Surface Quality Evolution Model and Consistency Control Method of Large Shaft Multi-Pass Grinding

The quality of machined surface is of vital significance due to its bearing of the in-service functionality of the component. In-service functionality of the machined parts like, tribological performance, fatigue life of the component etc.; are greatly dependent on the surface profile characteristic and the surface roughness generated after machining. However, the quality of surface is reliant on complexities of the numerous process parameters. The mechanics of metal cutting necessarily results into the dynamic instability of the process consequentially ensuing into cutting tool vibrations. Previous research indicates an association between the cutting tool vibrations and surface roughness. In this study the cutting tool vibrations in tangential and axial direction have been integrated with the input parameters; cutting speed, feed rate, depth of cut, work material hardness and tool edge geometry to develop prediction models for surface roughness from the experimentally obtained data by using Regression Analysis and artificial Neural Network methodologies. The results of the regression models and neural networks model are compared. A good agreement between the experimental and predicted values for both the models is seen, however neural networks approach outclasses regression analysis by a reasonable margin. Further it is also noted that the quality of surface is markedly influenced by the tool edge geometry and feed rate.

Influence of overlap between the laser beam tracks on surface quality in laser polishing of AISI H13 tool steel

Product quality assurance is a very important concern in industries these days. Surface quality is an important constituent of overall product quality in ceramic grinding. Surface roughness is one of the major quality attributes of a ground product and hence it is used to determine and evaluate the quality of the product. Although the surface roughness evaluation has been standardized, establishment of a model for reliable prediction of surface roughness is still a key issue. This paper presents a new analytical model for the prediction of arithmetic mean surface roughness based on the stochastic nature of grinding process, governed mainly by the random geometry of grain and random distribution of cutting edges on the wheel surface having random grain protrusion heights. A simple relationship between the surface roughness and the chip thickness was obtained, which was validated by the experimental results of silicon carbide grinding.

One novel method to improve surface quality in incremental sheet forming

Due to its high precision, productivity, and surface quality, computer numerical control turning (CNC) is a desirable processing tool in the traditional processing area. CNC machining procedures have a huge number of process parameters, making it challenging to find the best combination of parameters for increased accuracy. In this research work, the Taguchi method and ANOVA were used to study the effects of CNC machining parameters in EN8 steel turning: Surface roughness (Ra) value of component affected due to cutting speed, depth of cut and feed rate. Three-level three-parameter experimental design, using Minitab 17 software using L9 orthogonal array, using coated carbide insert cutting tools, using signal-to-noise ratio (S/N) to study the performance characteristics of EN8 steel turning. In this study, statistical approaches such as the signal-to-noise ratio (S/N ratio) and analysis of variance (ANOVA) were used to explore the effects of cutting speed, depth of cut, and feed rate on surface roughness. Nature-inspired algorithms play a vital role in solving real life. In this study, the bat algorithm can be used to predict the optimal surface value (Ra) and process parameters. Verify the results by conducting confirmation experiments. The current research shows that the feed rate is the most important factor affecting the surface roughness (Ra) of EN8 steel turning.

Magnetorheological fluid-assisted finishing (MFAF) process is an advanced finishing process that produces a high level of surface quality, even on complex freeform surfaces. Although the development of the MFAF process was started in the late 1990s, the evolution process is still in progress to increase process effectiveness and efficiency. With the assistance of Magnetorheological (MR) fluid consisting of ferromagnetic particles, abrasives, carrier fluid, and stabilizers, the unbonded multipoint cutting tool is generated during the finishing operations to achieve surface roughness (Ra) in the nanometer range. Hence, the MFAF process produces fine surface quality without affecting the component's surface topography. In the MFAF process, the properties of MR fluid can be controlled in-situ, with the help of an external magnetic field, according to the process's necessity. Apart from the magnetic field, several other process parameters, like finishing time, working distance, MR fluid constituent composition, and concentration are vital for determining the workpiece's surface quality during the MFAF process. Optimizing such critical process parameters requires an explicit computational model to describe the finished component's surface characteristics. This chapter aims to analyze various computational techniques and theoretical models applied to the MFAF process to obtain a surface roughness as a function of multiple process parameters that influence the finishing performance.

The tool position deviation will affect the surface quality, especially for ultra high strength and high hardness materials, such as aircraft landing gear, engine impeller of high strength and high hardness of the hard machining aviation parts. The 300M steel is a kind of low alloy medium carbon martensitic reinforced super high strength steel, which is a common material for aircraft landing gear workpiece. The material has a strength of 1900-2100 MPa and hardness of 50 HRC after heat treatment. This material is a typical difficult-to-machine material and can produce large cutting resistance in cutting, resulting in larger tool deviation and vibration, and affects the surface quality of workpiece. Aiming at its complex processing technology, it is particularly important to study the relationship between the surface quality and the process parameters in multi axis milling. The tool orientation, which is represented by the lead angle and the tilt angle of the tool, is representative and influential process parameter. Therefore, experimental study is conducted to reveal the relationship. The surface roughness of the workpiece after multi axis NC machining is measured accurately. The effect of tool orientation on surface roughness is analysed based on the experiment data. The result shows that it is of great significance to optimize the tool orientation, which is represented by the lead angle and the tilt angle of the tool, to improve surface quality.

