Production (throughput) bottlenecks are the critical stations defining and constraining the overall productivity of a system. Effective and timely identification of bottlenecks provide manufacturers essential decision input to allocate limited maintenance and financial resources for throughput improvement. However, identifying throughput bottleneck in industry is not a trivial task. Bottlenecks are

On the basis of scrap metal recycling, the shredding process transforms a large variety of objects composed predominantly of steel in crunched scrap. The main equipment used in this process is called shredder: a device consisting of several hammers attached in a horizontal rotor. Due to high efforts involved in the transformation process, the maintenance is an essential aspect to ensure the continuity

Big data analytics is playing a more and more prominent role in the manufacturing industry as corporations attempt to utilize vast amounts of data to optimize the operation of plants and factories to gain a competitive advantage. Since the advent of Industry 4.0, also known as smart manufacturing, big data analytics, combined with expert domain knowledge, is facilitating ever-greater levels of speed

In the aerospace industry, automated fibre laying processes are often applied for economical composite part fabrication. Unfortunately, the current mandatory visual quality assurance process takes up to 50% of the entire manufacturing time. An automised classification of manufacturing deviations using Neural Networks potentially improves the inspection's effectiveness. Unfortunately, the automated

This paper studies the integration of production, sampling inspection and age-based maintenance planning for an unreliable production system subject to gradual deterioration. The deterioration process of the production unit has a twofold effect on its reliability and product quality. To mitigate the effects of such deterioration, an age-based major maintenance can be conducted, which denotes a perfect

Effective tool wear monitoring (TWM) is essential for accurately assessing the degree of tool wear and for timely preventive maintenance. Existing data-driven monitoring methods mainly rely on complex feature engineering, which reduces the monitoring efficiency. This paper proposes a novel TWM model based on a parallel residual and stacked bidirectional long short-term memory (PRes–SBiLSTM) network

In S-shaped specimens which are frequently used to reflect the machining ability of machine tools, the surface error refers to the distance between the points on the actual machining surface to their relevant points on the design surface. The proper measurement of this error is crucial for evaluating the machining quality of S-shaped specimens. During the process of error measurement, improper registration

Cloud Manufacturing (CM) as a successful manufacturing business model and a major driver of Industry 4.0 has attracted a lot of attention in recent years. CM idea aims to streamline the on-demand provisioning of manufacturing resources and capabilities as services, providing end-users with flexible and scalable services accessible through global networks. This idea created many opportunities and challenges

Human–robot collaboration (HRC) has attracted strong interests from researchers and engineers for improved operational flexibility and efficiency towards mass personalization. Nevertheless, existing HRC development mainly undertakes either human-centered or robot-centered manner reactively, where operations are conducted by following the pre-defined instructions, thus far from an efficient integration

Today, data science presents immense opportunities by turning raw data into manufacturing intelligence in data-driven manufacturing that aims to improve operational efficiency and product quality together with reducing costs and risks. However, manufacturing firms face difficulties in managing their data science endeavors for reaping these potential benefits. Maturity models are developed to guide

Remaining useful life (RUL) prediction is a challenging task for prognostics and health management (PHM). Due to the complexity physics involved for precisely modeling the machine degradation process, learning-based data-driven methods, which learn the degradation pattern solely from the historical data without referring to physical models, have become promising alternatives to model-based prognostic

Human-Robot Collaboration (HRC) presents an opportunity to improve the efficiency of manufacturing processes. However, the existing task planning approaches for HRC are still limited in many ways, e.g., co-robot encoding must rely on experts’ knowledge and the real-time task scheduling is applicable within small state-action spaces or simplified problem settings. In this paper, the HRC assembly working

Medical innovations and patient expectations are pushing healthcare toward personalized medicine. In orthopedics, the concept of patient-specific implants could be economically realized with the use of additive manufacturing. Knee and hip replacements are some of the most common musculoskeletal procedures performed in the United States. Joint replacement implants are typically offered in standard sizes

