for each operation, analyzes the current waveform, and understands the tool's condition. Using a proprietary algorithm, it can detect tool abnormalities with high precision and estimate wear values.

STRUCTURE

The Mechanism of Cutting Tool Monitoring AI

STEP01

Measuring the Current Flow in the Motor During Machining

An external clamp-type current sensor, which can be installed in the control panel of the machine tool, is used to measure the current data flowing through the spindle and servo motors.

STEP02

Feature Extraction

An external clamp-type current sensor, which can be installed in the control panel of the machine tool, is used to measure the current data flowing through the spindle and servo motors. MAZIN's proprietary algorithm is then used to extract features from the measured current data. These features reflect tool anomalies and wear, allowing for a more detailed analysis of the tool's condition.

STEP03

Detection of Anomalies and detection of Wear

Based on the extracted features, the AI estimates the presence of tool anomalies and the degree of wear. If an anomaly occurs, the AI notifies the user through the system and can perform automatic machine control as needed. Additionally, the tool wear value is displayed numerically, allowing for the intuitive assessment of even subtle wear changes that may be difficult to discern with the naked eye.

SOLUTIONS

Expected Implementation Benefits

Prevention of Defective Product Output

By detecting tool abnormalities in real-time, immediate and appropriate actions can be taken, resulting in the prevention of defective product output.

Reduced Tool Replacement Frequency

Optimizing tool lifespan leads to a reduced frequency of tool replacement and cost savings for tools.

Reduction in Inspection Workload

AI records the detected tool condition and allows for analyzing it as time-series data, leading to a reduction in rework inspection workload.

ADVANTAGES

Benefits of Implementing Cutting Tool Monitoring

POINT01

Threshold-Free Anomaly Detection

Conventional cutting tool anomaly detection systems predominantly rely on threshold-based anomaly detection, where the accuracy of the threshold is crucial, making its adjustment a time-consuming process. Furthermore, there have been challenges in detecting subtle anomalies and other types of anomalies that were not detectable.MAZIN eliminates the need for thresholds and automatically measures and learns the current flow in the cutting tools during cutting processes, enabling precision anomaly detection based on data without the requirement for thresholds.

POINT02

Optimization of Tool Lifespan

Setting the correct tool lifespan requires real-time monitoring of wear and tool anomalies. However, in practice, this can be challenging, often leading to shorter estimations of tool lifespan. With our system, you can monitor tool wear and anomalies in real time, eliminating the need to set a shorter tool lifespan to prevent anomalies. This enables the optimization, and ultimately, the extension of tool lifespan based on wear.

STRONG POINT

Reasons to Choose MAZIN

Applicability

Being an external type, it can be used with all machines without manufacturer or year restrictions.

Implementation Ease

No need for program modifications or NC connection work. After the contract, you can start using it right away.

Operational Load

Since AI automatically performs measurements and analysis, there is no need for user-defined threshold settings, making operation easy.

Detection Accuracy

With no accuracy deviations due to threshold settings, AI performs precise anomaly detection based on data.

FAQ

Frequently Asked Questions

Please tell me about the pricing plans.

The features can be customized to fit your specific needs. Please contact us for more details.

Are there any restrictions on the facilities that can be implemented?

No, there are no restrictions. This product is an external system that can be installed easily without the need for additional construction work and can be implemented regardless of the manufacturer or year of the equipment.

Can you explain the process leading up to implementation?

To begin with, we will introduce the product to you through a web conference. During our discussions, we will confirm the applicability to your company's production, and if necessary, perform verification on the equipment you are considering for implementation. Once we have confirmed the product specifications and its suitability for your company's factory, an implementation decision can be made.