Workpiece Features Research Articles

Experimental study of geometric error of CNC turning machine tools based on ISO 13041-6

The product quality of machining results is greatly influenced by the accuracy and precision of CNC lathe machine tools. Regular inspection of the geometric inaccuracy of the machine tool is necessary to verify its operational viability. This research contribution focuses on conducting experimental studies to evaluate machine tool geometric error. The aim is to explore cost-effective measurement methods as alternatives to direct measurements, which often involve laser interferometers and ball bar tests. The objective of this study is to investigate the geometric inaccuracy of a CNC turning machine by conducting experimental cutting tests in accordance with ISO 13041-6:2009. The testing will utilize conventional workpiece forms and requirements, including circularity features, flatness, circular features, and maybe combination features. Several geometric errors that can be acquired with this method include circularity errors, linear positional errors, and squareness errors. The cutting test for each workpiece feature of the given shape and specification requires the use of 5 specimens. Consequently, the mean value of the geometric error may be computed. The geometric error value is derived by the analysis of measurement data collected from a Coordinate Measuring Machine (CMM) applied to a specimen of the machined workpiece. Moreover, the evaluation of the geometric error condition of machine tools is ascertained through the comparison of the average data for each category of geometric error against the permissible standard values given in ISO 10791-2, ISO 10791-4, and ISO 13041-4. The findings of the study indicate that the implementation of the object machine study is not viable for the production of machined workpieces of satisfactory quality. This is primarily due to the presence of geometric errors in CNC turning that exceed the acceptable tolerance levels. Specifically, these errors manifest as linear positional deviations along multiple coordinates along the X-axis and Z-axis, as well as squareness deviations between the X-axis and Z-axis. The maximum value of the linear positional error along the X-axis is 55.2 μm, while the maximum value of the linear positional error along the Z-axis is 25.6 μm. Additionally, the greatest observed squareness error is 37.3 μm. The X and Z machine axes exhibit deviations beyond acceptable limits in terms of unidirectional accuracy and unidirectional repeatability, as per the established norm

Research on Workpiece Intelligent Detection Method Based on SSD Algorithm and Transfer Learning

For the robot intelligent manufacturing workshop, the intelligent detection of workpiece products is the basis for efficient management and precision detection of production lines, which is of great significance for the reliable operation of intelligent manufacturing unmanned production lines. Therefore, SSD algorithm and migration learning in deep learning is studied to sorting the workpieces in this paper. Firstly, based on the five workpieces of screw, nut, gasket, spring, and gear, the multi-workpiece detection data set of the intelligent production line is constructed. Secondly, the multi-scale features of different workpieces are extracted by deep convolution network SSD. Then, the migration learning method is applied to complete the migration network training of workpiece intelligent detection, so the small data detection accuracy is obtained. Finally, after transfer learning solidifying and pruning the transfer learning training model, It is deployed to the embedded terminal Invidia Jetson Nano to realize the online intelligent detection. The experimental results show that the workpiece detection accuracy reaches 99% based on SSD network migration learning. After the network structure optimization pruning embedded deployment, the workpiece detection accuracy is better than 86%, and the frame rate is more than 7FPS. So the proposed method can realize five kinds of workpieces intelligent detection on unmanned production lines.

Spatial position recognition method of semi-transparent and flexible workpieces: A machine vision based on red light assisted

In the automatic sorting process, overlapping translucent and flexible workpieces on the conveyor belt, blurring the imaging edge features of translucent and flexible workpieces is a challenge to locate the upper and lower workpieces spatially, we propose a method for locating translucent and flexible workpieces spatially under the overlapping environment in conjunction with the most common automatic sorting of translucent and flexible workpieces such as infusion tube drip buckets. Firstly, we propose a rectangular surface light source based on 650 nm band and monocular CCD for imaging translucent workpieces such as infusion tube drip buckets and optimize the imaging parameters. Secondly, we study a feature matching recognition algorithm for flexible workpieces that are prone to deformation, construct a mapping relationship between the position of overlapping layers and imaging quality of translucent and flexible workpieces such as infusion tube drip buckets based on clarity and information entropy, and establish The mapping relationship between the position of the overlapping layers and the imaging quality of translucent and flexible workpieces such as infusion tube drip buckets is constructed based on clarity and information entropy, and a local spatial coordinate conversion model is established. Finally, the spatial positioning coordinates of overlapping and non-overlapping translucent and flexible workpieces in the local coordinate system are identified, and the results show that the imaging method and theory can be effectively applied to the identification of overlapping and spatial positioning coordinates in the automatic sorting of translucent workpieces such as infusion tube drip buckets.

Adaptive optimal process control with actor-critic design for energy-efficient batch machining subject to time-varying tool wear

Batch machining systems are essential for improving productivity and quality, but they consume considerable amounts of energy due to the continuous interaction with machine tools, workpieces, and cutting tools. In contrast to single-piece machining that has a short production cycle, the tool wear impacts in batch machining systems on energy consumption cannot be underestimated. However, few studies have focused on adaptive process control subject to time-varying tool wear because process optimization has always been previously considered a static problem. As an alternative to metaheuristic algorithms, reinforcement learning (RL) offers an attractive means for solving such a dynamic, high-dimensional, and high-coupling problem. In the case of turning cylindrical parts, an energy-efficient decision model is developed for the process control of pass operations of batch machining. The decision variables are decoupled by reformulating the problem as the Markov decision process, wherein the tool wear experiences dynamic changes. To solve the problem, an actor-critic RL framework with multi-constraint and multi-objective design is developed. Based on the framework, a dynamic process control method is proposed where the RL agent observes workpiece features, machining requirements, and tool wear states (inputs) and adaptively selects the control parameters such as cutting speed, feed rate, and cutting rate (outputs), with the aim to conserve energy. Two application tests and comparisons against metaheuristic methods are performed. The results indicate that the method can further reduce energy by over 20% compared with energy-efficient optimization ignoring tool wear effects. The learning efficiency of RL is about three times faster than that of metaheuristics. The online sampling time is less than 0.1 millisecond, which facilitates real-time control of process parameters.

Study of structure and properties of Ti — Al — V system alloy blanks produced by electron beam melting method

The regularities for formation and structural features of workpieces made of an Ti—Al—V system alloy obtained by the method of electron beam melting from wire are studied. The mechanical properties of the obtained workpieces at normal and cryogenic temperatures are determined, and possible ways to improve the complex of mechanical properties are proposed. An assessment of the possibility for obtaining a homogeneous defect-free structure during subsequent welding of workpieces is performed.

Reverse identification of dynamic and static motion errors for five-axis machine based on specimen feature decomposition

The dynamic and static error motions dramatically influence the accuracy of five-axis machine tools. We propose a new approach of reverse identification for dynamic and static motion error of five-axis machine tools through specimen cutting and feature decomposition. The error mapping between the workpiece features and the corresponding errors is established, and a feature specimen is designed accordingly. By measuring the specimen with the on-machine measurement and CMM, 15 static motion errors and dynamics-induced errors can be identified and separated. The error estimates are verified by the direct measurement via the interferometer, autocollimator and ballbar tests. These errors are compensated using a volumetric error model, and the specimen accuracy after compensation is effectively increased, which confirms the feasibility and accuracy of this method.

Minimization of Nonproductive Time in Drilling: A New Tool Path Generation Algorithm for Complex Parts

In computerized tool path programming, the operator/user can generate the tool path based on the shape and geometry of the part to be produced by choosing from a set of predefined strategies available in the library of Computer Aided Manufacturing (CAM) software. These tool paths are typically not optimum, specifically for complex geometries. This paper employed Travelling Salesman Problem (TSP) as a foundation to propose a new tool path optimization algorithm for drilling to minimize the tool path length and subsequently reduce the time spent on nonproductive movements. The proposed algorithm was solved using local search approach in the presence of multiple constraints including geometric obstacles and initial location of tool origin. The outcome was a near-optimum tool path for drilling operations with no collision with workpiece features. The computational efficiency of the proposed algorithm was also compared with other methods in available literature using a standard workpiece as a benchmark. The results confirmed that for given examples, the near-optimum collision-free tool paths using the developed model in this paper were almost 50% shorter than the tool path generated by a commercial CAM software.

