For the automatic laser deburring of ceramic cores, this study proposed a method for generating deburring tool paths by combining the computer-aided design/computer-aided manufacturing (CAD/CAM) approach with two-step coarse and fine point cloud registrations method. Specifically, the CAD/CAM approach was employed to generate the ideal tool paths based on the CAD model, and the coarse registration

Behind the brilliance of the deep diagnosis models, the issue of distribution discrepancy between source training data and target test data is being gradually concerned for catering to more practical and urgent diagnostic requirements. Consequently, advanced domain adaptation algorithms have been introduced to the field of fault diagnosis to address this problem. However, in performing domain adaptation

The Cyber-Physical Production System (CPPS) is a concept derived from software (cyber) and hardware (physical) applications and is based on global information exchange between such systems. The CPPS is known as a trend of Industry 4.0 (I4.0) focusing on flexibility regarding new products and adaptability to new requirements. This paper focuses on two I4.0 scenarios described by the Platform Industrie

Optimizing maintenance practices is a continuous process that must take into account the evolving state of the equipment, resources, workers, and more. To help streamline this process, facilities need a concise procedure for identifying critical tasks and assets that have major impact on the performance of maintenance activities. This work provides a process for making data investigations more effective

Titanium-based alloy products manufactured by Selective Laser Melting (SLM) have been widely used in biomedical applications, owing to their high biocompatibility, significantly good mechanical properties. In order to improve the Ti–6Al–4V SLM-fabricated part quality and help the manufacturing engineers choose optimal process parameters, an optimization methodology based on an artificial neural network

Engineering and manufacturing processes and systems designs involve many challenges, such as dynamism, chaotic behaviours, and complexity. Of late, the arrival of big data, high computational speed, cloud computing and artificial intelligence techniques (like machine learning and deep learning) has reformed how many engineering and manufacturing professionals approach their work. These technologies

Along with the coming of industry 4.0 era, industrial internet of things (IIoT) plays a vital role in advanced manufacturing. It can not only connect all equipment and applications in manufacturing processes closely, but also provide oceans of sensor data for real-time work-in-process monitoring. Considering the corresponding abnormalities existing in these sensor data sequences, how to effectively

Small and medium enterprises (SMEs) may not have the maturity to put forward and unfold all the benefits from an ERP based system, a vital tool for production planning. Manufacturing ubiquitous trends, however, are more approachable to SMEs, and even the more affordable tools could be of great advantage. In this paper we propose an algorithmic framework that uses process mining tools to extract the

Industrial big data and artificial intelligence are propelling a new era of manufacturing, smart manufacturing. Although these driving technologies have the capacity to advance the state of the art in manufacturing, it is not trivial to do so. Current benchmarks of quality, conformance, productivity, and innovation in industrial manufacturing have set a very high bar for machine learning algorithms

With the big success of the Cloud Computing, or the Cloud, new research areas appeared. Edge Computing (EC) is one of the recent paradigms that is expected to overcome the Quality of Service (QoS) and latency issues caused by the best-effort behaviour of the Cloud. EC aims to bring the computation power close to the end devices as much as possible and reduce the dependency to the Cloud. Bringing computing

The surface quality inspection of industrial printed circuit board (PCB) is a vitally important link in its manufacturing process. To inspect surface defects of PCBs effectively, the automatic optical inspection (AOI) technology, in which the PCB image acquisition depends on the path planning method, is widely adopted by industry. It is regarded as a characteristic travelling salesman problem (TSP)

Recently, Industry 4.0 facilitates implementing several modular smart factories particularly the Cyber-Physical System. Due to enhanced growth in the Cyber-Physical System, privacy and security issues turned out to be the most significant factor all over the world. This paper demonstrates a complete co-design approach meant for integrating the cyberspace and physical space of a cyber-physical system

This research proposes a method for machining quality monitoring (MQM) in laser-assisted micro-milling (LAMM) of glass. In tool-based mechanical processing including LAMM, the machining quality is generally affected by machining parameters and tool condition; therefore, previous studies have intensively focused on finding optimal machining parameters and monitoring tool condition to secure machining

Restoring damaged components is a very promising and high-value project, which enable to save a lot of production time and cost, and thus has already attracted wide attention, especially in the aviation industry. In the past few years, cold spray (CS) had been widely adopted in restoration and repair applications due to its unique advantages, such as no thermal influence, high efficiency, flexibility

In the last years, Additive Manufacturing, thanks to its capability of continuous improvements in performance and cost-efficiency, was able to partly replace and redefine well-established manufacturing processes. This research is based on the idea to achieve great cost and operational benefits especially in the field of tool making for injection molding by combining traditional and additive manufacturing

