In the era of Industry 4.0, the ease of access to precise measurements in real-time and the existence of machine-learning (ML) techniques will play a vital role in building practical tools to isolate inefficiencies in energy-intensive processes. This paper aims at developing an abnormal event diagnosis (AED) tool based on ML techniques for monitoring the operation of industrial processes. This tool

Additive Manufacturing (AM) is becoming data-intensive. The ability to identify Data Analytics (DA) opportunities for effective use of AM data becomes a critical factor in the success of AM. To successfully identify high-potential DA opportunities in AM requires a set of distinctive interdisciplinary knowledge. This paper proposes a methodology that enables collaborative knowledge management for identifying

The variant design node configuration is an effective method to solve a trade-off that exist in mass customization production between the requirements of product diversification and the product’s cost and delivery time. However, configuration changes in a product development network may generate an avalanche effect of design change propagation, which can consume substantial design resources. In order

The traceability of products in supply chains is crucial. The low-cost information collection system, tools, and transparent information proposed in this study were used to effectively manage and assign work on clothing production lines. In this system, each manufactured product was assigned a unique identification number. Information collection tools and collected data were continuously used in the

A major goal of materials design is to find material structures with desired properties and in a second step to find a processing path to reach one of these structures. In this paper, we propose and investigate a deep reinforcement learning approach for the optimization of processing paths. The goal is to find optimal processing paths in the material structure space that lead to target-structures,

In furtherance of emerging research within smart production planning and control (PPC), this paper prescribes a methodology for the design and development of a smart PPC system. A smart PPC system uses emerging technologies such as the internet of things, big-data analytics tools and machine learning running on the cloud or on edge devices to enhance performance of PPC processes. It achieves this by

Autonomous multi-robot optical inspection systems are increasingly applied for obtaining inline measurements in process monitoring and quality control. Numerous methods for path planning and robotic coordination have been developed for static and dynamic environments and applied to different fields. However, these approaches may not work for the autonomous multi-robot optical inspection system due

Tool breakage in manufacturing procedures can damage machined surfaces and machine tools. It is crucial to detect tool breakage in time and promptly respond to it. Due to the safety restrictions imposed in production, failure samples are significantly scarcer than normal samples, and this disequilibrium results in difficulty of failure detection. Therefore, we propose a new imbalanced data learning

Self-configuration is the preparation required to facilitate smart-manufacturing (SM) with the inputs derived without user intervention for engineering applications. Thus, it is vital for achieving the highest maturity level of SM technologies. In context, digital twin (DT) is an advanced virtual factory with simulation as its core technical functionality. However, the requirement of several inputs

Industry 4.0 (I4.0) brings together new disruptive technologies, increasing future factories’ productivity. Indeed, the control of production processes is fast becoming a key driver for manufacturing operations. Manufacturing control systems have recently been developed for distributed or semi-heterarchical architectures, e.g., holonic systems improving global efficiency and manufacturing operations’

The number of published manufacturing science digital articles available from scientific journals and the broader web have exponentially increased every year since the 1990s. To assimilate all of this knowledge by a novice engineer or an experienced researcher, requires significant synthesis of the existing knowledge space contained within published material, to find answers to basic and complex queries

Traditional inspection systems with a single light source are not efficient at detecting a few particular defects with a single inspection. Unlike before, the multi-light source inspection environment allows us to extract more different defects in a piece of work, depending on what we are working on with varying sources of light. We proposed the formulation of the multi-lights source lighting strategy

The COVID 19 pandemic, fluctuating demand, market uncertainty and the emergence of new technologies explain the need for a more flexible and agile supply chain. In fact, several important factors should be taken into account in the process of building an adaptive and reconfigurable supply chain. Reconfigurability is used to measure quantitatively the capability of supply chain to change easily their

In rapid hot-embossing of microarray products, sensors accuracy drifts, mechanical wears and environmental changes produce the nonlinear relationship between micro-forming accuracy and process parameters. Generally, the process parameters need to be adjusted according to ex-situ detection and on-spot experiences, leading to inefficiency. Therefore, an in-process optic detection of micro-forming heights

Gear machining precision prediction is a challenging research topic because there are many influencing factors in the process of gear machining in terms of stochastic disturbance and hidden variables. To address this issue, a method that can predict gear manufacturing errors based on parameter significance estimations and probability regression is proposed in this paper. First, an adaptive machining