Sinogram interpretability based CT artefact reduction for multi-material workpieces

ABSTRACT Computed tomography (CT) is a technology for the simultaneous inspection of internal and external features of workpieces. However, beam hardening and scattered radiation artefacts caused by the different material densities and atomic numbers commonly confound CT measurements of multi-material workpieces. Multispectral CT (MSP CT) measurements reduce artefacts by combining multiple CT volumes performed with different x-ray spectra. However, all known simulation-based CT parameter optimisation approaches for industrial applications consider only the single-spectrum case. This paper investigates the CT radon space, focusing on sinogram interpretability. Multispectral CT x-ray spectra can be determined by defining and analysing sinogram areas corresponding to equivalent volume slice artefacts. Validation indicates that the two MSP CT spectra recommended via a proposed CT simulation considering critical sinogram areas cause an artefact reduction. Results are quantified by a standard deviation reduction of measured geometrical features.

A knowledge-based method for tool path planning of large-sized parts

Proper tool paths ensure the high-efficiency and high-quality machining of workpiece. The current tool path planning method is inadequate to meet the complex process requirements for the complex workpiece of abundant local features. Due to the lack of ability of independent process decision-making, the planning effect is still heavily dependent on human experience and the planning is time-consuming and laborious, which is not conducive to shortening development cycle of new products. Therefore, a novel knowledge-based method for tool path planning is proposed in this paper. The proposed method can automatically plan the tool path that meets the requirements of the machining process by the adaptive process decision-making according to the perceived features of complex workpiece. The knowledge extraction method based on the syntactic dependency analysis is used to extract named entities in the process document. And the correctly classified entities are filled in the nodes of knowledge model represented by the form of frame to finish the establishment of process knowledge base. Then, with the help of feature extraction module based on the hybrid clustering method, the semantic segmentation of the point cloud of the workpiece is completed, and the Feature Matrix (FM) is formed. This matrix is used to provide feature information required for process decision, including Feature Vector (FV) and Structure Description Matrix (SDM). Finally, the tool path is generated in the Path Generator. At last, an automatic path planning system is developed for the painting process of automotive exterior. The planned paths are tested for 20 types of bumpers, where the chromatic aberration ΔE of each product is less than 1.4; both the average thickness of each paint and the requirement of production rate are satisfactory. These test results verify the effectiveness of the proposed tool path planning method.

Sinogram interpretability based CT artefact reduction for multi-material workpieces

Computed tomography (CT) is a technology for the simultaneous inspection of internal and external features of workpieces. However, CT measurements of multi-material workpieces are commonly disturbed by beam hardening and scattered radiation artefacts caused by the different material densities and atomic numbers. Multispectral CT (MSP CT) measurements reduce artefacts by combining multiple CT volumes, which are performed with different x-ray spectra. However, all known approaches for simulation-based CT parameter optimisation consider only the single-spectrum case. This paper investigates the CT radon space, focusing on sinogram interpretability. MSP CT x-ray spectra can be determined by defining and analysing sinogram areas corresponding to artefacts in an equivalent volume slice. Validation indicates that a CT simulation considering critical sinogram areas by two different energies causes an artefact reduction, quantified in this work by a standard deviation reduction of measured geometrical features.

Application of mechanistic force models to features of arbitrary geometry at low material removal rate

This paper presents a workpiece discretization method to apply existing cutting force models to predict the forces generated during low material removal rate robotic machining operations of features with arbitrary geometry. Two machining operations along a straight edge which are modelled using this feature discretization method are shown, a chamfer pass on a sharp corner and the removal of a trapezoidal cross section. The workpiece features are measured using a high-resolution laser profile scanner to obtain the volume of the features to be removed. The identified features are discretized into rectangular sections such that the cutting force models can be applied to predict the cutting forces. A linear and an exponential mechanistic model which relate tool immersion and feed rate to the cutting force are applied to the scanned workpiece features. The linear and nonlinear models show good agreement with the measured data, with the exception that the linear model occasionally over predicts the forces depending on the radial depth of cut.

Improved Long Short-Term Memory Network with Multi-Attention for Human Action Flow Evaluation in Workshop

As an indispensable part of workshops, the normalization of workers’ manufacturing processes is an important factor that affects product quality. How to effectively supervise the manufacturing process of workers has always been a difficult problem in intelligent manufacturing. This paper proposes a method for action detection and process evaluation of workers based on a deep learning model. In this method, the human skeleton and workpiece features are separately obtained by the monitoring frame and then input into an action detection network in chronological order. The model uses two inputs to predict frame-by-frame classification results, which are then merged into a continuous action flow, and finally, input into the action flow evaluation network. The network effectively improves the ability to evaluate action flow through the attention mechanism of key actions in the process. The experimental results show that our method can effectively recognize operation actions in workshops, and can evaluate the manufacturing process with 99% accuracy using the experimental verification dataset.

