Fault diagnosis by multisensor data: A data-driven approach based on spectral clustering and pairwise constraints

This paper deals with clustering based on feature selection of multisensor data in high-dimensional space. Spectral clustering algorithms are efficient tools in signal processing for grouping datasets sampled by multisensor systems for fault diagnosis. The effectiveness of spectral clustering stems from constructing an embedding space based on an affinity matrix. This matrix shows the pairwise similarity of the data points. Clustering is then obtained by determining the spectral decomposition of the Laplacian graph. In the manufacturing field, clustering is an essential strategy for fault diagnosis. In this study, an enhanced spectral clustering approach is presented, which is augmented with pairwise constraints, and that results in efficient identification of fault scenarios. The effectiveness of the proposed approach is described using a real case study about a diesel injection control system for fault detection

Evaluation of deep learning with long short-term memory networks for time series forecasting in supply chain management

Performance analysis and forecasting the evolution of complex systems are two challenging tasks in manufacturing. Time series data from complex systems capture the dynamic behaviors of the underlying processes. However, non-linear and non-stationary dynamics pose a major challenge for accurate forecasting. To overcome statistical complexities through analyzing time series, we approach the problem with deep learning methods. In this paper, we mainly focus on the long short-term memory (LSTM) networks for demand forecasts in supply chain management, where the future demand for a certain product is the basis for the respective replenishment systems. This study contributes to the literature by conducting experiments on real data to investigate the potential of using LSTM networks for final customer demand forecasting, and hence for increasing the overall value generated by a supply chain. Both forward LSTM and bidirectional LSTM (forward-backward) for short-and long-term demand prediction in supply chain management are considered in this study

Finite Mixture Models for Clustering Auto-Correlated Sales Series Data Influenced by Promotions

The focus of the present paper is on clustering, namely the problem of finding distinct groups in a dataset so that each group consists of similar observations. We consider the finite mixtures of regression models, given their flexibility in modeling heterogeneous time series. Our study aims to implement a novel approach, which fits mixture models based on the spline and polynomial regression in the case of auto-correlated data, to cluster time series in an unsupervised machine learning framework. Given the assumption of auto-correlated data and the usage of exogenous variables in the mixture model, the usual approach of estimating the maximum likelihood parameters using the Expectation–Maximization (EM) algorithm is computationally prohibitive. Therefore, we provide a novel algorithm for model fitting combining auto-correlated observations with spline and polynomial regression. The case study of this paper consists of the task of clustering the time series of sales data influenced by promotional campaigns. We demonstrate the effectiveness of our method in a case study of 131 sales series data from a real-world company. Numerical outcomes demonstrate the efficacy of the proposed method for clustering auto-correlated time series. Despite the specific case study of this paper, the proposed method can be used in several real-world application fields

Thermal characterization methodology for dry finishing turning of SAF 2507 stainless steel based on finite element simulations and surrogate models

This paper addresses the numerical thermal characterization of a 3D turning process of a SAF 2507 stainless steel. A thermographic test campaign was conducted to measure the temperature distribution at the tool-workpiece interface. The campaign was accommodated by means of a L18 fractional factorial design of experiment. The 3D turning process was simulated using the software TWS Advantedge. The heat transfer numerical coefficients were calibrated against experimental measures to obtain temperature values as accurate as possible. A statistical methodology framework was adopted to study the dependence of the coefficients from the machining parameters. A heat transfer surrogate model was then built and next experimentally validated

Utilizing Mixture Regression Models for Clustering Time-Series Energy Consumption of a Plastic Injection Molding Process

Considering the issue of energy consumption reduction in industrial plants, we investigated a clustering method for mining the time-series data related to energy consumption. The industrial case study considered in our work is one of the most energy-intensive processes in the plastics industry: the plastic injection molding process. Concerning the industrial setting, the energy consumption of the injection molding machine was monitored across multiple injection molding cycles. The collected data were then analyzed to establish patterns and trends in the energy consumption of the injection molding process. To this end, we considered mixtures of regression models given their flexibility in modeling heterogeneous time series and clustering time series in an unsupervised machine learning framework. Given the assumption of autocorrelated data and exogenous variables in the mixture model, we implemented an algorithm for model fitting that combined autocorrelated observations with spline and polynomial regressions. Our results demonstrate an accurate grouping of energy-consumption profiles, where each cluster is related to a specific production schedule. The clustering method also provides a unique profile of energy consumption for each cluster, depending on the production schedule and regression approach (i.e., spline and polynomial). According to these profiles, information related to the shape of energy consumption was identified, providing insights into reducing the electrical demand of the plant

IC.IDO as a tool for displaying machining processes. The logic interface between computer-aided-manufacturing and virtual reality

