Back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process

Data-Physics Fusion-Driven Defect Predictions for Titanium Alloy Casing Using Neural Network

The quality of Ti alloy casing is crucial for the safe and stable operation of aero engines. However, the fluctuation of key process parameters during the investment casting process of titanium alloy casings has a significant influence on the volume and number of porosity defects, and this influence cannot be effectively suppressed at present. Therefore, this paper proposes a strategy to control the influence of process parameters on shrinkage volume and number. This study constructed multiple regression prediction models and neural network prediction models of porosity volume and number for a ZTC4 casing by simulating the gravity investment casting process. The results show that the multiple regression prediction model and neural network prediction model of shrinkage cavity total volume have an accuracy of over 99%. The accuracy of the neural network prediction model is higher than that of the multiple regression model, and the neural network model realizes the accurate prediction of shrinkage defect volume and defect number through pouring temperature, pouring time, and mold shell temperature. The sensitivity degree of casing defects to key process parameters, from high to low, is as follows: pouring temperature, pouring time, and mold temperature. Further optimizing the key process parameter window reduces the influence of process parameter fluctuation on the volume and number of porosity defects in casing castings. This study provides a reference for actual production control process parameters to reduce shrinkage cavity and loose defects.

A Review of Optimization and Measurement Techniques of the Friction Stir Welding (FSW) Process

This review reports on the influencing parameters on the joining parts quality of tools and techniques applied for conducting process analysis and optimizing the friction stir welding process (FSW). The important FSW parameters affecting the joint quality are the rotational speed, tilt angle, traverse speed, axial force, and tool profile geometry. Data were collected corresponding to different processing materials and their process outcomes were analyzed using different experimental techniques. The optimization techniques were analyzed, highlighting their potential advantages and limitations. Process measurement techniques enable feedback collection during the process using sensors (force, torque, power, and temperature data) integrated with FSW machines. The use of signal processing coupled with artificial intelligence and machine learning algorithms produced better weld quality was discussed.

Coaxiality error analysis and optimization of cylindrical parts of CNC turning process

High precision rotary shafts with precise geometrical tolerances are generally mounted with a micron level clearance between the gears and casing during operation in industrial applications. Dynamics cyclic loads are inevitable in most of these applications which has an adverse effect on the fatigue life of the critical parts. Ensuring close dimensional tolerances and coaxiality during machining is highly desirable, as it affects the rotary characteristics in many applications. Thus, control of coaxiality error plays a vital role in rotating shafts and high precision machine tools. However, use of high precision machining would drastically increase the cost of manufacturing. Thus, a cost-effective machining process that could potentially reduce the coaxiality error is of high industrial importance. The present research efforts made an attempt to achieve minimum coaxiality error on cylindrical machined parts by optimizing parameters (cutting speed, feed rate, depth of cut and cutting tool nose radius). Experiments are planned, viz. central composite design matrix and statistical analysis determine the influence of machine parameters on coaxiality error of high-strength Al 7075 alloy by applying response surface methodology. Feed rate and depth of cut factors showed significant effect on coaxiality error. All machining parameters showed a non-linear effect on coaxiality error, which defines the strong interaction factor effects. The empirical equations derived were used to minimize coaxiality error by determining a set of machining parameters, viz. applying Big-Bang and Big Crunch and Rao (Rao-1, Rao-2 and Rao-3) algorithms. Rao algorithms outperform the Big-Bang and Big Crunch algorithm both in computation effort and solution accuracy. The results of Rao algorithms are experimentally verified, which resulted in reduced coaxiality error equal to 1.013 µm and resulted in 72.6% improvement compared to CCD experiments.

Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

The pandemic recession has caused enormous disturbances in many industrialized countries. The massive disruption of the supply chain of production is affecting manufacturing companies operating in and around India. Particularly the medium-sized bus body building works have been reduced, due to its compound anomalies. The integrated view of the production facility priorities is not an easy task. Since it is difficult for available labour to conduct an entire project, the completion of a production process is delayed. But still, the dilemma remains as to how production managers can correctly interpret the priorities of the facility. Indeed, this is a problem missing from the previous study. Fortunately, in the current competitive environment, it is essentially needed. This study has been used Back Propagation Neural Network (BPNN) approach for predicting production facility priorities. The experimental results confirm the suitability of the model for predicting priorities. A real-world problem is taken into account in making use of the model output. In this sense, this total solution facilitates production managers in assessing and enhancing the production facilities. The findings emphasize the priority of "equipment effectiveness, labour scheduling and communication" in order to strengthen the post-pandemic production facility.

Casting Process Improvement by the Application of Artificial Intelligence

On the way to building smart factories as the vision of Industry 4.0, the casting process stands out as a specific manufacturing process due to its diversity and complexity. One of the segments of smart foundry design is the application of artificial intelligence in the improvement of the casting process. This paper presents an overview of the conducted research studies, which deal with the application of artificial intelligence in the improvement of the casting process. In the review, 37 studies were analyzed over the last 15 years, with a clear indication of the type of casting process, the field of application of artificial intelligence techniques, and the benefits that artificial intelligence brought. The goals of this paper are to bring to attention the great possibilities of the application of artificial intelligence for the improvement of manufacturing processes in foundries, and to encourage new ideas among researchers and engineers.

Antitumor component recognition from the Aconiti Lateralis Radix Praeparata and Glycyrrhizae Radix et Rhizoma herb pair extract by chemometrics and mean impact value

The purpose of this research is to recognize the active antitumor components from the mixed pair extract of Aconiti Lateralis Radix Praeparata (Fuzi in Chinese) and Glycyrrhizae Radix et Rhizoma (Gancao in Chinese) using chemometrics and mean impact value (MIV) methods. Firstly, 30 common components of 31 different samples were analyzed quantitatively and qualitatively by HPLC-UV and UPLC-Q-TOF tandem mass spectrometry, respectively. Meanwhile, MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assays were used to test the inhibition activities of the 31 different samples against HeLa cells. Then a back propagation (BP) neural network, support vector regression (SVR), and two optimization algorithms – genetic algorithm (GA) and particle swarm optimization (PSO) – were applied to construct composition–activity relationship (CAR) models for the Fuzi–Gancao extract. Based on the optimal CAR model, the MIV was introduced to evaluate the contribution of each individual component to the anticancer efficacy of the extract. Results indicated that the SVR-PSO model best depicted the complex relationship between the chemical composition and the inhibition effect of a Fuzi–Gancao extract. The 30 common components were ranked by their absolute MIVs, and the top 8, which corresponded to peaks 17, 25, 22, 13, 23, 28, 5, and 7 in the chromatogram, were tentatively deemed to be the main antitumor components. The integrated strategy shows a novel and efficient approach to understanding the potential contributions of components from complicated herbal medicines, and the identified results suggest certain directions for screening and research into new antitumor drugs.

CAR models for the Fuzi–Gancao herb pair were constructed by BP, SVR, GA and PSO, and used to fit experimental data. The main active antitumor components were recognized from MIVs based on the optimal CAR model.