In the frame of the process monitoring domain, traceability in environments with non-exhaustive event logs remains as a challenge. In these scenarios, the discovery of the route followed by a particular item along the entire process is a difficult task since it is not possible to access all the required pieces of information from processes under monitorization. To tackle this issue, the concept of

Although it provides a feasible inbound logistics solution for steady production and low inventory management, the Milk-run mode inevitably leads to a high transportation costs due to the features of small-batch and high-frequency delivery. In order to break through the defections of the existing inbound logistics mode, an integrated inbound logistics (IIL) mode with low-carbon and high efficiency

A macroscopic model evaluating shear behavior is necessary to simulate the failure process of shear walls under different axial compression ratios and different out-of-plane bending moments. Accordingly, based on the results of experiments and numerical simulations, a three-segment skeleton curve model was established to reflect the relationship between shear force and deformation when a reinforced

This study proposed an approach to optimize the process parameters using the entropy weight method combining regression analysis in the resistance spot welding process. Based on the central composite experimental design, tests were carried out with three levels of process parameters for spot-welded titanium alloy sheets. Multiple quality characteristics, namely nugget diameter, maximum displacement

Wire plus arc additive manufacturing (WAAM) has been demonstrated to be a powerful technique to produce large-scale metal parts with low cost. However, techniques to achieve accurate geometry control and high process stability have not yet been perfectly developed. Although implementing vision sensing and closed-loop control can contribute to promoting the levels of process automation and stability

Predicting remaining useful life (RUL) is crucial for system maintenance. Condition monitoring makes not only degradation data available for RUL estimation but also categorized health status data for health state identification. However, RUL prediction has been treated as an independent process in most cases even though potential relevance exists with health status detection process. In this paper

In this investigation, the welding quality of TC2 titanium alloy with 0.4 mm thickness was predicted using two regression models and an artificial neural network model. The welding current and the voltage between the upper and lower electrodes were obtained using the Rogowski coil and a line voltage sensor. And then the variations of the dynamic resistance curve and the effects of the welding current

Some of the solutions reported in a recent paper (Akyol and Baykasoglu in J Intell Manuf 30(2):557–573, 2019. https://doi.org/10.1007/s10845-016-1262-6) are infeasible, jeopardising the conclusion that “best results for the 75% of the 320 test instances for the ALWABP-2” were obtained. In this note, we explore the contributions of the paper, show that some of the solutions are indeed infeasible and

Statistical process monitoring (SPM) methods have been adopted and studied to detect variations in the fused deposition modeling (FDM) process in recent years. The FDM process that builds parts layer-by-layer is accomplished in a specified manufacturing period (number of layers) without interruption or suspension. Thus, traditional SPM methods, where the average run length is used for the calculation

When machining metastable austenitic stainless steel with cryogenic cooling, a deformation-induced phase transformation from γ-austenite to α′-martensite can be realized in the workpiece subsurface. This leads to a higher microhardness and thus improved fatigue and wear resistance. A parametric and a non-parametric model were developed in order to investigate the correlation between the thermomechanical

This paper proposes a novel incremental model for acquiring skills and using them in Intrinsically Motivated Reinforcement Learning (IMRL). In this model, the learning process is divided into two phases. In the first phase, the agent explores the environment and acquires task-independent skills by using different intrinsic motivation mechanisms. We present two intrinsic motivation factors for acquiring

Establishing an effective early warning mechanism for the rocket final assembly process (RFAP) is crucial for the timely delivery of rockets and the reduction of additional production costs. To solve the unsystematic design of warning indicators and warning levels in RFAP and address the problem of low warning accuracy caused by imbalanced data distribution, this paper redesigns the warning indicators

Non-metallic inclusions are unavoidably produced during steel casting resulting in lower mechanical strength and other detrimental effects. This study was aimed at developing a machine learning algorithm to classify castings of steel for tire reinforcement depending on the number and properties of inclusions, experimentally determined. 855 observations were available for training, validation and testing

The introduction of data-related information technologies in manufacturing allows to capture large volumes of data from the sensors monitoring the production processes and different alarms associated to them. An early prediction of those alarms can bring several benefits to manufacturing companies such as predictive maintenance of the equipment, or production optimization. This paper introduces a new

Today’s Agriculture vehicles (AgV)s are expected to encompass mainly the three requirements of customers; economy, the use of High technology and reliability. In this manuscript, we investigate the technology solution for efficient health monitoring and diagnostic (HM&D) strategy to maximize the field efficiency and minimize the machine cost. Based on the data captured by various IoT sensors, we demonstrate

