ARMONICOS CO., LTD. List of Products and Services

The "spGauge" is a high-performance inspection tool that not only compares point cloud data with CAD data but also enables the alignment of point cloud data from master products with the point cloud data of the measurement target. Additionally, it supports alignment methods using overall shapes or parts, as well as reference features (such as planes, lines, and hole positions). This allows users to select the appropriate alignment method based on the inspection target and purpose, resulting in high-precision inspection results. 【Alignment (Excerpt)】 ■ Alignment at reference features ■ Alignment at cross-sections ■ Touch alignment ■ Alignment that matches corresponding surfaces *For more details, please download the PDF or feel free to contact us.

Even if individual parts are manufactured with high precision, it is not uncommon for parts to deform or misalign during the assembly or welding process. This can potentially affect the overall quality and performance of the product. The assembly evaluation function of "spGauge" analyzes the conditions of deformation and misalignment after assembly in detail, assisting in identifying causes and formulating countermeasures. By utilizing point cloud data obtained from non-contact measurement devices, it visualizes the degree of deformation and misalignment of each part after assembly using color-coded maps and vectors. This allows for an intuitive understanding of problem areas and their trends, contributing to efficient quality improvement activities. 【Features of spGauge Assembly Evaluation Function】 - High-precision fitting with CAD data: The segmented point cloud data is best fitted to the CAD data with high precision. - Visualization of deformation areas: Deformed parts are visually displayed using a color map, clearly identifying problem areas. - Visualization of misalignment: A duplicate of the CAD data is created at the surface point cloud data positions, displaying the direction and amount of misalignment using vectors and numerical values. *For more details, please refer to the page below or feel free to contact us.

Conventional welding spot inspections have been conducted using visual confirmation, distance measurements, and contact measuring devices. However, these methods have faced challenges such as long measurement times, variability in results due to the operator, and low detection rates when the indentations are small. Particularly, when the shape reproducibility of 3D scanners is low, automatic detection can become difficult. The "AI Welding Spot Inspection" feature addresses these challenges, enabling simpler and more precise welding spot inspections. By analyzing the curvature gradient images projected onto the weld marks, AI reduces measurement time, improves detection accuracy, and eliminates variability in results caused by human operators, significantly contributing to the efficiency and reliability of welding quality management. **Features of spGauge AI Welding Spot Inspection:** - Achieves a high automatic detection rate even when the indentations of the weld marks are small or when the shape reproducibility of the 3D scanner is low. - Significantly reduces false detections compared to conventional inspection functions. - Supports a series of tasks including automatic pairing with 3D measurement data, automatic detection of positional deviations, and report generation. *For more details, please refer to the page below or feel free to contact us.*

In the preparation for product inspection, there was a challenge where the information on dimensional tolerances existed only in 2D drawings, and the task of manually applying this to 3D models was very time-consuming and prone to human error. The "drawing import function" has made it possible to automatically recognize dimensional tolerance information from 2D DXF drawing data and directly reflect it in the corresponding 3D model. This significantly reduces the time required for inspection preparation. 【Features of the spGauge Drawing Import Function】 - Import of 2D drawing information: Information such as "dimensions," "geometric tolerances," and "datums" is imported from 2D drawing data (DXF) into the 3D coordinate space of spGauge. - Element identification and placement in 3D: The imported 2D drawing data is separated into elements such as front view and top view, and placed in the corresponding positions within the 3D shape data. - Automatic recognition of dimensions and geometric tolerances: Dimensional attributes such as dimension values, tolerance values, symbols, and leader lines are automatically recognized. - Automation of GD&T inspection: The recognized information is converted into spGauge's GD&T inspection attributes, enabling automatic inspection of the measured point cloud data! *For more details, please refer to the page below or feel free to contact us.

T-Promotion is a system that uses polygon data from the front and back of press panels (non-contact measurement data) to evaluate the thickness variation rate relative to the reference plate thickness. *Patent pending. It allows for the confirmation of trends in the overall thickness variation rate of the panel and eliminates measurement omissions. It is effective for detecting thickness reduction and necking areas. The application of thickness evaluation is effective in the following operations: - Utilizing thickness evaluation during the production preparation stage ✓ Confirming the occurrence of necking and cracking ✓ Identifying processes where thickness reduction occurs: Evaluating thickness between process panels, determining at which process the thickness reduction is happening ✓ Evaluating formability: When changing press conditions, identifying which conditions result in less thickness reduction - Detecting changes through regular thickness evaluations of panels after mass production and utilizing this for maintenance.

Detection of hole shapes faithful to the actual pressed products The accuracy of the center position is quite important for holes used in the assembly of pressed products. Are you facing issues with the extraction accuracy of hole center coordinates in conventional inspection methods? spGauge can recognize "round holes," "square holes," and "long holes" from CAD data, and can automatically extract and inspect them from measurement point cloud data. Inspection of gaps and interference areas in shape inspection Automotive door panels are composed of multiple pressed parts. By utilizing non-contact measurement point cloud data of each pressed part along with CAD data, it is possible to virtually assemble them and detect gaps and interference areas between parts, obtaining numerical information as well. CATS (CAT-Solution) automatic detection of defects in pressed products This function detects defective areas from shape inspection results (color maps) and can provide details of the defects and instructions for countermeasures. - Edge warping - Twisting and wall warping - Springback - Spring go - Shape defects

The optional feature "CATS_Mold" of spGauge automatically detects defective areas based on the shape inspection results (color map) of resin molded products and displays notes according to the phenomena. The detectable phenomena include a wide range such as "sink marks," "insufficient filling," "abnormalities in ejector pin marks," "dimensional abnormalities," and "warping/twisting," with different optimal countermeasures for each phenomenon. In CATS_Mold, countermeasures corresponding to the defect phenomena are selected from a database, integrating the phenomenon and countermeasure for note display. This allows for consistent inspection and countermeasure instructions by anyone, without relying on the judgment of experienced personnel. Additionally, in molds with multiple cavities, variations in products can occur even with the same processing data (CAM) due to the influence of gate positions and cooling channels, making it time-consuming to align everything to the same shape or make adjustments to fit combinations. By using spGauge, it is possible to understand the deformation trends of each part from the shape inspection data and find the optimal combination pattern that minimizes reprocessing.