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Complex and exquisite patterns are sculpted on the surface to beautify the parts. Due to the thin-walled nature, the blank of the part is often deformed by the forming and clamping processes, disabling the nominal numerical control (NC) sculpting programs. To address this problem, a fast adaptive sculpting method of the complex surface is proposed.

The geometry of the blank surface is measured using on-machine measurement (OMM). The real blank surface is reconstructed using the non-uniform rational basis spline (NURBS) method. The angle-based flattening (ABF) algorithm is used to flatten the reconstructed blank surface. The dense points are extracted from the pattern on the image using the OpenCV library. Then, the dense points are quickly located on the complex surfaces to generate the tool paths.

By flattening the reconstructed surface and creating the mapping between the contour points and the planar mesh triangular patches, the tool paths can be regenerated to keep the contour of the pattern on the deformed thin-walled surface.

The proposed method can adjust the tool paths according to the deformation of the thin-walled part. The consistency of sculpting patterns is improved.

A new system for on‐machine measurement of workpieces is described. The system is based on a fine touch sensor that enables the cutting tool itself to act as a contact probe to inspect the workpiece. The proposed measurement technology combines the Q‐setter with the fine touch sensor. This low cost measurement system is applied to automatic workpiece setup and improving workpiece dimensional accuracy on a CNC turning center. It is shown that using the proposed measurement system results in workpiece setting time decrease and workpiece machining accuracy improvement. The development of the on‐machine measurement system makes the fine touch sensor extremely attractive for CNC machine retrofitting.

This paper aims to propose a non-contact method using machine vision for measuring the surface roughness of a rotating workpiece at speeds of up to 4,000 rpm.

A commercial digital single-lens-reflex camera with high shutter speed and backlight was used to capture a silhouette of the rotating workpiece profile. The roughness profile was extracted at sub-pixel accuracy from the captured images using the moment invariant method of edge detection. The average (Ra), root-mean square (Rq) and peak-to-valley (Rt) roughness parameters were measured for ten different specimens at spindle speeds of up to 4,000 rpm. The roughness values measured using the proposed machine vision system were verified using the stylus profilometer.

The roughness values measured using the proposed method show high correlation (up to 0.997 for Ra) with those determined using the profilometer. The mean differences in Ra, Rq and Rt between the two methods were only 4.66, 3.29 and 3.70 per cent, respectively.

The proposed method has significant potential for application in the in-process roughness measurement and tool condition monitoring from workpiece profile signature during turning, thus, obviating the need to stop the machine.

The machine vision method combined with sub-pixel edge detection has not been applied to measure the roughness of a rotating workpiece.

A multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body and quality evaluation. This paper aims to discuss the aforementioned idea.

First, the differential body is set on a rotation platform before measuring. Then one laser sensor called as “primary sensor”, is installed on the intern of the differential body. The spherical surface and four holes on the differential body are sampled by the primary sensor when the rotation platform rotates one revolution. Another sensor called as “secondary sensor”, is installed above to sample the external cylinder surface and the planar surface on the top of the differential body, and the external cylinder surface and the planar surface are high in manufacturing precision, which are used as datum surfaces to compute the errors caused by the motion of the rotation platform. Finally, the sampled points from the primary sensor are compensated to improve the measurement accuracy.

A multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body. Based on the characteristics of the measurement data, a gradient image-based method is proposed to distinguish different objects from laser measurement data. A case study is presented to validate the measurement principle and data processing approach.

The study investigates the possibility of correction of sensor data by the measurement results of multiple sensors to improving measurement accuracy. The proposed technique enables the error analysis and compensation by the geometric correlation relationship of various features on the measurand.

The proposed error compensation principle by using multiple sensors proved to be useful for the design of new measurement device for special part inspection. The proposed approach to describe the measuring data by image also is proved to be useful to simplify the measurement data processing.

As the infrastructure of the railway, the rail could sink or deform to different degrees due to the impact of train operation or the geological changing force for years, which will lead to the possibility that the facilities on both sides of the rail invade the rail clearance and bring hidden dangers to the safe operation of the train. The purpose of this paper is to design the gauge to measure the clearance parameters of rail.

Aiming at the problem, the gauge for clearance measurement was designed based on a combination measurement method in this paper. It consists of the measurement box and the rail measurement vehicle, which integrates a laser displacement sensor, inclination sensor, gauge sensor and mileage sensor. The measurement box was placed outside the rail vehicle. Through the design of a hardware circuit and software system, the movement measurement of the clearance parameters was realized.

In this paper, the measurement equations of horizontal distance and vertical height were established, the optimal solutions of the structural parameters in the equations were obtained by Levenberg–Marquardt method, then the parameter calibration problem was also solved.

