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Developing a computational model for studying tribological performance is essential for computing accurate life cycle of various materials. Caused by the existence of complicated and nonlinear interactions between material surfaces, exact modeling of wear behavior is very difficult. Artificial intelligence (AI) can be used in distinguishing similar patterns in experimental data and predictive modeling of a certain material's wear behavior. In this paper, artificial neural networks (ANNs) approach, adaptive neural-based fuzzy inference system (ANFIS) technique, and fuzzy clustering method (FCM) are used to develop a simple, accurate, and applicable model for predicting the wear behavior of sinter-hardened steel parts. Three different cooling rates (0.5, 2, and 3 °C/s) were applied on six specimens made out of pre-alloyed Astaloy 85 Mo and Distaloy AB powders by powder metallurgy (PM) method. Reciprocating dry sliding wear tests were carried out on these specimens with three different loads. The empirical results were assorted in two different batches. One was used along with the mentioned artificial intelligence approaches to develop three wear behavior models. In order to verify and compare these models, predicted results gained from these models were compared to the second batch of the results. Outcome of the models were promising. Using magnitude of two statistical functions, root-mean squared error and coefficient of multiple determinations, we showed that our predictions are in great correlation with experimental data and the best performance can be obtained using ANN method. [ABSTRACT FROM AUTHOR]

Adaptive neural network-based fuzzy inference system (ANFIS) is an artificial intelligent neuro-fuzzy technique used for modeling and control of ill-defined and uncertain systems. The present paper proposes this novel technique of ANFIS to predict the tensile strength of inertia friction-welded tubular pipe joints with the aid of artificial neural network approach combined with the principle of fuzzy logic. The proposed model is multiple input–single output type of model which uses rotational speed and forge load as input signals. The set of rules has been generated directly from the experimental data using ANFIS. The performance of the proposed model is validated by comparing the predicted results with the actual practical results obtained by conducting the confirmation experiments. The application of χ2 test confirms that the values of tensile strength predicted by proposed ANFIS model are well in agreement with the experimental values at 0.1 % level of significance. The proposed model can also be used as intelligent online adaptive control model for pipeline welding. [ABSTRACT FROM AUTHOR]

This paper describes the development of an artificial neural network-based in-process mixed-material-caused flash monitoring system (ANN-IPMFM) in the injection molding process. This proposed system integrates two sub-systems. One is the vibration monitoring sub-system that utilizes an accelerometer sensor to collect and process vibration signals during the injection molding process. The other, a threshold prediction sub-system, predicts a control threshold based on the process parameter settings, thus allowing the system to adapt to changes in these settings. The integrated system compares the monitored vibration signals with the control threshold to predict whether or not flash will occur. The performance of the ANN-IPMFM system was determined by using varying ratios of polystyrene (PS) and low-density polyethylene (LDPE) in the injection molding process, and comparing the number of actual occurrences of flash with the number of occurrences predicted by the system. After a 180 trials, results demonstrated that the ANN-IPMFM system could predict flash with 92.7% accuracy. [ABSTRACT FROM AUTHOR]

Clothing manufacturers’ direct investment and joint ventures in developing regions have seen to grow rapidly in the past few decades. Manufacturers face difficulties during the decision-making process in the selection of a plant location due to vague and subjective considerations. Selecting a plant location relies mostly on subjective intuition and assessment as variables to be considered in the decision making process. But these variables cannot always be represented in terms of objective value, such as country risk and community facilities. Though several optimization techniques have been developed to assist decision makers in searching for the optimal sites, it is difficult to rank the sites which display a small difference of scores. Classification is thus more reasonable and realistic. This paper investigates two recent types of classification techniques, namely unsupervised and supervised artificial neural networks, on the site selection problem of clothing manufacturing plants. The limitations of adaptive resonance theory in unsupervised artificial neural networks will be demonstrated. A comparison of the performance of the three types of supervised artificial neural networks – including back propagation, learning vector quantization and probabilistic neural network – is used and the proposed classification decision model will be presented. The experimental results indicate that the supervised artificial neural network is a proven and effective classifier in which a probabilistic neural network performs better than the others in this site selection problem. [ABSTRACT FROM AUTHOR]