The requirements engineering of Industrial Cyber-Physical Systems is extremely challenging due to large system sizes, component heterogeneity, involvement of multi-discipline stakeholders and machines, and continuous evolution. Formal and semi-formal languages, techniques, tools and frameworks can assist by providing repeatable and rigorous structures for eliciting, specifying, analysing, verifying

Additive manufacturing (AM) is one of the most trending areas in production that allows creating three-dimensional objects according to a predetermined design. AM finds its application in all kinds of niches, from medicine to the aerospace industry, although there are still several technological barriers that must be addressed. For example, monitoring techniques that guarantee decision-making to guarantee

Concerns about environmental degradation and fossil fuel depletion have led to the advent of energy-aware manufacturing and material handling processes in factories and warehouses. However, as the transition to eco-friendly material handling by electric material handling vehicles (EMV) is progressing, the use of electric forklifts (EFs) remains a challenge, as these EMVs are recognized only as energy

The deep learning (DL) model has performed successfully in various fields, including manufacturing. DL models for defect image data analysis in the manufacturing field have been applied to multiple domains such as defect detection, classification, and localization. However, DL models require trade-offs in accuracy and interpretability. We use explainable artificial intelligence techniques to analyze

As one of the manufacturing industries with high energy consumption and high pollution, sand casting is facing major challenges in green manufacturing. In order to balance production and green sustainable development, this paper puts forward man–machine dual resource constraint mechanism. In addition, a multi-objective flexible job shop scheduling problem model constrained by job transportation time

Continuous casting is the most important route for the production of steel today. Due to the physical, mechanical, and chemical components involved in the production, continuous casting is a very complex process, pushing conventional methods of monitoring and control to their limits. In recent years, this complexity and the increasing global competition created a demand for new methods to monitor and

In semiconductor manufacturing, detecting defect patterns is important because they are directly related to the root causes of failures in the wafer process. The rapid advancement of the integrated circuit technology has recently led to more frequent occurrences of mixed-type defect patterns, wherein two or more defect patterns simultaneously occur in a wafer bin map. The detection of these mixed patterns

Remaining Useful Life (RUL) estimation is a fundamental task in the prognostic and health management (PHM) of industrial equipment and systems. To this end, we propose a novel approach for RUL estimation in this paper, based on deep neural architecture due to its great success in sequence learning. Specifically, we take the Transformer encoder as the backbone of our model to capture short- and long-term

In recent years, fault detection and diagnosis for industrial processes have been rapidly developed to minimize costs and maximize efficiency by taking advantages of cheap sensors and microprocessors, data analysis and artificial intelligence methods. However, due to the nonlinear and dynamic characteristics of industrial process data, the accuracy and efficiency of fault detection and diagnosis methods

To clarify the causality among process parameters is a core issue of data-driven production performance analysis and product quality optimization. The difficulty lies in accurately measuring and distinguishing direct and indirect associations of complex manufacturing systems. In this work, the nonparametric-copula-entropy and network deconvolution method is proposed for causal discovery in complex

With the increasing demand for inventive products, finding out inventive design solutions hidden in different industrial engineering domains has always been a challenge for engineers. In addition, patent documents are full of the latest inventive knowledge inside. In this paper, we rely on the assumption that an engineering problem may have an inventive practical solution in another scientific domain

This paper presents a smart supervisory framework for a single process controller, designed for Industry 4.0 shop floors. This digitization of a full supervisory suite for a single process controller enables self-awareness, self-diagnosis, self-prognosis, and self-healing (by definition, these "self" elements are missing from other supervisory frameworks diagnosing numerous controllers in parallel)

Prognostic health management minimizes system downtime and improves overall equipment effectiveness. Accurate prediction of remaining useful life (RUL) is key to prognostics. Prominent machine learning algorithms implement handcrafted feature extraction to improve RUL prediction. Deep learning automates feature extraction from raw data but requires large datasets and computationally expensive fine-tuning

The processing quality of the grooves of a nuclear-fuel rod will directly affect the quality of the finished nuclear-fuel rod. Due to the highly reflective, microscopic, and annular characteristics of nuclear-fuel rod grooves, it has been quite challenging to realize imaging and microscopic defect detection for these grooves. In this work, a machine vision-based defect detection system was developed

Coverage analysis is essential for many coverage tasks (e.g., robotic grit-blasting, painting, and surface cleaning) performed by Autonomous Industrial Robots (AIRs). Coverage analysis enables (1) the performance evaluation (e.g., coverage rate and operation efficiency) of AIRs for a coverage task, and (2) the configuration design of a multi-AIR system (e.g., decision on the number of AIRs to be used)

