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J. Manuf. Mater. Process., Volume 3, Issue 3 (September 2019)

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Open AccessArticle
Bayesian Optimized Deep Convolutional Network for Electrochemical Drilling Process
J. Manuf. Mater. Process. 2019, 3(3), 57; https://doi.org/10.3390/jmmp3030057
Received: 11 June 2019 / Revised: 9 July 2019 / Accepted: 11 July 2019 / Published: 14 July 2019
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Abstract
Electrochemical machining is a promising non-traditional manufacturing process to make high-quality parts. The benefits of minimal thermally and mechanically induced stresses, free of burr, and a low surface roughness are appealing for industry and research institutes. However, the combined chemical reaction, electric field, [...] Read more.
Electrochemical machining is a promising non-traditional manufacturing process to make high-quality parts. The benefits of minimal thermally and mechanically induced stresses, free of burr, and a low surface roughness are appealing for industry and research institutes. However, the combined chemical reaction, electric field, fluid mechanics, and material properties involve a significant number of independent parameters which are difficult to analyze in order to draw comprehensive conclusions. To our current knowledge, process responses such as the material removal rate, optimal feed rate, and cutting profile cannot be represented accurately by analytical solutions. In recent years, deep learning has had tremendous success in analyzing sophisticated systems. The improved computation efficiency and reduced size of the training dataset required for deep learning have enabled various prediction models in the manufacturing industry. In this paper, a new approach is developed using the deep convolutional network with the Bayesian optimization algorithm to predict the diameters of the drilled hole from an electrochemical machining process. The Keras application programming interface (API) was used to build the deep convolutional network; the feed rate, pulse-on time, and voltage were used as input parameters to provide a fair comparison with a neural network from previous research. Random dropout layers were added to prevent overfitting of the network. Instead of tuning the network parameter by trial and error, the Bayesian parameter optimization algorithm was implemented to find the optimal set of parameters of the deep convolutional network that yields the minimum mean square error. The proposed algorithm was compared with a previously developed neural network with partially embedded physical knowledge. Improved training speed and accuracy were observed in comparison with the traditional neural network. The prediction model using the proposed deep learning algorithm demonstrated better prediction accuracy and provided a more systematic way to select the hyperparameter for the deep convolutional network. Full article
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Open AccessArticle
Impact of the Process Parameters, the Measurement Conditions and the Pre-Machining on the Residual Stress State of Deep Rolled Specimens
J. Manuf. Mater. Process. 2019, 3(3), 56; https://doi.org/10.3390/jmmp3030056
Received: 24 May 2019 / Revised: 30 June 2019 / Accepted: 5 July 2019 / Published: 10 July 2019
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Abstract
Mechanical surface treatments, e.g., deep rolling, are widely spread finishing processes due to their ability to enhance the fatigue strength of the treated materials with means of cold working and inducement of favorable compressive residual stresses. Despite of the clear advantages of deep [...] Read more.
Mechanical surface treatments, e.g., deep rolling, are widely spread finishing processes due to their ability to enhance the fatigue strength of the treated materials with means of cold working and inducement of favorable compressive residual stresses. Despite of the clear advantages of deep rolling, the controlled generation of compressive residual stresses is still a challenging task, as the process can be influenced by the pre-machining stress state of the treated material. Additionally, the exact characterization of the induced residual stress field is impacted by the specific characteristics of the applied measurement technique. Therefore, this paper is focused on the X-ray diffraction residual stress analysis of deep rolled specimens, pre-machined to achieve rough or polished surface. The deep rolling process was realized as a single-trace to avoid the influence of the other process parameters and the resulted residual stress field on the surface and in depth was investigated. Additionally, the surface residual stress profiles were determined using two different measuring devices to analyze the impact of the different measurement conditions. Full article
(This article belongs to the Special Issue Surface Integrity in Machining)
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Open AccessArticle
Stress-Induced Phase Transformation and Its Correlation with Corrosion Properties of Dual-Phase High Carbon Steel
J. Manuf. Mater. Process. 2019, 3(3), 55; https://doi.org/10.3390/jmmp3030055
Received: 21 June 2019 / Revised: 5 July 2019 / Accepted: 5 July 2019 / Published: 9 July 2019
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Abstract
It is well known that stress-induced phase transformation in dual-phase steel leads to the degradation of bulk corrosion resistance properties. Predicting this behaviour in high carbon steel is imperative for designing this grade of steel for more advanced applications. Dual-phase high carbon steel [...] Read more.