This scientific communication investigates the logic interface of a CAM solver, i.e., MasterCAM, into a Virtual Reality (VR) environment. This integration helps in displaying machining operations in virtual reality. Currently, to partially visualize the results of a simulation in an immersive environment, an import/export procedure must be done manually. Here, a software plugin integrated into IC.IDO (by ESI Group) has been realized and fully described. This application allows the complete integration of CAM solver into the VR environment. In particular, the VERICUT solver has been integrated into VR. This kind of integration has never been done yet

Topic Modeling for Automatic Analysis of Natural Language: A Case Study in an Italian Customer Support Center

This paper focuses on the automatic analysis of conversation transcriptions in the call center of a customer care service. The goal is to recognize topics related to problems and complaints discussed in several dialogues between customers and agents. Our study aims to implement a framework able to automatically cluster conversation transcriptions into cohesive and well-separated groups based on the content of the data. The framework can alleviate the analyst selecting proper values for the analysis and the clustering processes. To pursue this goal, we consider a probabilistic model based on the latent Dirichlet allocation, which associates transcriptions with a mixture of topics in different proportions. A case study consisting of transcriptions in the Italian natural language, and collected in a customer support center of an energy supplier, is considered in the paper. Performance comparison of different inference techniques is discussed using the case study. The experimental results demonstrate the approach’s efficacy in clustering Italian conversation transcriptions. It also results in a practical tool to simplify the analytic process and off-load the parameter tuning from the end-user. According to recent works in the literature, this paper may be valuable for introducing latent Dirichlet allocation approaches in topic modeling for the Italian natural language

A Comparison of Different Topic Modeling Methods through a Real Case Study of Italian Customer Care

The paper deals with the analysis of conversation transcriptions between customers and agents in a call center of a customer care service. The objective is to support the analysis of text transcription of human-to-human conversations, to obtain reports on customer problems and complaints, and on the way an agent has solved them. The aim is to provide customer care service with a high level of efficiency and user satisfaction. To this aim, topic modeling is considered since it facilitates insightful analysis from large documents and datasets, such as a summarization of the main topics and topic characteristics. This paper presents a performance comparison of four topic modeling algorithms: (i) Latent Dirichlet Allocation (LDA); (ii) Non-negative Matrix Factorization (NMF); (iii) Neural-ProdLDA (Neural LDA) and Contextualized Topic Models (CTM). The comparison study is based on a database containing real conversation transcriptions in Italian Natural Language. Experimental results and different topic evaluation metrics are analyzed in this paper to determine the most suitable model for the case study. The gained knowledge can be exploited by practitioners to identify the optimal strategy and to perform and evaluate topic modeling on Italian natural language transcriptions of human-to-human conversations. This work can be an asset for grounding applications of topic modeling and can be inspiring for similar case studies in the domain of customer care quality

A Review of Production Planning Models: Emerging features and limitations compared to practical implementation

In the last few decades, thanks to the interest of industry and academia, production planning (PP) models have shown significant growth. Several structured literature reviews highlighted the evolution of PP and guided the work of scholars providing in-depth reviews of optimization models. Building on these works, the contribution of this paper is an update and detailed analysis of PP optimization models. The present review allows to analyze the development of PP models by considering: i) problem type, ii) modeling approach, iii) development tools, iv) industry-specific solutions. Specifically, to this last point, a proposed industrial solution is compared to emerging features and limitations, which shows a practical evolution of such a system

CaCO3 as an environmentally friendly renewable material for drug delivery systems: Uptake of HSA-CaCO3 nanocrystals conjugates in cancer cell lines

Chemical and biochemical functionalization of nanoparticles (NPs) can lead to an active cellular uptake enhancing their efficacy thanks to the targeted localization in tumors. In the present study calcium carbonate nano-crystals (CCNs), stabilized by an alcohol dehydration method, were successfully modified by grafting human serum albumin (HSA) on the surface to obtain a pure protein corona. Two types of CCNs were used: naked CaCO3 and the (3-aminopropyl)triethoxysilane (APTES) modified CaCO3-NH2. The HSA conjugation with naked CCN and amino-functionalized CCN (CCN-NH2) was established through the investigation of modification in size, zeta potential, and morphology by Transmission Electron Microscopy (TEM). The amount of HSA coating on the CCNs surface was assessed by spectrophotometry. Thermogravimetric analysis (TGA) and Differential scanning calorimetry (DSC) confirmed the grafting of APTES to the surface and successive adsorption of HSA. Furthermore, to evaluate the effect of protein complexation of CCNs on cellular behavior, bioavailability, and biological responses, three human model cancer cell lines, breast cancer (MCF7), cervical cancer (HeLa), and colon carcinoma (Caco-2) were selected to characterize the internalization kinetics, localization, and bio-interaction of the protein-enclosed CCNs. To monitor internalization of the various conjugates, chemical modification with fluorescein-isothiocyanate (FITC) was performed, and their stability over time was measured. Confocal microscopy was used to probe the uptake and confirm localization in the perinuclear region of the cancer cells. Flow cytometry assays confirmed that the bio-functionalization influence cellular uptake and the CCNs behavior depends on both cell line and surface features