Surface roughness is a crucial factor affecting the surface quality of workpieces in manufacturing industries. Thus, it is important to provide an accurate performance of surface roughness prediction and optimal parameters to reduce the burden of time and costs during the process. In this study, two predict models, namely multiple linear regression and deep belief network (DBN) models, were performed to accurately predict change in surface roughness in the rotational electromagnetic finishing (REMF). Compared to the statistical-based model, the data-driven model based on the DBN architecture was a significantly considerable effect on surface roughness prediction in the REMF process. Among the considered DBN models, DBN5 architecture as [7, 14, 14, 1] showed effective features of the nonlinear relationship between process parameters and response with the highest determination coefficient (R2) of 0.9340 and the lowest mean squared error (MSE) of 1.3037 \(\times\) 10−3 in the testing datasets. In addition, a genetic algorithm (GA) as a heuristic optimization technique was adopted to optimize the input parameters of the best derived DBN model. It showed that the maximum change in surface roughness was 0.530 at particle length of 3 mm, particle diameter of 0.7 mm, particle weight of 1.3 kg, liquid water quantity of 1.0 l, a rotational speed of 1323 rpm, working time of 35 min, and initial surface roughness of 2.5478 m\(\upmu\).

Incremental sheet forming (ISF) is a versatile and innovative sheet metal forming technology which can readily produce asymmetrical, symmetrical, and complex geometric parts using a simple machine tool. However, limitations of the process include surface roughness, formability, and process throughput. Surface finish influences the quality, functionality, and aesthetics of the formed parts. Thus, ISF should be able to achieve an obligatory level of surface quality to be considered a competitive solution relative to other manufacturing processes. A variety of forming parameters have been investigated as primary causes of surface roughness variation in ISF. As a result, several forming parameters have been reported to be more influential to surface roughness than others. Further, surface roughness may differ based on a combination of parameter values while forming a workpiece rather than a single parameter alone. These influential forming parameters also have differing effects on surface roughness based on the workpiece material and the final part geometry. In addition, ISF can be divided into various categories, such as single-point incremental sheet forming (SPIF), two-point incremental sheet forming (TPIF) and multi-stage incremental sheet forming. The same set of forming parameters applied to these processes can yield a range of surface roughness values. Researchers have attempted different approaches to measure, analyze, and optimize the parameters that influence surface quality in ISF processes. However, the findings of these studies are sometimes contradictory. Most approaches consider a few forming parameters as variables in order to sort out the relationships between surface roughness and the selected forming parameters, while keeping other parameters constant. The forming parameters considered by prior research also vary from approach to approach, depending on researcher preferences and their perspective of influential ISF parameters on surface roughness. As the research of measuring, analyzing, and optimizing forming parameters to obtain desired surface quality is in the early stages of development, strong evidence on which researchers can agree is limited and lacks coherence. No prior reports summarizing the approaches used by researchers have been found in the literature, nor have comparative analyses been conducted to evaluate efficacy. A synthesis of the extant literature is provided herein to highlight the various approaches and practices that have been employed to study the effect of ISF process parameters on surface roughness. With the help of previous research findings, the outcomes reveal how various approaches compare and differ in terms of the identified influential forming parameters and offer potential directions for future study.

Investigation of laser and process parameters for Selective Laser Erosion

The aim of this article is to understand the surface quality, as well as the emission and dispersion of dust particles generated during dry polishing of granite, with a view to minimizing both surface roughness and dust particles emission by optimizing the parameters of the industrial process. The optimized polishing parameters found must have a good influence on both the surface quality and the dust particles emission. Investigating dust particle dispersion will help with the design of ventilation solutions to capture dust during the polishing process. Results show that the spindle speed and the feed rate of the polishing tool have important influence on the surface quality and the dust particles emission during the dry polishing process. Choosing a spindle speed of approximately 1500 rpm and an average feed rate of 17 mm/s to perform the dry polishing of granite is a good compromise, which leads to minimize dust particles emission while achieving a good surface finish. It is also shown that the dust emitted contaminates in the same way the vicinity and near the polishing tool. The dust particles, emitted during dry polishing of granite, belong to PM2.5 (particles less than 2.5 μm in diameter). These particles, once inhaled, easily reach the lung alveolar region and cause serious injuries.