Optimization of operations and maintenance (O&M) in the industry is a topic that has been largely studied in the literature. Many authors focused on reliability-based approaches to optimize O&M, but little attention has been given to study the influence of macroeconomic variables on the long-term maintenance policy. This work aims to optimize time-based maintenance (TBM) policy in the mining industry

This paper proposes a novel data preprocessing method that converts numeric data into representative graphs (polygons) expressing all of the relationships between data variables in a systematic way based on Hamiltonian cycles. The advantage of the proposed method is that it has an embedded feature extraction capability in which each generated polygon depicts a class-specific representation in the data

Demand forecasts are used as input to planning activities and play an important role in the management of fundamental operations. Accurate demand forecasting is an important information for many organizations. It provides information for each stage of inventory management. In this study, multiple linear regression analysis, multiple nonlinear regression analysis, artificial neural networks and support

Maintenance is important for the service of products and it is different from repair because repair focuses on the time node when the products break down, which is a qualitative problem, but maintenance pays more attention to the continuity of machine’s work, and thus it is not a qualitative problem. Nevertheless, almost all the study methods are of qualitative methods because they only qualitatively

Early cost estimation of machined parts is difficult as it requires detailed process information that is not usually available during product design. Parametric methods address this issue by estimating machining time from predictors related to design choices. One of them is complexity, defined as a function of dimensions and tolerances from an analogy with information theory. However, complexity has

Task-Parameterized Learning from Demonstrations (TP-LfD) is an intelligent intuitive approach to support collaborative robots (cobots) for various industrial applications. Using TP-LfD, human’s demonstrated paths can be learnt by a cobot for reproducing new paths for the cobot to move along in dynamic situations intelligently. One of the challenges to applying TP-LfD in industrial scenarios is how

The article presents a new thermo-mechanical machining method for the manufacture of long low-rigidity shafts which combines straightening and heat treatment operations. A fixture for thermo-mechanical treatment of long low-rigidity shafts was designed and used in tests which involved axial straightening of shafts combined with a quenching operation (performed to increase the corrosion resistance of

In the aerospace industry, the Automated Fiber Placement process is an established method for producing composite parts. Nowadays the required visual inspection, subsequent to this process, typically takes up to 50% of the total manufacturing time and the inspection quality strongly depends on the inspector. A Deep Learning based classification of manufacturing defects is a possibility to improve the

The Internet of Vehicles (IoV) is a communication paradigm that connects the vehicles to the Internet for transferring information between the networks. One of the key challenges in IoV is the management of a massive amount of traffic generated from a large number of connected IoT-based vehicles. Network clustering strategies have been proposed to solve the challenges of traffic management in IoV networks

Industrial robots play an important role in the milling of large complex parts. However, the robot is less rigid and prone to vibration-related problems; chatter, which affects machining quality and efficiency, is more complex and difficult to monitor. In this paper, a variational mode decomposition-support vector machine (VMD-SVM) model based on information entropy (IE) is built to detect chatter

During the last five decades, manufacturing has radically changed not only due to the technology development but also because of the new market and environmental requirements. Nowadays, companies are focused not only on cost-reduction and effectiveness or realized processes but also on reducing the negative impact on natural and social environment. Therefore, the maintenance is also transforming its

WAAM has been proven a promising alternative to fabricate medium and large scale metal parts with a high depositing rate and automation level. However, the production quality may deteriorate due to the poor deposited layer surface quality. In this paper, a laser sensor based surface roughness measuring method was developed for WAAM. To improve the surface integrity of deposited layers by WAAM, different

Nowadays, industrial enterprises are equipped with sophisticated equipment, diagnostics and prediction of the state of which is an urgent task. The article presents the developed system for diagnostics of industrial equipment based on the methodology for analyzing failure modes, their influence and the degree of AMDEC criticality (l'Analyse des Modes de Défaillances, de leurs Effets et de leur Criticité)

Cloud manufacturing (CM) is a new networked manufacturing model that delivers various on-demand manufacturing capabilities to the consumers from the providers. In this model, the provider and consumer never meet each other, thus “trust” is the major enabler for starting a collaboration. In another word, a user must be sure that the requested capability will not be provided with malicious results, and

Visual sensor plays an important part in intelligentized welding systems, and the calibration of the vision sensor is the indispensable part of visual systems. Aiming at the problem of the tedious calibration process, this paper describes an automatic calibration algorithm. First, the robot motion equation and the motion range constraint equation are proposed to ensure that the collected images of