Recently, many methods based on artificial neural networks (ANNs) or deep neural networks (DNNs) have been proposed to accurately predict the cycle time of a job. However, the prediction mechanism of an ANN is difficult to understand and communicate for users, which limits its acceptability (or usefulness). To solve this problem, a two-stage explainable artificial intelligence (XAI) approach is proposed in this study to better explain a classification-based cycle time prediction method. In the proposed methodology, first, jobs are divided into several clusters. A scatter radar diagram is then designed to illustrate the classification result. Compared with existing XAI techniques, the scatter radar diagram meets more requirements for better interpretation. Subsequently, an ANN is constructed for each cluster to predict the cycle times of jobs in the cluster. A random forest is then constructed to approximate the prediction mechanism of the ANN. In existing practice, the random forest generates many decision rules to predict the cycle time of a job, which may cause confusion for the user. To solve this problem, a systematic procedure is established to re-organize these decision rules. In this way, the first few decision rules can provide most of the information, and the user does not have to read all the rules. The two-stage XAI approach has been applied to a real case from the literature to evaluate its effectiveness. [ABSTRACT FROM AUTHOR]

The digitalization of industry is targeting at the integration of artificial intelligence (AI) in manufacturing systems, for delivering intelligent machinery. Although AI seems a long-term target, similar enabling technologies such as artificial neural networks (ANNs) have been introduced. Despite that ANNs are inspired by the human brain's functioning, understanding how they work and training them is a challenging task, requiring engineers with advanced math and coding skills. On the contrary, augmented reality (AR) is a cutting-edge digital technology, enabling the registration of 3D content on the physical environment, thus enhancing user's perception in a growing variety of scientific fields. Therefore, this research work aims at the design and development of an AR-based framework that facilitates the conceptualization of an ANN through AR, assists engineers train efficient ANN and moreover share knowledge through suitable communication channels. Finally, the framework can handle datasets with the use of cloud services. [ABSTRACT FROM AUTHOR]

In this study, an adaptive optimization method based on artificial neural network model is proposed to optimize the injection molding process. The optimization process aims at minimizing the warpage of the injection molding parts in which process parameters are design variables. Moldflow Plastic Insight software is used to analyze the warpage of the injection molding parts. The mold temperature, melt temperature, injection time, packing pressure, packing time, and cooling time are regarded as process parameters. A combination of artificial neural network and design of experiment (DOE) method is used to build an approximate function relationship between warpage and the process parameters, replacing the expensive simulation analysis in the optimization iterations. The adaptive process is implemented by expected improvement which is an infilling sampling criterion. Although the DOE size is small, this criterion can balance local and global search and tend to the global optimal solution. As examples, a cellular phone cover and a scanner are investigated. The results show that the proposed adaptive optimization method can effectively reduce the warpage of the injection molding parts. [ABSTRACT FROM AUTHOR]

The present work deals with drill wear monitoring using an artificial neural network. A back propagation neural network (BPNN) has been used to predict the flank wear of high-speed steel (HSS) drill bits for drilling holes on copper work-piece. Experiments have been carried out over a wide range of cutting conditions and the effect of various process parameter like feedrate, spindle speed, and drill diameter on thrust force and torque has been studied. The data thus obtained from the experiments have been used to train a BPNN for wear prediction. The performance of the trained neural network has been tested with the experimental data, and has been found to be satisfactory. [ABSTRACT FROM AUTHOR]

The electronic industry is rapidly developing, creating high demand for IC production. Etched semiconductor lead frames are the basic material used in IC packaging. IC packaging requires high-precision lead frames. The dimensions of the pilot hole are generally required to be highly precise in lead frame manufacturing. The photo-etching process must control the dimension of the pilot hole and record the manufacturing data of the etching machine and inspection data. This study presents the development of an artificial neural network (ANN) model that can be applied to construct the predicting model. The predictive model can estimate the dimensions of the pilot hole and thus determine the process parameters needed to improve lead frame quality in the etching process. [ABSTRACT FROM AUTHOR]