The gauge has high precision; its measurement uncertainty reaches 1.27 mm. The gauge has manual and automatic working modes, which are convenient to operate and have practical popularization value.

Refurbishing may be the most practical approach under the low volume production. This effort aims to achieve robotic laser cladding with the main purpose of achieving maximum processing flexibility, predictably high quality, lower maintenance and operating costs. This study aims to focus on online measurement and cladding path generation toward automatic laser cladding.

Based on the specific requirements of automatic laser cladding, an approach was proposed toward an automatic laser cladding with powder injection for the refurbishment of components with free‐form surfaces. This study assessed the feasibility of integrating a non‐contact free‐form surface measurement system, an industrial robot, and an algorithm for generating cladding tool paths seamlessly.

3D laser scanning and laser cladding systems can be embedded into an existing robot motion control system. Online measurement based 3D surface reconstruction is a practical approach toward cladding tool path generation for on‐site refurbishment.

Robotic laser cladding may be a potential application by integrating other measurement devices, such as temperature sensor based monitoring system.

Refurbishing worn‐out components could have significant economic benefits. This study indicates that robotic laser cladding may potentially facilitate improved refurbishment of oversized components.

Presently, 6 Degree of Freedom (6DOF) visual pose measurement methods enjoy popularity in the industrial sector. However, challenges persist in accurately measuring the visual pose of blank and rough metal casts. Therefore, this paper introduces a 6DOF pose measurement method utilizing stereo vision, and aims to the 6DOF pose measurement of blank and rough metal casts.

This paper studies the 6DOF pose measurement of metal casts from three aspects: sample enhancement of industrial objects, optimization of detector and attention mechanism. Virtual reality technology is used for sample enhancement of metal casts, which solves the problem of large-scale sample sampling in industrial application. The method also includes a novel deep learning detector that uses multiple key points on the object surface as regression objects to detect industrial objects with rotation characteristics. By introducing a mixed paths attention module, the detection accuracy of the detector and the convergence speed of the training are improved.

The experimental results show that the proposed method has a better detection effect for metal casts with smaller size scaling and rotation characteristics.

A method for 6DOF pose measurement of industrial objects is proposed, which realizes the pose measurement and grasping of metal blanks and rough machined casts by industrial robots.

Large size of aircraft assembly tooling structure and complex measurement environment exist. The laid enhanced reference points (ERS) are subject to a combination of nonuniform temperature fields and measurement errors, resulting in increased measurement registration errors. In view of the nonuniform temperature field and measurement errors affecting the ERS point registration problem, the purpose of this paper is to propose a neural network-based ERS point registration compensation method for large-size measurement fields under a nonuniform temperature field.

The approach is to collect ERS point information and temperature data, normalize the collected data to complete the data structure design and complete the construction of the neural network prediction model by data training. The data learning is performed to complete the prediction model construction, and the prediction model is used to complete the compensation analysis of ERS points. Finally, the algorithm is verified through experiments and engineering practice.

Experimental results show that the proposed neural network-based ERS point prediction and compensation method for nonuniform temperature fields effectively predicts ERS point deformation under nonuniform temperature fields compared with the conventional method. After the compensation analysis, the registration error is effectively reduced to improve registration accuracy. Reducing the combined effect of environmental nonuniform temperature field and measurement error has apparent advantages.

The method reduces the registration error caused by combining a nonuniform temperature field and measurement error. It can be used for aircraft assembly site prediction and registration error compensation analysis, which is essential to improve measurement accuracy further.

This paper aims to present a methodology for volumetric error compensation. This technique is applied to an Objet Eden350V 3D printer and involves a custom measurement strategy.

The kinematic model of the printer is explained, and its error model is simplified to 18 independent error functions. Each error function is defined by a cubic Legendre polynomial. The coefficients of the polynomials are obtained through a Levenberg–Marquardt optimization process. This optimization process compares, in an iterative algorithm, nominal coordinates with actual values of the cloud of points. The points are built in the faces of a gauge artefact as conical sockets defining one unique point for each socket. These points are measured by a coordinate measuring machine self-centring measurement process.

Most of the errors of the 3D printer are systematic. It is possible to obtain an improvement of 70 per cent in terms of global mean error reduction in single points within a volume of 120 × 120 × 40 mm. The forecast of the final error compensation fully matches the actual final error.

This methodology can be used for accuracy improvement in additive manufacturing machines.

Unlike the calculation of geometric errors, the proposed parametric determination through optimization of the error model allows global error reduction, which decreases all sort of systematic errors concurrently. The proposed measurement strategy allows high reliability, high speed and operator independence in the measurement process, which increases efficiency and reduces the cost. The proposed methodology is easily translated to other rapid prototyping machines and allows scalability when replicating artefacts covering any working volume.