A theoretical model of surface roughness prediction for high-speed machining Zr-based bulk metallic glasses was established. The roughness components of the prediction model, including plastic side flow, material recovery, cutting vibration and kinematics were discussed. The cutting forces and machined surface morphology were studied to characterize the cutting vibration and plastic side flow, respectively. Specially, the cutting vibration during machining was characterized with radial force amplitude. The surface morphology of the Zr-based bulk metallic glass (BMG) was mainly characterized with plastic flow traces, molten droplets, dimples, adhensions and ploughing marks. The presence of material recovery would effectively improve the surface quality and, on the contrary, the presence of plastic side flow would deteriorate it. Finally, the surface roughness prediction model was validated with turning experiments under varying tool geometric parameters, and the calculated surface roughness showed good agreement with the measured results. The present results are beneficial to revealing the underlying mechanisms for machined surface roughness, and can provide basis for optimizing cutting parameters and surface quality for industry.

The aim of this work is to develop a novel approach for ensuring the quality of the finished surface based on a multi-factor model. The proposed model can take into account most of the machining process parameters. The main parameters include cutting conditions, dynamic stability of the cutting process, thermal effects in the cutting area. The development of a multi-factor model was based on a literature review and experimental data obtained from the cutting force analysis and colour pyrometry. The data obtained were summarised into a unified multi-factor model. We analysed the key literature sources and summarised the experimental data and findings to assure finished surface quality when controlling one of the input parameters of the machining process. It was shown that the surface quality (roughness) can be achieved by applying different machining parameters. They include the rational cutting conditions, a change in the geo m-etry of a cutting tool, reducing the relative spacial dynamic vibrations of the tool relative to the working surface of the raw part, using the methods influencing the physical and mechanical properties of the processed materials. It was established that the process dynamic stability, the cutting conditions or the chip formation process can be used as an input parameter. The proposed scheme of multi-factor influence of processing parameters on the output parameter - the surface roughness - applies to any materials under processing. The created model takes into account all input parameters of mechanical processing. It aims at the quality management of the finished surface based on the required performance characteristics of the items. Based on the proposed multi-factor scheme, we plan to create an adaptive system capable of controlling the mechanical processing based on a computer numeric control machining centre.

Chemical mechanical polishing (CMP) is a critical technique for fabricating ultra-smooth and high-quality surfaces of single crystal diamond (SCD), where processing parameters profoundly influence polishing performance. To achieve superior diamond surface finishes, this study first investigates the effects of key process parameters, including oxidant concentration, catalyst type, and abrasive particle size, on surface quality through single-factor experiments. Subsequently, an Archimedes optimization algorithm (AOA)-based prediction model for diamond CMP surface roughness (Sa) is developed and validated experimentally. Results reveal that high-concentration oxidants, fine-particle abrasives, and dual-catalyst polishing systems synergistically enhance surface quality. The AOA-based prediction model demonstrates a root-mean-square error (RMSE) of 0.006 and a correlation coefficient (R) of 0.98 between the predicted and experimental Sa values. Under the conditions of a dual-catalyst type, 35% oxidant concentration, and 500 nm abrasive particle size, the model predicts a surface roughness of 0.128 nm, with an experimental value of 0.125 nm and a relative error of less than 3%. These findings highlight the capability of the model to accurately forecast surface roughness across diverse process parameters, offering a novel predictive framework for precision CMP of SCD.

PurposeSurface roughness is an important evaluation index for industrial components, and it strongly depends on the processing parameters for selective laser molten Ti6Al4V parts. This paper aims to obtain an optimum selective laser melting (SLM) parameter set to improve the surface roughness of Ti6Al4V samples.Design/methodology/approachA response surface methodology (RSM)-based approach is proposed to improve the surface quality of selective laser molten Ti6Al4V parts and understand the relationship between the SLM process parameters and the surface roughness. The main SLM parameters (i.e. laser power, scan speed and hatch spacing) are optimized, and Ti6Al4V parts are manufactured by the SLM technology with no post processes.FindingsOptimum process parameters were obtained using the RSM method to minimise the roughness of the top and vertical side surfaces. Obtained parameter sets were evaluated based on their productivity and surface quality performance. The validation tests have been performed, and the results verified the effectivity of the proposed technique. It was also shown that the top and vertical sides must be handled together to obtain better top surface quality.Practical implicationsThe obtained optimum SLM parameter set can be used in the manufacturing of Ti6Al4V components with high surface roughness requirement.Originality/valueRSM is used to analyse and determine the optimal combination of SLM parameters with the aim of improving the surface roughness quality of Ti6Al4V components, for the first time in the literature. Also, this is the first study which aims to simultaneously optimise the surface quality of top and vertical sides of titanium alloys.