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

Abstract 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

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

Abstract 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 frame work 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

IC.IDO as a tool for displaying machining processes. The logic interface between Computer-Aided-Manufacturing and Virtual Reality

Abstract 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

Design of a Multi-Mode Hybrid Micro-Gripper for Surface Mount Technology Component Assembly

In the last few decades, industrial sectors such as smart manufacturing and aerospace have rapidly developed, contributing to the increase in production of more complex electronic boards based on SMT (Surface Mount Technology). The assembly phases in manufacturing these electronic products require the availability of technological solutions able to deal with many heterogeneous products and components. The small batch production and pre-production are often executed manually or with semi-automated stations. The commercial automated machines currently available offer high performance, but they are highly rigid. Therefore, a great effort is needed to obtain machines and devices with improved reconfigurability and flexibility for minimizing the set-up time and processing the high heterogeneity of components. These high-level objectives can be achieved acting in different ways. Indeed, a work station can be seen as a set of devices able to interact and cooperate to perform a specific task. Therefore, the reconfigurability of a work station can be achieved through reconfigurable and flexible devices and their hardware and software integration and control For this reason, significant efforts should be focused on the conception and development of innovative devices to cope with the continuous downscaling and increasing variety of the products in this growing field. In this context, this paper presents the design and development of a multi-mode hybrid micro-gripper devoted to manipulate and assemble a wide range of micro- and meso-SMT components with different dimensions and proprieties. It exploits two different handling technologies: the vacuum and friction

Validation of TiAlN functional coatings through cryo-tribological characterization using a pin-on-disk experiment

Abstract The purpose of this study has been to investigate the effects of TiAlN functional coatings in cryo-tribological pin-on-disk experiments. Nowadays, the introduction of new machining technologies for chip removal, both at high speed and in cryogenic conditions, is posing new challenges and opening new horizons to research. As a matter of fact, that in this technology needs to focus on the choice of workpiece materials to be machining and tools coatings used. For the latter, the wear phenomenon has been studied employing pins-on-disk (made by nickel-based alloys) under a liquid nitrogen jet flows simulating cryogenic machining. The coatings used have been of two different types: the coating C1 is a ZrTiAlN quaternary, while the coating C2 is a TiAlN/ZrN. The films were deposited with processes developed by the ENEA Brindisi laboratory using a dual magnetron sputtering and HiPPMS physical deposition technique. The wear measures were acquired employing a full factorial design with two factors: i.e., the test conditions (DRY or CRYO) and the pin coatings (not coated NC, coating C1 or C2). The number of tests was 12 since 2 were the replications. Based on preliminary experimental results, it can be stated that there is a type of coating, i.e., TiAlN/ZrN, that allows for high processing speed, high material removal, and a considerable increase in tool life

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

Shape Factors And Feasibility of an Industrial Product through Sheet Metal Hydroforming

Sheet metal hydroforming has gained an increasing interest around the world in automotive and aerospace industries. This non conventional metal forming process has many advantages that meet industrial needs in reality very well, such as: formability improvement, good surface quality, higher dimensional accuracy, reduction of springback amount compared with the conventional processes (1). Furthermore, the process chain could be simplified with considerable cost efficiency (2, 3). Through a research program, whose objective is to define specific rules in order to assess a macro-feasibility for a given hydroforming process, the authors have analyzed the influence of the process variables on sheet metal hydroforming by taking into account different types of geometries. The goal of this research, as described in this paperwork, is to implement a methodology that allows one to check the “macro” feasibility of a product through sheet metal hydroforming starting from simple considerations in the early stage of the process design (4). In this specific case, the developed methodology is characterized by the definition of a set of specifically designed “shape factors” and by their application on properly designed study cases. Their application can determine the feasible limits for a considered process set up condition. The definition of the shape factors and of each of their lower bounds has been drawn through an extensive numerical and experimental investigation on three different study cases which have been described in recent publications by the same authors (5, 6 and 7). In this paper the authors aim to describe and to check the developed methodology through “Fondello Fanale”, an application on an industrial test case characterized by a complex geometry. Starting from the geometry of the industrial test case, one can say that it is necessary to use more than one of the defined shape factors to analyze the product feasibility. On the considered component, the most critical areas have been chosen and the geometrical gradients of the shape have been taken into account by the authors. In each one of the considered areas, shape factors have been calculated and their value has been compared to their physical limits for the considered material and thickness. Thorough numerical and experimental investigation the shape factors analysis has given the opportunity to check the non-feasibility of the product. Further developments are related to the possibility, starting from the minimum values of the analyzed shape factors, to re-design the geometry of the product in order to reach its feasibility

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

Punch Radius Influence on “Large Size” Hydroformed Components

Sheet metal hydroforming has gained increasing interest during last years, especially as application in the manufacturing of some components for: automotive, aerospace and electrical appliances for niche productions. Different studies have been also done to determine the optimal forming parameters making an extensive use of FEA. In the hydroforming process a blank sheet metal is formed through the action of a fluid and a punch. It forces the sheet into a die, which contains a compressed fluid. Many studies have been focused on the analysis of process and geometric parameters influence about the hydroforming process of a single product with main dimensions till to 100 mm. In this paper the authors describe the results of an experimental activity developed on two different large sized products obtained through sheet metal hydroforming. Different geometric and process parameters have been taken into account during the testing phase to study, in particular, the punch radius influence on the process feasibility. An ANOVA analysis has been implemented to study the influence of geometrical and process parameters on the maximum hydroforming depth. Through this work it has been possible to verify that in the hydroforming process of large size products geometry and, in particular, punch radius, are some of the main factors that influences the feasibility of the products. Different considerations can be made about the effects of the blankholder force and the fluid pressure on the maximum hydroforming depth. As further developments, the authors would perform a numerical study in order to enlarge the knowledge of the process design space to other possible values of the punch radius