It is well known that stress-induced phase transformation in dual-phase steel leads to the degradation of bulk corrosion resistance properties. Predicting this behaviour in high carbon steel is imperative for designing this grade of steel for more advanced applications. Dual-phase high carbon steel consists of a martensitic structure with metastable retained austenite which can be transformed to martensite when the required energy is attained, and its usage has increased in the past decade. In this study, insight into the influence of deformed microstructures on corrosion behaviour of dual-phase high carbon steel was investigated. The generation of strain-induced martensite formation (SIMF) by residual stress through plastic deformation, misorientation and substructure formation was comprehensively conducted by EBSD and SEM. Tafel and EIS methods were used to determine corrosion intensity and the effect of corrosion behaviour on hardness properties. As a result of the static compression load, the retained austenite transformed into martensite, which lowered its corrosion rate by 5.79% and increased the dislocation density and the length of high-angle grain boundaries. This study demonstrates that balancing the fraction of the martensite phase in structure and dislocation density, including the length of high-angle grain boundaries, will result in an increase in the corrosion rate in parallel with the applied compression load. Full article
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Open AccessArticle
The Effect of Switchback Parameters on Root Pass Formation of Butt Welds with Variable Gap
J. Manuf. Mater. Process. 2019, 3(3), 54; https://doi.org/10.3390/jmmp3030054
Received: 17 June 2019 / Revised: 27 June 2019 / Accepted: 1 July 2019 / Published: 5 July 2019
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Abstract
Root pass manufacturing in automated welding is still a challenge when the backing plate is not feasible. Using the concept of bead formation in an original way, the GMAW (Gas Metal Arc Welding) switchback technique was assessed against linear movement as a means [...] Read more.
Root pass manufacturing in automated welding is still a challenge when the backing plate is not feasible. Using the concept of bead formation in an original way, the GMAW (Gas Metal Arc Welding) switchback technique was assessed against linear movement as a means of facing this challenge. Experimental work was applied, keeping the process parametrization and joint configuration, so that only the switchback parameters were modified, i.e., the stroke lengths and speeds. Thermography was used to estimate the effect of the switchback parameters on bead formation. The results showed the potential of the switchback technique as a means of favoring weld pool control. Surprisingly, the operational gap range is not necessarily larger when switchback is applied. The strong influence of stroke lengths and speeds on the process performance was characterized. In general, the results showed that linear movement leads to larger pools and deeper penetrations, more adequate for gaps with no clearances. Shorter stroke lengths and slower stroke speeds (intermediate pool size) better suit root gaps that are not too wide, while longer stroke lengths and faster stroke speeds (smaller pool size, more easily sustainable) are applicable to larger root gaps. Full article
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Open AccessArticle
Prediction of Surface Quality Based on the Non-Linear Vibrations in Orthogonal Cutting Process: Time Domain Modeling
J. Manuf. Mater. Process. 2019, 3(3), 53; https://doi.org/10.3390/jmmp3030053
Received: 2 May 2019 / Revised: 21 June 2019 / Accepted: 24 June 2019 / Published: 26 June 2019
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Abstract
This work presents an analysis of relationships between the non-linear vibrations in machining and the machined surface quality from an analytical model based on a predictive machining theory. In order to examine the influences of tool oscillations, several non-linear mechanisms were considered. Additionally, [...] Read more.
This work presents an analysis of relationships between the non-linear vibrations in machining and the machined surface quality from an analytical model based on a predictive machining theory. In order to examine the influences of tool oscillations, several non-linear mechanisms were considered. Additionally, to solve the non-linear problem, a new computational strategy was developed. The resolution algorithm significantly reduces the computational times and makes the iterative approach more stable. In the present approach, the coupling between the tool oscillations and (i) the regenerative effect due to the variation of the uncut chip thickness between two successive passes and/or when the tool leaves the work (i.e., the tool disengagement from the cut), (ii) the friction conditions at the tool–chip interface, and (iii) the tool rake angle was considered. A parametric study was presented. The correlation between the surface quality, the cutting speed, the tool rake angle, and the friction coefficient was analyzed. The results show that, during tool vibrations, the arithmetic mean deviation of the waviness profile is highly non-linear with respect to the cutting conditions, and the model can be useful for selecting optimal cutting conditions. Full article
(This article belongs to the Special Issue Surface Integrity in Machining)
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Open AccessArticle
Metal Additive Manufacturing Cycle in Aerospace Industry: A Comprehensive Review
J. Manuf. Mater. Process. 2019, 3(3), 52; https://doi.org/10.3390/jmmp3030052
Received: 29 May 2019 / Revised: 13 June 2019 / Accepted: 21 June 2019 / Published: 26 June 2019
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Abstract
Additive Manufacturing (AM) is the forefront of advanced manufacturing technologies and has the potential to revolutionize manufacturing, with a dramatic change in the design and project paradigms. A comprehensive review of existent metal AM processes, processable materials, respective defects and inspection methods (destructive [...] Read more.
Additive Manufacturing (AM) is the forefront of advanced manufacturing technologies and has the potential to revolutionize manufacturing, with a dramatic change in the design and project paradigms. A comprehensive review of existent metal AM processes, processable materials, respective defects and inspection methods (destructive and non-destructive) is presented in a succinct manner. Particularly, the AM design optimization methodologies are reviewed and their threats and constraints discussed. Finally, an aerospace industry case study is presented and several cost-effective examples are enumerated. Full article
(This article belongs to the Special Issue Recent Development in Metal Additive Manufacturing)
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J. Manuf. Mater. Process. EISSN 2504-4494 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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