This study is focused on dry longitudinal turning of AISI steel using CVD coated cutting inserts. The machining was conducted at different levels of cutting speed, feed, depth of cut, corner radius, ...rake, inclination and approach angles. Surface roughness was measured after each experiment, and statistical analysis was used to derive an empirical, regression model for arithmetical mean surface roughness. The regression model was used to theoretically minimize surface roughness, followed by additional verification experiments. The 95 % confidence interval constructed using ten additional batteries of experiments, contained the theoretically predicted minimum roughness of Ra = 0.238 μm. The mean absolute prediction error of the optimal roughness equals 0.006 μm. The results reveal practical applicability of the developed model.
In this study, the modelling of arithmetical mean roughness after turning of C45 steel was performed. Four parameters of cutting tool geometry were varied, i.e.: corner radius r, approach angle κ, ...rake angle γ and inclination angle λ. After turning, the arithmetical mean roughness Ra was measured. The obtained values of Ra ranged from 0.13 μm to 4.39 μm. The results of the experiments showed that surface roughness improves with increasing corner radius, increasing approach angle, increasing rake angle, and decreasing inclination angle. Based on the experimental results, models were developed to predict the distribution of the arithmetical mean roughness using the response surface method (RSM), Gaussian process regression with two kernel functions, the sequential exponential function (GPR-SE) and Mattern (GPR-Mat), and decision tree regression (DTR). The maximum percentage errors of the developed models were 3.898 %, 1.192 %, 1.364 %, and 0.960 % for DTR, GPR-SE, GPR-Mat, and RSM, respectively. In the worst case, the maximum absolute errors were 0.106 μm, 0.017 μm, 0.019 μm, and 0.011 μm for DTR, GPR-SE, GPR-Mat, and RSM, respectively. The results and the obtained errors show that the developed models can be successfully used for surface roughness prediction.
In this research, an evaluation of the external transverse micro-turning with conventional cutting inserts was performed with a constant cutting force in a dry environment. During machining, the ...number of revolutions, machining time and cutting forces was varied. Before and after machining, the diameter of the workpiece, circularity and the roughness of the machined surface was measured. The obtained results indicate that with increasing number of revolutions, time and cutting force, the cutting depth increases. The results show that this type of machining can achieve very small cutting depths and reduce circularity deviation and roughness of the machined surface. Based on the experimental results, the modelling of the artificial neural network (ANN) was performed which reliably predicted the change in diameter, cylindricity, and roughness after micro-turning operation, with a mean percentage error smaller than 3 %. It can be concluded that the application of ANN is adequate during the machining process with the constant cutting force, since the output parameters can be predicted with small error, while also reducing effort and costs.
Wrong or inadequate design and manufacture of modular fixtures can lead to deformations and displacements of workpiece and fixture-workpiece assembly, as well. Deformations and displacements can ...significantly impact final workpiece accuracy, rendering the fixture less efficient. With that in mind, this paper reviews development of a novel multi-purpose solution for a modular fixture design with higher efficiency, higher accessibility and flexibility. The results of simulations and modelling indicate that the proposed modular fixture design has advantages over the existing, conventional modular fixtures. The proposed framed structure of modular fixtures exhibits versatility in that it allows reliable locating and clamping of workpieces featuring complex geometry and shape. The novel design solution for modular fixtures opens new directions for future investigation, regarding selection and optimization of materials, shape and geometry of fixture elements which can be used to extend and upgrade modular fixtures. All this contributes to higher workpiece quality and accuracy, as well as the higher productivity and lower production costs.
This article gives an account of the machined surface roughness investigation based on the features of a digital image taken subsequent to the technological operation of milling of aluminium alloy ...Al6060. The data used for investigation were obtained by mixed-level factorial design with two replicates. Input variables (factors) are represented by the face milling basic machining parameters: spindle speed, feed per tooth and depth of cut. Output variable or response is the most frequently used surface roughness parameter-arithmetic average of the roughness profile, Ra. Digital image of the machined surface is provided for every test sample. Based on experimental design and obtained results of roughness measuring, a base has been created of input data (features) extracted from digital images of the samples' machined surfaces. This base was later used for generating the fuzzy inference system for prediction of the surface roughness using the adaptive neuro-fuzzy inference system.
The paper presents the new model for eco-design of fixtures based on life cycle and cost assessment. Four fixture types with different mechanical and physical properties as well as different ...manufacturing costs have been evaluated. The life cycle results show that the environmental impact is closely related to the mass of steel needed for fixture manufacture. On the other hand, the fixture with the largest environmental impact had the smallest total fixture cost and vice versa. The results show that it is possible to implement environmental and cost analysis in fixture design process and to enable comparative analysis of fixture constructions by three standpoints, technical, environmental and economic. 34 refs.
Small and start-up companies that need product quality control can usually only afford low-cost systems. The main goal of this investigation was to estimate the influence of high dynamic range images ...as input for the low-cost photogrammetric structure from motion 3D digitization. Various industrial products made of metal or polymer suffer from poor visual texture. To overcome the lack of visual texture and ensure appropriate 3D reconstruction, stochastic image in the form of the light pattern was projected on the product surface. During stochastic pattern projection, a set of low dynamic range and sets of high dynamic range images were captured and processed. In this investigation digital single lens reflex camera that supports five different tone-mapping operators to create high dynamic range images were used. Also, high precision measurements on a coordinate measuring machine are performed in order to verify real product geometry. The obtained results showed that reconstructed polygonal 3D models generated from high dynamic range images in this case study don't have a dominant influence on the accuracy when compared to the polygonal 3D model generated from low dynamic range images. In order to estimate 3D models dimensional accuracy, they were compared using computer-aided inspection analysis. The best achieved standard deviation distance was +0.025 mm for 3D model generated based on high dynamic range images compared to the nominal CAD model.
The presented work discusses the applicability of the selective laser melting technique (SLM) in manufacture of removable partial denture (RPD) frameworks with the emphasis on material properties. ...The paper presents initial results of a conducted test of the mechanical properties of the F75 Co-Cr dental alloy used with selective laser melting.
Personalized bone grafts are one of the best examples of the latest achievements in the biomedical engineering. In the area of maxillofacial bone tissue reconstruction or jaw bone augmentation, their ...application has for some time been on the rise, and its ever increasing significance is driven by the growing technical support. One of the key segments is the bone graft modelling customized to suit patient's specific needs, since it greatly determines not only the future anatomic functionality but also the acceptance probability of the graft by the bone tissue. With the graft geometry importance in mind, presented in this paper is an approach to personalized bone graft modelling. The approach is based on application of modern computer-aided systems and methods, and enables efficient geometric design while minimizing the risk of errors during modelling and placement stages. Verification is based on a case study of a personalized bone graft designed for a patient requiring mandible augmentation.
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