Empirical Mode Decomposition of Pressure Signal for Health Condition Monitoring in Waterjet Cutting

Waterjet/abrasive waterjet cutting is a flexible technology that can be exploited for different operations on a wide range of materials. Due to challenging pressure conditions, cyclic pressure loadings, and aggressiveness of abrasives, most of the components of the ultra-high pressure (UHP) pump and the cutting head are subject to wear and faults that are difficult to predict. Therefore, the continuous monitoring of machine health conditions is of great industrial interest, as it allows implementing condition-based maintenance strategies, and providing an automatic reaction to critical faults, as far as unattended processes are concerned. Most of the literature in this frame is focused on indirect workpiece quality monitoring and on fault detection for critical cutting head components (e.g., orifices and mixing tubes). A very limited attention has been devoted to the condition monitoring of critical UHP pump components, including cylinders and valves. The paper investigates the suitability of the water pressure signal as a source of information to detect different kinds of fault that may affect both the cutting head and the UHP pump components. We propose a condition monitoring approach that couples empirical mode decomposition (EMD) with principal component analysis to detect any pattern deviation with respect to a reference model, based on training data. The EMD technique is used to separate high-frequency transient patterns from low-frequency pressure ripples, and the computation of combined mode functions is applied to cope with the mode mixing effect. Real industrial data, acquired under normal working conditions and in the presence of actual faults, are used to demonstrate the performances provided by the proposed approach

Computer Numerical Control CNC Machine Health Prediction using ‎Multi-domain Feature Extraction and Deep Neural Network Regression

Tool wear monitoring has become more vital in intelligent production to enhance Computer Numerical Control CNC machine health state. Multidomain features may effectively define tool wear status and help tool wear prediction. Prognostics and health management (PHM) plays a vital role in condition-based maintenance (CBM) to prevent rather than detect malfunctions in machinery. This has great advantage of saving costs of fault repair including human effort, financial costs as long as power and energy consumption. The huge evolution of Industrial Internet of Things (IIOT) and industrial big data analytics has made Deep Learning a growing field of research. The PHM society has held many competitions including PHM10 concerning CNC milling machine cutters data for tool wear prediction The purpose of this paper is to predict tool wear of CNC cutters and. We adopted a multi-domain feature extraction method for health statement of the cutters. and a deep neural network DNN method for tool wear prediction

Malfunction and Bad Behavior Diagnosis on Domestic Environment

Abstract Greenhouse gas emissions from homes arise primarily from fossil fuels burned for heat, the use of products that contain greenhouse gases, and the handling of waste. Human activities are responsible for almost all of the increase in greenhouse gases in the atmosphere over the last 150 years. The household sector is one of the biggest aggregate consumers and this is the reason why increasingly policies have been considering it. One of the key factors in curbing energy consumption in this sector is widely recognized to be due to erroneous behaviors and systems malfunctioning, mainly explained by the lack of awareness of the final user; so, training the final user to energy awareness can be more effective and cheaper than other policies. In this context, energy management in homes is playing, and will play even more in future, a key role in increasing the final consumer awareness towards its own energy consumption and consequently in bursting its active role in smart grids. The aim of this paper is to highlight the economic benefits of low cost intelligent control domestic devices, to identify energy behavior, system status and improve energy efficiency. The scope is to develop interaction between final users to create a network of energy consumption efficiency. The paper presents an application of Multi-scale Principal Component Analysis to diagnose inefficient occupant behavior and systems malfunctioning and suggest good practices of energy conservation

Galling wear detection and measurement in sheet metal forming

Galling wear of sheet metal stamping tooling is an expensive issue for sheet metal forming industries. Forming of high strength steels, particularly in the automotive industry, has led to accelerated tool wear rates. These wear rates lead to product quality and die maintenance issues, making galling wear an expensive issue for automotive manufacturers and the sheet metal forming industries in general. Process monitoring allows for the continuous monitoring of tooling condition so that wear development can be detected. The aim of this investigation was to develop an in-depth understanding of the relationship between punch force variation and wear for implementation in future process monitoring regimes. To achieve this aim, the effect of wear and other friction influencing factors on punch force signatures were investigated. This required the development of an accurate method for quantifying galling wear severity so that the relationship between galling wear progression and punch force signature variation could be quantified. Finally, the specific effects of wear and friction conditions on the punch force signatures were examined. An initial investigation using a statistical pattern recognition technique was conducted on stamping force data to determine if the presence of galling wear on press tooling effected punch force variation. Galling wear on tooling, changes in lubrication type, and changes in blank holder pressure were all found to effect variation in punch force signatures shape. A new galling wear severity measurement methodology was developed based on wavelet analysis of 2D surface roughness profiles that accurately provided an indication of the location and severity of galling wear damage. Using the new method for quantifying galling wear severity in the relationship between punch force variation and galling wear progression was investigated, and a strong linear relationship was found. Finally, two prominent vii forms of punch force signature shape variation were linked to friction conditions driven by wear, lubrication, and blank holder pressure. This work describes and quantifies the relationship between galling wear and punch force signature variation. A new methodology for accurate measurement of galling wear severity is presented. Finally, specific forms of punch force signature variation are linked to different friction conditions. These results are critical for future implementation of punch force based galling wear process monitoring and a significant reduction in costs for the metal forming industries

Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments