An investigation of stored energy in uniaxial and biaxial directional rolling on mechanical properties and microstructure of pure copper

Proceedings of the Institution of Mechanical Engineers, Part C:

2022

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Most sectors are moving towards downsizing products that retain high standards of quality and satisfy customer needs. Miniaturisation of components typically makes use of scaling rules. In this work, we use the idea of similarity theory to investigate the micro-and nano-scale thinning behaviours of multi-stage deep-drawn cups. Deep drawing with a limiting draw ratio (LDR) of 1.69 was used to shape ETP copper sheets into cylindrical cups via annealing and directionally rolling. Each rolling pass consists of nine incremental passes that result in a 50% reduction in thickness. The influence of scaling laws for uni and bi-directional rolling was studied, and scaled dies and punch sets were employed to create scaled cups of five different thicknesses, each 50% of the original 6 mm. For this simulation, we used Altair INSPIRE, a sort of explicit finite element analysis that uses real-world material test data. To investigate the impact of thickness variation across all scaled cups, measurements of the thickness were taken at three points on multi-stage deep-drawn cups. Bi-directional rolling produced cups with less thickness variance than uni-directional rolling. All the scaled cups had a consistency greater than the mean at the lip point and less at the cup base for both ways of rolling, it was also noticed. Physical evaluations of the drawn cups for extreme values verified that the inferred variations lie within ±15% of their original thickness for all cups, and the mean thickness likewise has 50% of its multi-stage-drawing, proving the applicability of the scaling rules.

Section: Mechanical Propertiesmentioning

confidence: 62%

Section: Mechanical Propertiesmentioning

confidence: 99%

An investigation on the size-effects on thickness-distribution of directionally rolled multi-stage deep-drawn copper cups through finite element analysis, and its validation through experimentation employed scaling law

Proceedings of the Institution of Mechanical Engineers, Part C:

2022

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“…A carefully selected heat treatment at 150°C can relieve stress without recrystallization. Mechanical properties [17][18][19][20][21] and anisotropy [17][18][19][20][21] are obtained by uniaxial tensile test according to ASTM E8-the lower dimensional standard, and the forming limit diagram of this test passes the Nakajima test. 17 The schematic diagram of the employed rolling process is shown in Figure 1(a) and (b) depicts the route sequence.…”

Section: Methodsmentioning

confidence: 99%

Artificial Neural Network prediction of forming limit diagram for directionally-rolled, size scaled copper strips

Harshavardhana

Proceedings of the Institution of Mechanical Engineers, Part C:

2023

Estimating the forming limit diagram (FLD) is tedious and cost-intensive. Methods driven by data and artificial intelligence are used to determine the relationship between scaled thickness and the forming rates of various cups drawn out of ETP copper sheets. Machine learning (ML) techniques have a good chance of predicting the FLD of copper alloys, and they are being used increasingly in sensitive electronic and structural applications. The current research aims to create ML-based artificial neural network (ANN) tools to model the relationship between scaled thickness and forming rates as a function of formability. The FLD is measured for copper strips of initial dimensions of 1500 mm long, 750 mm wide, and 6 mm thick, whose thickness was reduced successively by 50% in nine incremental steps. Thus, 3, 1.5, 0.75, 0.38, and 0.19 mm sheets were obtained and used to determine FLD through the Nakajima approach. An FEA model of the drawing was made in Altair Inspire Form, and the simulation results were used to train a two-step ML. A Bayesian regularization (BR) and Levenberg-Marquardt algorithm (LM) were used in the first step to predict strains’ maximum and minimum points. In the second step, the minor strains predicted in the first step are used as inputs. Using the same feature set, the BR and LM algorithms predict the major strain, showing a linear trend until the middle and then a nonlinear trend. The trained ML model was used to predict unknown intermediate values for estimating the over-learning and over-fitting problems here for 2 and 0.25 mm thick sheets and are validated experimentally. The variation between the FLDs of predicted and experimentally verified data falls between 2% and 5%. Such small changes in the FLD values show that the proposed ML model could be used to predict the FLDs of copper strips.

“…Every succeeding pass exposes the sample to 6% strain, totaling 3.45%, with a final thickness of 0.18 mm. 19…”

Section: Methodsmentioning

confidence: 99%

A study on deep drawn cups and the selection of optimal settings deploying ANN training and architectural parameters using the Taguchi ARAS approach

Proceedings of the Institution of Mechanical Engineers, Part C:

2022

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In recent years, the use of artificial neural networks (ANN) for modeling and optimizing metal forming processes has gained popularity. Numerous benefits are provided by ANNs, which can only be attained by constructing a high-performance ANN model. However, selecting an ANN’s appropriate training and architectural parameters remains challenging. Typically, these parameters are chosen using a trial-and-error approach in which many ANN models are produced and evaluated. This article describes the use of the Taguchi method for optimizing an ANN model trained on architectural parameters by the Levenberg–Marquardt algorithm and the additive ratio assessment method for selecting the optimized parameter. The method’s operation is demonstrated using the Altair Inspire form, the Forming Limit Diagram, and the Maximum Thinning Rate. The ANN model was trained using the orthogonal array, and its prediction performance was compared to the experimental validation. Analysis of variance assisted in determining whether the model was significant. The analysis of means helped us in deciding the appropriate ANN level for each parameter. Taguchi orthogonal array, ANN optimized for additive ratio assessment method. This model was developed and was found to be the most accurate. Through learning and testing, it was demonstrated that a systematic method for selecting the optimal ANN training and architecture parameters saves time and money by avoiding trial and error. The Taguchi-based additive ratio assessment method algorithm can then be used to determine the ideal processing parameters.