Integrated optimization of process planning and scheduling for reducing carbon emissions

In order to reduce environment impacts of manufacturing processes and fill in research gaps that most previous separated optimization of process planning and scheduling ignored influences of process planning on scheduling, a multi-objective integrated optimization model of process planning and scheduling for reducing carbon emissions in manufacturing processes is proposed. The model aims at minimizing makespan and carbon emissions in manufacturing processes by integrated optimizing machining methods for all machining features of workpieces, machine allocations of processes, process routes and machining sequence of workpieces. Because there are many parameters in the proposed model needed to be optimized and they are interactional, a four segment encoding method is designed and a Non-dominated Sorting Genetic Algorithm II is adopted to solve the proposed model. A case study including three workpieces with twenty-three machining features to be processed by turning, milling, drilling, boring and grinding is used to verify the proposed integrated model and algorithm. Results show that the proposed integrated optimization method can further reduce carbon emissions and makespan in manufacturing processes compared with conventional separated optimization of process planning and scheduling. The proposed integrated optimization method is validated.

Surface Texture of Titanium 31 and H.S.S using Micro EDM Drill and Wire EDM

Micro EDM Drill is variant EDM processes in a situation where it employs water as a dielectric fluid, with pressure flushing and rotating nanotube electrode. This paper brings out the relative features of work pieces i.e., H.S.S and Titanium 31 about their surface roughness with Talysurf, as well as Surfaces Texture with Scanning Electronic Microscope (SEM analysis).Thus the results are evaluated for Ra (Surface Roughness), Rsk (Surface Skewness) and Rku (Surface Kutosis).

Research on a Handheld 3D Laser Scanning System for Measuring Large-Sized Objects.

A handheld 3D laser scanning system is proposed for measuring large-sized objects on site. This system is mainly composed of two CCD cameras and a line laser projector, in which the two CCD cameras constitute a binocular stereo vision system to locate the scanner’s position in the fixed workpiece coordinate system online, meanwhile the left CCD camera and the laser line projector constitute a structured light system to get the laser lines modulated by the workpiece features. The marked points and laser line are both obtained in the coordinate system of the left camera in each moment. To get the workpiece outline, the handheld scanner’s position is evaluated online by matching up the marked points got by the binocular stereo vision system and those in the workpiece coordinate system measured by a TRITOP system beforehand; then the laser line with workpiece’s features got at this moment is transformed into the fixed workpiece coordinate system. Finally, the 3D information composed by the laser lines can be reconstructed in the workpiece coordinate system. A ball arm with two standard balls, which is placed on a glass plate with many marked points randomly stuck on, is measured to test the system accuracy. The distance errors between the two balls are within ±0.05 mm, the radius errors of the two balls are all within ±0.04 mm, the distance errors from the scatter points to the fitted sphere are distributed evenly, within ±0.25 mm, without accumulated errors. Measurement results of two typical workpieces show that the system can measure large-sized objects completely with acceptable accuracy and have the advantage of avoiding some deficiencies, such as sheltering and limited measuring range.

Construction and characterization of micro/nano-topography on titanium alloy formed by micro-milling and anodic oxidation

The surface micro/nano-topographical modifications have been widely used in improving the biocompatibility of biomedical implants. In this paper, the feasibility of the micro-milling process for construction of micro-grooves without burrs on titanium alloy materials was investigated. Low melting point alloy was selected as supporting material to extend the boundary of the workpiece, so the burrs produced on the surface of supporting material. A novel hierarchical micro/nano-topography with micro-grooves and TiO2 nanotubes was fabricated on titanium alloy surface combining micro-milling and anodic oxidation. The scanning electron microscope, energy-dispersive spectroscopy, laser scanning microscope, and contact angle tester were used in characterizing surface features of machined workpieces. Results showed that the micro-grooves with dimension of 40 μm in depth were machined using V-shaped cutting tool, and the TiO2 nanotubes with dimension of 70 nm in diameter were superimposed on the surfaces of micro-grooves by anodic oxidation. In addition, the hydrophilicity of micro/nano-structured surface was significantly enhanced with the water contact angle decreasing from 114.8° to 60.3°. It is concluded that this hybrid method combining micro-milling technology and anodic oxidation can be used in improving the biological activity of biomedical implants through changing surface topographies.

Measurement and algorithm for localization of aspheric lens in magnetorheological finishing

Unlike traditional machinery manufacturing generally using simple features of workpiece as the alignment reference, optical manufacturing, which prioritizes shape tolerances of the machining surface over position tolerances between workpiece geometry features, chooses the machining surface itself as the alignment reference. Thus, the manual alignment of workpiece in optical manufacturing could be very time-consuming when the machining surface is aspheric. Workpiece localization technology is able to automate the workpiece alignment and improve its efficiency. However, there are no relevant reports about application of this technology in the field of optical manufacturing. In this work, the workpiece localization problem for aspheric surface in magnetorheological finishing (MRF) is investigated from aspects of measurement and algorithm. The probe radius error is compensated by applying the polynomial fitting method. A new localization algorithm is proposed in order to improve the computational efficiency for the localization of aspheric surface. Simulation and experiment results verify the validity of the proposed methods. The accurate and efficient workpiece localization in MRF has been achieved and applied in real application which proves its practicability.

Active precision design for complex machine tools: methodology and case study

Machine tool design starts with the determination of performance specifications. Precision of the NC axes is an important aspect of machine tool design. Conventionally, the precision specification of machine tools is empirically determined, resulting in poor designs with insufficient or excessive precision. To provide a cost-effective precision specification for complex machine tools, such as gear cutting machines, an active precision design approach is proposed to generate the specification of the positioning repeatability of NC axes to meet the designated working precision requirements of the machine tools. The methodology consists of error analysis and precision design in four steps: (1) workpiece surface formation modeling in terms of the motion axes and layout of the machine tool, and the generating principle of workpiece features; (2) workpiece machining error modeling based on the workpiece surface formation model by considering kinematic errors of the NC axes of the machine tool; (3) workpiece machining precision modeling via the machining error model; and (4) precision allocation according to the required workpiece precision and the machining error model. The methodology is demonstrated and validated through a case study of precision design for a six-axis CNC spiral bevel gear milling machine.

Qualifying Measuring Systems by Using Six Sigma

The technology of sheet-bulk metal forming enables the production of complex workpieces with filigree surface structures in only a few forming steps. In order to provide a rapid and production-related workpiece inspection of not only large workpiece features, but also small features in an appropriate quality, a multi-sensor optical measurement system with different resolutions is required. Workpiece features of medium size can be measured by two types of fringe projection sensors. With a structured approach according to Six Sigma, which is based on the five phases design, measure, analyze, improve and control complex tasks are divided into smaller individual problems. In each phase the Six Sigma method recommends tools for solving the individual problems effectively. With the support of the Six Sigma guideline an exemplary sheet-bulk metal forming workpiece feature is used in order to qualify the two measuring systems for a production-related measurement. After defining the explicit goal for the investigations, a detailed analysis of the measurement process leads to a couple of relevant influences. These are input factors for the design of experiments. By a full factorial design, not only an influence of a factor itself, also the interactions between multiple factors can be detected. In the analyze-phase, these results are calculated by different statistical methods. To present the results in a comprehensible way several types of diagrams are used. The shown approach gives an example for a traceable and methodical way to qualify a measurement system for challenging measurement tasks.

Interim feature-based cutting parameter optimization for aircraft structural parts

The geometric state of a workpiece is changing dynamically during the machining process. Such geometric changes lead to changes of the rigidity of workpiece features. Knowing the feature rigidity during the machining process is very crucial for the selection of right cutting parameters especially for aircraft structural parts with complex structures. This issue has rarely been addressed in the literature for cutting parameter optimization. In order to address this issue, an interim feature concept is introduced and the interim feature is modeled by using geometric information of final state and machining process information for aircraft structural parts. The interim feature rigidity is evaluated by simplifying the structures of interim feature. By considering the constraints of the interim feature rigidity, machine tool characteristics, and cutting tool performance, the cutting parameters are optimized using a genetic algorithm. A prototype system is developed based on the proposed approach, and a case study is performed. The experiment results show that the machining efficiency can be improved by 26 % after cutting parameter optimization, while the same machining quality is maintained.