scholarly journals Prediction of Process Parameters That Affecting on Surface Roughness in Multi-Point Forming Process Using ANOVA Algorithm

2019 ◽  
Vol 15 (2) ◽  
pp. 70-79
Author(s):  
Tahseen Fadhil Abbas ◽  
Karem Mohsen Younis ◽  
Khalida Kadhim Mansor
Keyword(s):  
Surface Roughness ◽  
Product Quality ◽  
Prediction Models ◽  
Model Parameters ◽  
Forming Process ◽  
Blank Holder ◽  
Response Parameter ◽  
Optimum Parameters ◽  
Multipoint Forming ◽  
Reconfigurable Die

  Multipoint forming process is an engineering concept which means that the working surface of the punch and die is produced as hemispherical ends of individual active elements (called pins), where each pin can be independently, vertically displaced using a geometrically reconfigurable die. Several different products can be made without changing tools saved precious production time. Also, the manufacturing of very expensive rigid dies is reduced, and a lot of expenses are saved. But the most important aspects of using such types of equipment are the flexibility of the tooling. This paper presents an experimental investigation of the effect of three main parameters which are blank holder, rubber thickness and forming speed that affect the surface integrity for brass (Cu Zn 65-35) with 0.71 mm thickness. This paper focuses on the development of prediction models for estimation of the product quality. Using Analysis of Variance (ANOVA), surface roughness has been modeled. In the development of this predictive model, blank holder, rubber thickness and forming speed have been considered as model parameters. The mean surface roughness (Ra) is used as response parameter to predict the surface roughness of multipoint forming parts. The data required has been generated, compared and evaluated to the proposed models obtained from experiments. Taguchi algorithm was used to predict the forming parameters (blank holder, rubber thickness and forming speed) on product roughness in forming process of Brass (Cu Zn 65-35) based on orthogonal array of L9 and finally ANOVA was used to find the optimum parameters that have effect on the product quality.

scholarly journals PREDICTION AND ENHANCEMENT OF THICKNESS REDUCTION IN MULTI-POINT FORMING PROCESS USING ANOVA ALGORITHM

2019 ◽  
Vol 19 (1) ◽  
pp. 92-104
Author(s):  
Tahseen F Abaas ◽  
Karem M Younis ◽  
Khalida K Mansor
Keyword(s):  
Analysis Of Variance ◽  
Thickness Reduction ◽  
Model Parameters ◽  
Forming Process ◽  
Minimum Thickness ◽  
Blank Holder ◽  
Response Parameter ◽  
Multipoint Forming ◽  
Thickness Deviation ◽  
Reconfigurable Die

Multipoint forming is an engineering concept which means that the working surface of the dieand/or punch is made up of hemispherical ends of individual active elements (called pins), whereeach pin can be independently, vertically displaced using a geometrically reconfigurable die,precious production time is saved because several different products can be made withoutchanging tools. The aim of this work is to present the effect of many parameters (blank Holdertypes, rubber thickness and forming speed) on the reduction of thickness for brass with 0.71 mmthickness. This research is concentrate on the development of predictive models to estimate theminimum deviation in thickness using analysis of variance (ANOVA), minimum thicknessdeviation has been modeled. In the development of this predictive model, blank holder, rubberthickness and forming speed have been considered as model parameters. Arithmetic theminimum thickness deviation used as response parameter to assess the thickness reduction ofMultipoint forming parts. The data required has been generated, compared and evaluated to theproposed models that obtained from experiments. Taguchi algorithm is used to predict theeffect of forming parameters on thickness reduction in forming process of Brass (65-35) basedon orthogonal array of L9. The analysis of variance was used to find the best factors that effecton the thickness deviation, The result of this research is the contribution of blank holder types,rubber thickness and forming speed with respect to minimum thickness deviation is (69.195,18.1 and 12.733) % respectively.

Research on Stamping of Aluminum Alloy Hemispherical Components Based on Numerical Simulation and Experiments

2010 ◽  
Vol 97-101 ◽  
pp. 236-239
Author(s):  
Cheng Jun Han ◽  
Xin Bo Lin ◽  
Yan Bo Li
Keyword(s):  
Numerical Simulation ◽  
Aluminum Alloy ◽  
Product Quality ◽  
Experimental Research ◽  
Simulation Software ◽  
Blank Holder Force ◽  
Forming Process ◽  
Blank Holder ◽  
Wrought Aluminum

Experimental research on stamping of wrought aluminum alloy has been an important issue at home and abroad. In this paper, taking stamping of aluminum alloy hemispherical components for example, the effects of blank holder force (BHF) on stamping forming process of aluminum alloy are explored by methods of experiments and numerical simulation. Through experiments, the forming laws of hemispherical components are found out. The research shows that the BHF has significant effects on the quality of stamping components and reasonable BHF can greatly improve the formability of hemispherical components. Additionally, by applying simulation software in stamping, the development circle of product and its moulds can be shortened, and product quality and its competitiveness in the market can be improved.

Prediction of workpiece surface roughness using soft computing

2008 ◽  
Vol 222 (10) ◽  
pp. 1221-1232 ◽  
Author(s):  
B Samanta ◽  
W Erevelles ◽  
Y Omurtag
Keyword(s):  
Surface Roughness ◽  
Soft Computing ◽  
Prediction Models ◽  
End Milling ◽  
Depth Of Cut ◽  
Model Parameters ◽  
Machining Parameters ◽  
Validation Data ◽  
Inference System ◽  
Workpiece Surface

A study is presented to model surface roughness in end-milling using soft computing (SC) or computational intelligence (CI) techniques. The techniques include the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). ANFIS combines the learning capability of ANN and the effective handling of imprecise information in fuzzy logic. Prediction models based on multivariate regression analysis (MRA) are also presented for comparison. The machining parameters, namely, the spindle speed, feed rate, and depth of cut, were used as inputs to model the workpiece surface roughness. The model parameters were tuned using the training data maximizing the modelling accuracy. The trained models were tested using the set of validation data. The effects of different machining parameters, number, and type of model parameters on the prediction accuracy were studied. The procedure is illustrated using the experimental data of end-milling 6061 aluminium alloy. Although statistically all three models predicted roughness with satisfactory goodness of fit, the test performance of ANFIS was better than ANN and MRA. In comparison with MRA, the performance of ANN was better in training but similar in test. The results show the effectiveness of CI techniques in modelling surface roughness.

scholarly journals Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model

2021 ◽  
Vol 11 (11) ◽  
pp. 5011
Author(s):  
Yuanxing Huang ◽  
Zhiyuan Lu ◽  
Wei Dai ◽  
Weifang Zhang ◽  
Bin Wang
Keyword(s):  
Surface Roughness ◽  
Failure Criterion ◽  
Cutting Tool ◽  
Cutting Tools ◽  
Remaining Useful Life ◽  
Model Parameters ◽  
Inverse Gaussian ◽  
Wear Process ◽  
Useful Life ◽  
Dynamic Wear

In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision. To effectively schedule production and maintenance actions, it is vital to model the wear process of cutting tools and predict their remaining useful life (RUL). However, it is difficult to determine the RUL of cutting tools with cutting precision as a failure criterion, as cutting precision is not directly measurable. This paper proposed a RUL prediction method for a cutting tool, developed based on a degradation model, with the roughness of the cutting surface as a failure criterion. The surface roughness was linked to the wearing process of a cutting tool through a random threshold, and accounts for the impact of the dynamic working environment and variable materials of working pieces. The wear process is modeled using a random-effects inverse Gaussian (IG) process. The degradation rate is assumed to be unit-specific, considering the dynamic wear mechanism and a heterogeneous population. To adaptively update the model parameters for online RUL prediction, an expectation–maximization (EM) algorithm has been developed. The proposed method is illustrated using an example study. The experiments were performed on specimens of 7109 aluminum alloy by milling in the normalized state. The results reveal that the proposed method effectively evaluates the RUL of cutting tools according to the specified surface roughness, therefore improving cutting quality and efficiency.

Preparation of Semisolid A356 Alloy by a Cooling Slope Processing

2011 ◽  
Vol 675-677 ◽  
pp. 767-770 ◽  
Author(s):  
Jun Xu ◽  
Tong Min Wang ◽  
Zong Ning Chen ◽  
Jing Zhu ◽  
Zhi Qiang Cao ◽  
...  
Keyword(s):  
A356 Alloy ◽  
A356 Aluminum Alloy ◽  
Forming Process ◽  
Pouring Temperature ◽  
Cooling Slope ◽  
Optimum Parameters ◽  
Grain Structures ◽  
Semisolid Forming ◽  
Size And Morphology ◽  
A356 Aluminum

In order to obtain the non-dendritic feedstock for the semisolid forming process, a cooling slope processing was used. In this work, the effects of the angle, length of cooling slope and pouring temperature on the microstructure of A356 aluminum alloy were investigated. It showed that these parameters affect the size and morphology of α-Al phase to some extent. The results indicate that a pouring temperature of 650°C and a cooling slope with 45° in angle and 50 cm in length are the optimum parameters for preparing fine and globular grain structures. To eliminate the solidification shell formed in the surface of cooling slope, a nitride dope was coated on the surface of the cooling slope.

Machine Learning Acoustic Emission Based Monitoring of Cold Forging for Smart Manufacturing: A Review

2021 ◽  
Vol 2 (3) ◽  
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Product Quality ◽  
High Speed ◽  
High Rate ◽  
Cold Forging ◽  
Smart Manufacturing ◽  
Forming Process ◽  
Cold Forming ◽  
Effective Manner

Cold forging is a high-speed forming technique used to shape metals at near room temperature. and it allows high-rate production of high strength metal-based products in a consistent and cost-effective manner. However, cold forming processes are characterized by complex material deformation dynamics which makes product quality control difficult to achieve. There is no well defined mathematical model that governs the interactions between a cold forming process, material properties, and final product quality. The goal of this work is to provide a review for the state of research in the field of using acoustic emission (AE) technology in monitoring cold forging process. The integration of AE with machine learning (ML) algorithms to monitor the quality is also reviewed and discussed. It is realized that this promising technology didn’t receive the deserving attention for its implementation in cold forging and that more work is needed.