The need to monitor tool wear is crucial, particularly in advanced manufacturing industries, as it aims to maximise the lifespan of the cutting tool whilst guaranteeing the quality of workpiece to be manufactured. Although there have been many studies conducted on monitoring the health of cutting tools under a specific cutting condition, the monitoring of tool wear across multi-cutting conditions still

The early fault diagnosis is a kind of important technology to ensure the normal and reliable operation of wind turbines. However, due to the potential presence of noisy labels in health condition dataset and the weakly explanation of the deep neural network decisions, the performance of fault diagnosis is severely limited. In this paper, a framework called normalized recurrent neural network (NRNN)

In this work, the performance of rapid prototyping (RP) based rapid tool is investigated during electrical discharge machining (EDM) of titanium as work piece using EDM 30 oil as dielectric medium. Selective laser sintering, a RP technique, is used to produce the tool electrode made of AlSi10Mg. The performance of rapid tool is compared with conventional solid copper and graphite tool electrodes. The

Modern production systems face enormous challenges due to rising customer requirements resulting in complex production systems. The operational efficiency in the competitive industry is ensured by an adequate production control system that manages all operations in order to optimize key performance indicators. Currently, control systems are mostly based on static and model-based heuristics, requiring

As the complex driving scenarios bring about an opportunity for application of deep learning in safe driving, artificial intelligence based on deep learning has become a heatedly discussed topic in the field of advanced driving assistance system. This paper focuses on analysing vehicle active safety control of collision avoidance for intelligent connected vehicles (ICVs) in a real driving risk scenario

In the present day markets, it is essential for organizations that manage their supply chain efficiency to sustain their market share and improve profitability. Optimized inventory control is an integral part of supply chain management. In inventory control problems, determining the ordering times and the order quantities of products are the two strategic decisions either to minimize total costs or

In recent years, recommender systems have become necessary in overcoming the challenges related to the incredible growth of information. They are used in a wide range of contexts and applications, mainly as prediction tools for customer interest, designed to help customers decide, compare, discover and explore products (Meyer in Recommender systems in industrial contexts, Sciences et Technologies de

3D printing offers great potential for developing complex flexure mechanisms. Recently, thickness-correction factors (TCFs) were introduced to correct the thickness and stiffness deviations of powder-based metal 3D printed flexure hinges during design and analysis. However, the reasons for the different TCFs obtained in each study are not clear, resulting in a limited value of these TCFs for future

Regional science and technology (S&T) resource allocation is an important supporting means of Intelligent Manufacturing in the future. Research on the efficiency of S&T resource allocation is helpful to judge the potential of Intelligent Manufacturing in a specific region. S&T performance evaluation and resource allocation are critical administrative activities for a country or region. Due to resource

Remanufacturing plays a vital role in promoting the development of circular economy for its great advantages in energy saving, material saving and emission reduction. Remanufacturing process planning (RPP), which affects the performance of remanufacturing greatly, becomes increasingly important to the remanufacturing enterprises. In general, RPP is knowledge dependent. Some remanufacturing enterprises

In the manufacturing process of LED chips, the accuracy of the LED chip visual localization system affects the quality of LED chip production directly. There are many errors that have impacts on positioning system accuracy. Therefore, the identification and compensation of the critical errors is key to efficiently improving the precision of the system. Based on this fact, an error analysis method and

Facility layout aims to arrange a set of facilities in a site. The main objective function is to minimize the total material handling cost under production-derived constraints. This problem has received much attention during the past decades. However, these works have mainly focused on solving a 2D layout problem, dealing with the footprints of pieces of equipment. The obtained results have been then

In this paper, an enhanced random vector functional link network (RVFL) algorithm was employed to predict kerf quality indices during CO2 laser cutting of polymethylmethacrylate (PMMA) sheets. In the proposed model, the equilibrium optimizer (EO) is used to augment the prediction capability of RVFL via selecting the optimal values of RVFL parameters. The predicting model includes four input variables:

Operations involving gas–liquid agitated vessels are common in the biochemical and chemical industry; ensuring good contact between the two phases is essential to process performance. In this work, a methodology to compute acoustic emission data, recorded using a piezoelectric sensor, to evaluate the gas–liquid mixing regime within gas–liquid and gas–solid–liquid mixtures was developed. The system

With the transformation from traditional manufacturing to intelligent manufacturing, customer-oriented personalized customization has gradually become the main mode of production. Interactive algorithms determine the pros and cons of the solution via customers which can make customers better participants in the customization process. However, if the population size is expanded and the number of evolutionary

The paradigm shift toward Industry 4.0 is not solely completed by enabling smart machines in a factory but also by facilitating human capability. Refinement of work processes and introduction of new training approaches are necessary to support efficient human skill development. This study proposes a new skill transfer support model in a manufacturing scenario. The proposed model develops two types

The recognition of woven fabric pattern is a crucial task for mass manufacturing and quality control in the textile industry. Traditional methods based on image processing have some limitations on accuracy and stability. In this paper, an automatic method is proposed to jointly realize yarn location and weave pattern recognition. First, a new big fabric dataset is established by a portable wireless

Recent technological advancements in computing, sensing and communication have led to the development of cyber-physical manufacturing processes, where a computing subsystem monitors the manufacturing process performance in real-time by analyzing sensor data and implements the necessary control to improve the product quality. This paper develops a predictive control framework where control actions are

Most scheduling problems are required to follow rigid metrics, such as the maximum completion time, earliest deadline first, etc., ignoring the flexibility of manufacturing services (MSs) and the effects of their historical data hidden in millions of manufacturing activities. The historical data serves as a powerful basis for describing the comprehensiveness or credibility of the MS itself with the

The amount of electronic waste generated in the world is impressive. The USA alone yearly throw away 9.4 million tons of electronic devices: only 12.5% is recycled. One way to reduce this massive impact on the environment is to disassemble these devices with the aim of reusing and recycling as many parts as possible. Disassembling end-of-life products is a complex industrial process that may pose workers

Equipment maintenance is momentous for improving production efficiency, how to integrate maintenance into production to address uncertain problems has attracted considerable attention. This paper addresses a novel approach for integrating preventive maintenance (PM) into production planning of a complex manufacturing system based on availability and cost. The proposed approach relies on two phases:

This paper presents a new systematic approach to the optimization of both design and manufacturing variables across a multi-step production process. The approach assumes a generic manufacturing process in which an initial near net shape (NNS) process is followed by a limited number of finishing operations. In this context the optimisation problem becomes a multi-variable problem in which the aim is

The template design problem (TDP) is a hard combinatorial problem with a high number of symmetries which makes solving it more complicated. A number of techniques have been proposed in the literature to optimise its resolution, ranging from complete methods to stochastic ones. However, although metaheuristics are considered efficient methods that can find enough-quality solutions at a reasonable computational

This study proposed a deep convolutional neural network (DCNN) aided optimization (DCNNAO) method to improve the quality of deposition during the cold spray process which was simulated by molecular dynamics (MD). The idea of the DCNNAO is to extract the value of the objective function from the MD simulation snapshots directly by DCNN aided image process technique. Considering the huge memory requirement

Due to technology advancement, smart visual sensing required in terms of data transfer capacity, energy-efficiency, security, and computational-efficiency. The high-quality image transmission in visual sensor networks (VSNs) consumes more space, energy, transmission delay which may experience the various security threats. Image compression is a key phase of visual sensing systems that needs to be effective

Intelligent fault diagnosis methods based on signal analysis have been widely used for bearing fault diagnosis. These methods use a pre-determined transformation (such as empirical mode decomposition, fast Fourier transform, discrete wavelet transform) to convert time-series signals into frequency domain signals, the performance of dignostic system is significantly rely on the extracted features. However

Design of automation system relies on experts’ knowledge and experience accumulated from past solutions. In designing novel solutions, however, it is difficult to apply past knowledge and achieve design right-first-time, therefore wasting valuable resources and time. SADT/IDEF0 models are commonly used by automation experts to model manufacturing systems based on the manual process. However, function

This study introduces a developed method to a smart computer-aided design/manufacturing (CAD/CAM) system, where layout design, process planning, and comprehensive computerized numerical control (CNC) code generation can be implemented to satisfy laser cutting holes, tapping, irregular and complicated profile processing, engraving, and burr back-scraping. The smart CAD/CAM(SCAM) system is developed

In recent years, the growing popularity of artificial neural networks has urged more and more researchers to try introduce these methods to the machining field, with some of them actually producing good results. The acquisition of cutting data often means higher cost and time, limiting the application of neural network in the machining sector, to a certain extent. In this paper, for the task of cutting

Robots are extensively used during the era of Industry 4.0 to achieve high productivity, better quality and lower cost. While designing a robotic assembly line, production managers are concerned about the cost involved in such a system development. Most of the research reported till date did not consider purchasing cost while optimizing the design of a robotic assembly line. This study presents the

