Study of geometrical defects of free-form surface machined using neural network

The manufacture of total hip arthroplasty (THA) requires the control of the quality of free form surfaces. In fact, the polyethylene insert is deformed to fit the overall geometry of the femoral part, which has an impact on the quality of the contact. In this paper, we propose a method for evaluating the defects of complex forms. The originality of the approach is the use of artificial intelligence to position the cloud of measured points, obtained with a three-dimensional measuring machine equipped with a contactless sensor, with regard to the 3D CAD model of the THA. The artificial intelligence algorithm used is based on neural networks that are trained using a virtual positioning realized with 3D CAD software. Finally, the difference between the positioned point cloud and the CAD model allows us to evaluate the shape defect of the measured THA surface. We found that the error of the proposed method is at the vicinity of micron scale.

Spectroscopic Approach to Correction and Visualisation of Bright-Field Light Transmission Microscopy Biological Data

The most realistic information about a transparent sample such as a live cell can be obtained using bright-field light microscopy. Under high-intensity pulsing LED illumination, we captured a primary 12-bit-per-channel (bpc) response from an observed sample using a bright-field microscope equipped with a high-resolution (4872 × 3248) image sensor. In order to suppress data distortions originating from the light interactions with elements in the optical path, poor sensor reproduction (geometrical defects of the camera sensor and some peculiarities of sensor sensitivity), we propose a spectroscopic approach for the correction of these uncompressed 12 bpc data by simultaneous calibration of all parts of the experimental arrangement. Moreover, the final intensities of the corrected images are proportional to the photon fluxes detected by a camera sensor. It can be visualized in 8 bpc intensity depth after the Least Information Loss compression.

METHOD TO DEFINE MEASUREMENT UNCERTAINTY FOR DESIGN SPACE EXPLORATION IN ADDITIVE MANUFACTURING

Additive manufacturing is a process used for quick prototyping in industries. Geometrical defects are observed on printed parts. The aim of the paper is to propose a design method to implement measurements uncertainties into a Design Space for Additive Manufacturing parameters selection. To do so, two tests have been realized. The first test consists in determining the instrument’s uncertainty by measuring a standard length several times by an operator. The second test aim to determine the uncertainty within operators mesurement of geometric outputs (clad’s height, clad’s width, dilution’s height, dilution’s width and contact angle). Based on the results of our tests, uncertainties have been applied in our Design Space populated with 31 real printed clads. The uncertainties display with error bars on scatterplots allow to capitalize the knowledge for his/her exploration of the Design Space for future prints. The given information provides to ease the engineer to select the optimal solution (laser power, tool speed and wire feed speed) for his/her given Additive Manufacturing problematic among candidate points

A CAD model for the tolerancing of mechanical assemblies considering non-rigid joints between parts with defects

During the design stage, the ideal simulation and visualization of the mechanical assemblies behavior require the modeling of parts with dimensional and geometrical defects. However, the deviations caused by parts deformations can generate an important difference between the ideal assembly and the real product. In this regard, this paper proposes a tolerance analysis method of CAD assemblies considering non-rigid joints between parts with defects. The determination of realistic rigid components with dimensional and geometrical defects is based on the worst case tolerancing approach and the Small Displacement Torsor (SDT) parameters. The Finite Element (FE) computation is executed to determine deformations of realistic non-rigid part models under external loads. Sub-algorithms to define non-rigid joints between realistic parts are developed. The tolerance analysis is established using the realistic CAD assembly. A case study is presented to evaluate the proposed model.

A Novel Method of Defect Detection Based on Experimental Investigation of Galvanized Steel Welded Plates Using Wobble Laser Process

This paper aims at investigating the effect of laser welding parameters on the hardness profile, using hardness mapping analyses, and welding geometry of galvanized steel plates. Hardness distribution and geometry deflection of galvanized welded thin plates are commonly applied in fields where the weld quality is of utmost importance. Due to the welding process and material condition, welding galvanized steel is one of the problematic matters in welding technology. Here, the design of experiment (DOE) approach is used to study the effect of process parameters. Using a pattern matrix of micro-indentation hardness experiment, the welding defects are visualized on hardness profile of the weld cross-section. The effect of process parameters on welding defect formation is then qualitatively analyzed. The geometrical defects of welding such as weld width and voids are then quantitatively studied based on analysis of variance (ANOVA), and predictive models of welding voids and weld seam width are developed based on the regression method. Response surface method (RSM) is then applied to define the trend of process factors interaction on the welding defects. The experimental results confirm the reliability of developed predictive models of welding defects geometry, weld width, and voids area of laser-welded galvanized plates.