Estimation of Internal Surface Roughness of Additively Manufactured Components Under Complex Conditions Using Artificial Intelligence and Measurements of Ultrasonic Backscatter

2021 ◽  
Author(s):  
Mohamed Subair Syed Akbar Ali ◽  
Mato Pavlovic ◽  
Prabhu Rajagopal
Keyword(s):  
Artificial Intelligence ◽  
Surface Roughness ◽  
Internal Surface ◽  
Side Surface ◽  
Ultrasonic Waves ◽  
Model Parameters ◽  
Ultrasonic Measurements ◽  
Neural Network Algorithm ◽  
X Ray Computed ◽  
Pulse Echo

Abstract Additive Manufacturing (AM) is increasingly being considered for fabrication of components with complex geometries in various industries such as aerospace and healthcare. Control of surface roughness of components is thus a crucial aspect for more widespread adoption of AM techniques. However, estimating the internal (or ‘far-side’) surface roughness of components is a challenge, and often requires sophisticated techniques such as X-ray computed tomography, which are difficult to implement online. Although ultrasound could potentially offer a solution, grain noise and inspection surface conditions complicate the process. This paper studies the feasibility of using Artificial Intelligence (AI) in conjunction with ultrasonic measurements for rapid estimation of internal surface roughness in AM components, using numerical simulations. In the first models reported here, a pulse-echo configuration is assumed, whereby a specimen sample with rough surfaces is insonified with bulk ultrasonic waves and the backscatter is used to generate A-scans. Simulations are carried out for various combinations of the model parameters, yielding a large number of such A-scans. A neural network algorithm is then created and trained on a subset of the datasets so generated using simulations, and later used to predict the roughness from the rest. The results demonstrate the immense potential of this approach in inspection automation for rapid roughness assessments in AM components, based on ultrasonic measurements.

Modeling and Analysis of Surface Roughness with Statistical and Soft Computing Approach

2021 ◽  
Vol 106 ◽  
pp. 109-115
Author(s):  
L.B. Abhang ◽  
M. Hameedullah
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Prediction Models ◽  
Tool Geometry ◽  
Modeling And Analysis ◽  
Empirical Prediction ◽  
Lathe Machine ◽  
Carbide Inserts ◽  
Computing Approach

The objective of this study focuses on developing empirical prediction models using response regression analysis and fuzzy-logic. These models latter can be used to predict surface roughness according to technological variables. The values of surface roughness produced by these models are compared with experimental results. Experimental investigation has been carried out by using scientific composite factorial design on precision lathe machine with tungsten carbide inserts. Surface roughness measured at end of each experimental trial (three times), to get the effect of machining conditions and tool geometry on the surface finish values. Research showed that soft computing fuzzy logic model developed produces smaller error and has satisfactory results as compared to response regression model during machining.

scholarly journals Predicting Corporate Financial Sustainability Using Novel Business Analytics

2018 ◽  
Vol 11 (1) ◽  
pp. 64 ◽  
Author(s):  
Kyoung-jae Kim ◽  
Kichun Lee ◽  
Hyunchul Ahn
Keyword(s):  
Financial Distress ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Support Vector ◽  
Model Parameters ◽  
Financial Sustainability ◽  
Business Analytics ◽  
Financial Distress Prediction ◽  
Proposed Model ◽  
Distress Prediction

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

Applying 2k Factorial Design to Study on Parameters Affecting Springback of Forming of Advanced High Strength Steel Sheets (AHSS)

2017 ◽  
Vol 872 ◽  
pp. 83-88
Author(s):  
Ramil Kesvarakul ◽  
Chamaporn Chianrabutra ◽  
Watcharapong Sirigool
Keyword(s):  
Elastic Recovery ◽  
Weight Ratio ◽  
High Strength ◽  
Die Design ◽  
Statistical Experimental Design ◽  
Blank Holder Force ◽  
Forming Process ◽  
Blank Holder ◽  
Punch Radius ◽  
Springback Angle

Advanced high strength steels (AHSS) are widely used in the automotive industry due to their appropriate strength to weight ratio. This alloy has unique hardening behavior and variable unloading elastic modulus; however, the unavoidable obstacle of AHSS sheet metal forming is springback. The springback is a result of elastic recovery and residual stress. The aim of this study is to determine the proper process parameters enabling the reduction of the springback defects in AHSS forming process. This work was divided into two parts, regarding to the effects of numerical parameters and process parameter on forming AHSS. In this paper, a U-shape forming was used to examine the springback behaviors, such as springback angle, sidewall curl, and thickness, through an experiment. To achieve this purpose, 2k factorial statistical experimental design has been employed to investigate the parameters affecting the springback of forming in AHSS to find out the main effect in the springback reduction focusing on using as a guideline for die design. It showed that the blank holder force is the most influential parameter. The second is the punch radius. However, the blank holder force and punch radius is not simple to adjust in die design, the die radius becomes the important parameter to be used to reduce the springback angle.

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