Quality control in silicon steel manufacturing process is a crucial step. The application of image processing techniques is very useful in steel inspection and manufacturing. It has established to be the most reliable and promising solution for the development of an automatic defect detection. Since the surface of the silicon steel strip has a cluttered background and defects with small sizes, flaws

In smart manufacturing practices, finding a balance between the size of cyber-physical system (CPS) deployment and the performance of production systems is the preliminary goal pursued by some small or medium-sized one-of-a-kind production enterprises with limited resources in implementing the transformation of smart production systems. To achieve this goal, we first address the pull control strategies

In this paper we focus on an unrelated parallel workgroup scheduling problem where each workgroup is composed of a number of personnel with similar work skills which has eligibility and human resource constraints. The most difference from the general unrelated parallel machine scheduling with resource constraints is that one workgroup can process multiple jobs at a time as long as the resources are

In order to find a new lean methodology to improve the process of procurement management, this paper takes the warehouse in the manufacturing enterprise as a case to study. During lean consulting service provision, the firsthand material which the research work needs through the spot inspection was collected. To analysis the whole procurement process, the procurement value stream map based on the data

Compared with obstacle avoidance in open environment, collision-free path planning for duct-enter task is often challenged by narrow and complex space inside ducts. For obstacle avoidance, redundant robot is usually applied for this task. The motion of redundant robot can be decoupled to end-effector motion and self-motion. Current methods for duct-enter task are not robust due to the difficulty to

The routing design of the various electrical wires, tubes, and hoses of a commercial vehicle requires a significant number of man-hours because of the variety of the commercial vehicles, frequent design changes of other vehicular components and the manual trial-and-error approaches. This study proposes a new graph-based routing algorithm to find the collision-free routing path in the constrained space

A growing awareness is brought that the safety and security of industrial control systems cannot be dealt with in isolation, and the safety and security of industrial control protocols (ICPs) should be considered jointly. Fuzz testing (fuzzing) for the ICP is a common way to discover whether the ICP itself is designed and implemented with flaws and network security vulnerability. Traditional fuzzing

Time series data is pervasive in many applications and the anomaly detection about it is important, which will provide the early warning of some unexpected patterns. In this paper, we propose a multiple similarity based anomalous subsequences detection method, which is unsupervised and domain knowledge free. Firstly, to improve the time efficiency, an anomaly candidates selection scheme is introduced

The development of information technology and process technology have been enhanced the rapid changes in high-tech products and smart manufacturing, specifications become more sophisticated. Large amount of sensors are installed to record equipment condition during the manufacturing process. In particular, the characteristics of sensor data are temporal. Most the existing approaches for time series

The asset administration shell (AAS) has a virtual representation as an asset description and technical functionality as a smart manufacturing service. A digital twin (DT) is an advanced virtual factory technology that has simulation as its core technical functionality, which it performs in the type and instance stages of the physical asset. For providing an efficient information object to the DT application

The fast and accurate estimation of makespan is essential for the determination of the delivery date and the sustainable development of the enterprise. In this paper, a high-quality training dataset is constructed and an adaptive ensemble model is proposed to achieve fast and accurate makespan estimation. First, both the logistics features extracted by the Pearson correlation coefficient and the new

Due to the real working conditions, the collected mechanical fault datasets are actually limited and always highly imbalanced, which restricts the diagnosis accuracy and stability. To solve these problems, we present an imbalanced fault diagnosis method based on the generative model of conditional-deep convolutional generative adversarial network (C-DCGAN) and provide a study in detail. Deep convolutional

Deep neural network (DNN) exhibits state-of-the-art performance in many fields including weld defect classification. However, there is still a large room for improving the classification performance over the generic DNN models. In this paper, a unified deep neural network with multi-level features is proposed for weld defect classification. Firstly, we define 11 weld defect features as inputs of our

The application of cutting-edge technologies such as AI, smart sensors, and IoT in factories is revolutionizing the manufacturing industry. This emerging trend, so called smart manufacturing, is a collection of various technologies that support decision-making in real-time in the presence of changing conditions in manufacturing activities; this may advance manufacturing competitiveness and sustainability

This study investigates a flow shop group scheduling problem where both sequence-dependent setup time between groups and round-trip transportation time between machines are considered. The objective is to minimize makespan. To solve the problem, we first develop a mixed integer linear programming model and then propose an efficient co-evolutionary discrete differential evolution algorithm (CDDEA).