The transition to industry 4.0 has impacted factories, but it also affects the entire value chain. In this sense, human-centred factors play a core role in transitioning to sustainable manufacturing processes and consumption. The awareness of human roles in Industry 4.0 is increasing, as evidenced by active work in developing methods, exploring influencing factors, and proving the effectiveness of

The increasing interest towards additive manufacturing (AM) is pushing the industry to provide new solutions to improve process stability. Monitoring is a key tool for this purpose but the typical AM fast process dynamics and the high data flow required to accurately describe the process are pushing the limits of standard statistical process monitoring (SPM) techniques. The adoption of novel smart

In nowadays manufacturing, each technical assistance operation is digitally tracked. This results in a huge amount of textual data that can be exploited as a knowledge base to improve these operations. For instance, an ongoing problem can be addressed by retrieving potential solutions among the ones used to cope with similar problems during past operations. To be effective, most of the approaches for

The research and development (R&D) of the scale-up process of third-generation photovoltaics (PVs) can benefit from the emerging trends and technologies related to the Industrial Internet of Things. However, to migrate the small-scale laboratory PVs products to a larger version of the industrial scale, a processing platform is needed to design, fabricate, and test the production line. In this paper

Deep learning-based predictive quality enables manufacturing companies to make data-driven predictions of the quality of a produced product based on process data. A central challenge is that production processes are subject to continuous changes such as the manufacturing of new products, with the result that previously trained models may no longer perform well in the process. In this paper, we address

In consequence of high cost pressure and the progressive globalization of markets, blanking, which represents the most economical process in the value chain of manufacturing companies, is particularly dependent on reducing machine downtimes and increasing the degree of utilization. For this purpose, it is necessary to be able to make a real-time prediction about the current and future process conditions

A correction to this paper has been published: https://doi.org/10.1007/s10845-021-01786-z

There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, particularly for creep and fatigue. Research has shown that this difference in performance is due to the complex relationships between AM process parameters

The material removal rate (MRR) plays a critical role in the chemical mechanical planarization (CMP) process in the semiconductor industry. Many physics-based and data-driven approaches have been proposed to-date to predict the MRR. Nevertheless, most of them neglect the underlying equipment structure containing essential interaction mechanisms among different components. To fill the gap, this paper

EU regulations on \(\textit{CO}_2\) limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of

Data protection across organizations is limiting the application of centralized learning (CL) techniques. Federated learning (FL) enables multiple participants to build a learning model without sharing data. Nevertheless, there is very few research works on FL in intelligent manufacturing. This paper presents the results of an empirical study on failure prediction in the production line based on FL

Digitalization is an opportunity for manufacturing SMEs to transform, not only for the flexibility and agility, but also for improved responsiveness in meeting customer requirements. Quality, being an integral part of customer-orientation, needs to be at the forefront of transformation under digitalization. Also, the digitalization is the need of the hour for continuous improvement to avoid disparity

Ultra-precision manufacturing (UPM) is a promising manufacturing technology for fabricating micro-components and its production volume raises in the coming future due to a significant increase in the production volume for highly technological products nowadays. Therefore, UPM industries are accountable for executing sustainability practices to minimize negative environmental impacts from their manufacturing

Detection of fiber composite material boundaries and defects is critical to the automation of the manufacturing process in the aviation industry. This paper describes a process to evaluate four well-performing deep convolutional neural network models (Mask R-CNN, U-Net, DeepLab V3+, and IC-Net) for use in such a process. A custom-captured dataset of images showing fiber cut-pieces with geometrical

The aerospace industry has established the Automated Fiber Placement process as a common technique for manufacturing fibre reinforced components. In this process multiple composite tows are placed simultaneously onto a tool. Currently in such processes manual testing requires often up to 50% of the manufacturing duration. Moreover, the accuracy of quality assurance varies significantly with the inspector

Studies have shown that melt-pool characteristics such as melt-pool size and shape are highly correlated with the formation of porosity and defects in parts built with the laser powder bed fusion (L-PBF) additive manufacturing (AM) processes. Hence, optimizing process parameters to maintain a constant melt-pool size during the build process could potentially improve the build quality of the final part

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