Interactions between light and matter during short-pulse laser materials processing are highly nonlinear, and hence acutely sensitive to laser parameters such as the pulse energy, repetition rate, and number of pulses used. Due to this complexity, simulation approaches based on calculation of the underlying physical principles can often only provide a qualitative understanding of the inter-relationships

In the last decade, collaborative assembly systems (CAS) are becoming increasingly common due to their ability to merge the flexibility of a manual assembly system with the performance of traditional robotics. Technical constraints, e.g., dedicated tools or resources, or performance requirements, e.g., throughput, could encourage the use of a CAS built around a multi-robot and multi-operator layout

In this study, the innovative compact heat exchanger (CHE) newly designed and manufactured using metal additive manufacturing technology were numerically and experimentally investigated. Some experiments were carried out to determine the hot water (\(hw\)) and cold water (\(cw\)) outlet temperatures of CHE. As a result of the CFD analysis, the average outlet temperatures of the \(hw\) and \(cw\) flow

The ceramic membrane plays an important role in the wastewater disposal industry. The serial number engraved on each ceramic membrane is an essential feature for identification. Here, an automatic inspection system for serial numbers of ceramic membranes is proposed to replace the manual inspection. To the best of our knowledge, this is the first attempt to automatically inspect serial numbers of ceramic

Cyber-Physical System (CPS) is one of the most promising directions of Industry 4.0 smart manufacturing. Abundant manufacturing data and information are available for decision-makers in real-time thanks to the application of various frontier technologies in CPS. However, the inherent complexity and uncertainty of manufacturing optimization still plague scholars and practitioners and impede further

The minimization of surplus components with normal dimensional distributions while making selective assemblies was the only objective considered in the previous research works carried out by various researchers in different periods. Seldom works have been found on selective assembly by considering all dimensional distributions. In this proposed work, a novel method is developed for making assemblies

Virtual commissioning is a key technology in Industry 4.0 that can address issues faced by engineers during early design phases. The process of virtual commissioning involves the creation of a Digital Twin—a dynamic, virtual representation of a corresponding physical system. The digital twin model can be used for testing and verifying the control system in a simulated virtual environment to achieve

Particle swarm optimization (PSO) and differential evolution (DE) are two efficient meta-heuristic algorithms, achieving excellent performance in a wide variety of optimization problems. Unfortunately, when both algorithms are used to solve complex problems then they inevitably suffer from stagnation, premature convergence and unbalanced exploration–exploitation. Hybridization of PSO and DE may provide

This paper proposes a moving window recursive sparse principal component analysis (MWRSPCA)-based online fault monitoring scheme, aim at providing an online fault monitoring solution for large-scale complex industrial processes (e.g., chemical industry processes) with time-varying and dynamically changing characteristics. It establishes a sparse principal component analysis (SPCA) model based on the

In most cases, designers must manually specify geometric tolerance types and values when designing mechanical products. For the same nominal geometry, different designers may specify different types and values of geometric tolerances. To reduce the uncertainty and realize the tolerance specification automatically, a tolerance specification method based on machine learning is proposed. The innovation

The dynamic production scheduling is a very complex process that may arise from the occurrence of unpredictable situations such as the arrival of new orders besides the ones already accepted. As a consequence, companies may often encounter several difficulties to make decisions about the new orders acceptance and sequencing along with the production of the existing ones. With this recognition, a mathematical

Jaya algorithm (JAYA) is a recently developed metaheuristic algorithm for global optimization problems. JAYA has a very simple structure and only needs the essential population size and terminal condition for solving optimization problems. However, JAYA is easy to get trapped in the local optimum for solving complex global optimization problems due to its single learning strategy. Motivated by this

The use of expert knowledge by manufacturing companies to support everyday activities has become an emerging practice thanks to the new knowledge management tools. A big set of knowledge is available in the organizations but its profitable use to solve problems and assist decision making is still a challenge. This is the case of CAM operations or preprocessing steps for which various works have been

In subtractive manufacturing, differences in machinability among batches of the same material can be observed. Ignoring these deviations can potentially reduce product quality and increase manufacturing costs. To consider the influence of the material batch in process optimization models, the batch needs to be efficiently identified. Thus, a smart service is proposed for in-situ material batch identification

For improving manufacturing efficiency and minimizing costs, design for additive manufacturing (AM) has been accordingly proposed. The existing design for AM methods are mainly surrogate model based. Due to the increasingly available data nowadays, machine learning (ML) has been applied to medical diagnosis, image processing, prediction, classification, learning association, etc. A variety of studies