Additive manufacturing (AM) of metal materials has attracted widespread attention and is shifting the conventional manufacturing landscape toward free-form processes. With increasing concerns about global sustainability, eco-consideration is highly encouraged to be integrated into AM processes. This review provides a comprehensive and timely discussion on the life cycle of metal parts fabricated through

This study presents a detailed analysis of the production efforts for personal protective equipment in makerspaces and informal production spaces (i.e., community-driven efforts) in response to the COVID-19 pandemic in the United States. The focus of this study is on additive manufacturing (also known as 3D printing), which was the dominant manufacturing method employed in these production efforts

Thus far, researches related to sustainable product-service system (PSS) largely investigate the interrelationships of factors with a focus on consumer, and concentrate on the requirement identification for PSS design; few address the interactions of multiple value from different stakeholders in the sustainable PSS implementation. In this paper, the multi-dimensional value elements for sustainable

To reduce the disassembly costs to enterprises and improve the disassembly efficiency of waste products, this study proposed a partial sequence-dependent disassembly line balancing problem (PSD-DLBP) and established a multi-objective mathematical model to simultaneously minimize the number of workstations, total disassembly time, idle balance index and the number of disassembly tools. Then, a Pareto-discrete

Several strategies have been detected in the extant literature to understand how Circular Economy (CE) can be pursued. Considering all the End-of-Life (EoL) management practices detected, disassembly processes have been identified as strategic. However, only scattered attempts have explored how digital technologies (specifically, simulation) can support the CE adoption, by focusing on disassembly processes

This paper presents a novel solution to predict the visual defects, one of the major criteria for analyzing manufacturability for the Laser-based Powder Bed Fusion (LPBF) process. For the existing manufacturability investigations, the key challenge is how to model the complex interrelationships among the design, process, and final quality. The recent research proposed machine learning methods to model

Many manufacturing companies experience increasing product variety, which not only leads to significantly increased operations’ costs but also to an uneven distribution of these costs across product variants. To deal with this challenge, managers need to accurately quantify the costs of product variety-induced complexity to understand its impact on company revenues. However, quantifying complexity

Since any faulty operations could directly affect the composite property, making early prognosis is particularly crucial for complex equipment. At present, data-driven approach has been typically used for fault prediction. However, for part of complex equipment, it is difficult to access reliable and sufficient data to train the fault prediction model. To address this issue, this paper takes autoclave

Improving machining performance of thin-walled parts is of great significance in aviation industry, since most aviation parts are characterized by large size, complex shape, and thin-walled structure. Machining process monitoring is the essential premise to improve the machining performance. In order to improve the machining quality and efficiency, this paper presents a position-oriented process monitoring

Energy efficient control strategies have been of recent interest in literature as promising measures to reduce the energy consumed by production equipment during interruptions of part flow. A general time-based control policy is analyzed. Machine idle times are assumed stochastic and the expected value of the energy consumed per produced part is reduced while assuring a certain target of expected production

Although ground robots have been successfully used for many years in manufacturing, the capability of aerial robots to agilely navigate in the often sparse and static upper part of factories makes them suitable for performing tasks of interest in many industrial sectors. This paper presents the design, development, and validation of a fully autonomous aerial robotic system for manufacturing industries

A goal in ultrasonic welding (USW) process monitoring is to accurately predict quality outcomes based on monitored signals. However, in most cases, knowing only that the USW process has failed is insufficient. Modern process automation should assess signal information and intercede to rectify process problems. Identification of when a process signal deviates from an acceptable final quality outcome

Controlling production and release of material into a manufacturing system effectively can lower work-in-progress inventory and cycle time while ensuring the desired throughput. With the extensive data collected from manufacturing systems, developing an effective real-time control policy helps achieving this goal. Validating new control methods using the real manufacturing systems may not be possible

As an extension of the classical job shop scheduling problem, flexible job shop scheduling problem (FJSP) is considered as a challenge in manufacturing systems for its complexity and flexibility. Meta-heuristic algorithms are shown effective in solving FJSP. However, the multiple critical paths issue, which has not been formally discussed in the existing literature, is discovered to be a primary obstacle

Large-sized product cannot be printed as one piece by a 3D printer because of the volume limitation of most 3D printers. Some products with the complex structure and high surface quality should also not be printed into one piece to meet requirement of the printing quality. For increasing the surface quality and reducing support structure of 3D printed models, this paper proposes a 3D model segmentation

Small and medium manufacturing enterprises (SMEs) often lack skills and resources required to perform in-house PHM analytics. While cloud-based services provide SMEs the option to outsource PHM analytics in the cloud, a critical limiting factor to such arrangement is the data owner’s unwillingness to share data due to data privacy concerns. In this paper, we showcase how homomorphic encryption, a cryptographic

The distinguishing property of Reconfigurable Manufacturing Systems (RMS) is that they can rapidly and efficiently adapt to new production requirements, both in terms of their capacity and functionalities. For this type of systems to achieve the desired efficiency, it should be possible to easily and quickly setup and reconfigure all of their components. This includes fixturing jigs that are used to

Uncertainty is a characteristic element in the early planning phase of assembly systems. Today, planning methods normally apply precise data and do not take uncertainty into account. In this context, customer demand is one of the most important planning parameters for the design of assembly systems, providing specifications for the quantity of products to be assembled. This parameter is essential for

With the development of Internet of Things (IoT), 5 G, and cloud computing technologies, the amount of data from manufacturing systems has been increasing rapidly. With massive industrial data, achievements beyond expectations have been made in the product design, manufacturing, and maintain process. Big data analytics (BDA) have been a core technology to empower intelligent manufacturing systems.

Degradation data have been widely used for the remaining useful life (RUL) prediction of systems. Most existing works apply a preset model to capture the degradation process and focus on the degradation process without shocks or constant shock effects. More generally, the actual degradation path is unobservable due to the existence of measurement uncertainty, which interferes with the determination

Companies with manufacturing systems that are more responsive and resilient will be able to survive or even gain market shares in the face of the unpredicted variable of an outbreak similar to the COVID-19 pandemic. Motivated by an industrial company restructuring its manufacturing system with the layout of fixed-position assembly islands (FPAI) during the COVID-19 pandemic, this paper introduces the

A shared digital twin of a manufacturing system is valuable to provide maintenance services in a collaborative manner. An accurate analytical model of the Bayesian network is crucial to depict endogenous failure mechanisms in the digital twin. Nodes’ connections in Bayesian networks correspond to a range of linear, bilinear or multilinear mappings over finite-state variables. The conditional probability

Disassembly of end-of-life products is a common step in remanufacturing and recycling. Disassembly sequence planning is the process that automatically finds the optimal sequence of components being removed. A key element of disassembly sequence planning is a suitable mathematical representation that describes the interference of any two components in a product. Previous studies on disassembly sequence

Manufacturing processes usually exhibit mixed operational conditions (OCs) due to changes in process/tool/equipment health status. Undesired OCs are direct causes of out-of-control production and thus need to be identified. Data-driven OC identification has been widely used for recognizing undesired OCs, yet most methods of this kind require labels indicating the OCs in model training. In industrial

One of the most important issues in cloud manufacturing involves obtaining an optimal manufacturing service composition solution. However, traditional manufacturing service composition methods either focused on single-task-oriented service composition or optimized solutions under a deterministic environment. In the study, a multitask-oriented manufacturing service composition (MMSC) model with two

Due date control (DDC) can be regarded as one of the competitive abilities of an enterprise. Based on advanced connected technologies implemented in Industry 4.0, DDC can collect processing information accurately and in real time to be integrated with production systems. It is an intelligent event-driven feedback control that can be designed to control process plans. In this study, a work-in-process

A smart manufacturing system (SMS) is a multi-field physical system with complex couplings among various components. Usually, designers in various fields can only design subsystems of an SMS based on the limited cognition of dynamics. Conducting SMS designs concurrently and developing a unified model to effectively imitate every interaction and behavior of manufacturing processes are challenging. As

In this paper, we present a simple, easily adaptable method for collision and deadlock resolution in transport systems. This method is based on the dividing the transport road network into non-overlapping zones, which enables it to be analyzed as a system of discrete events. The method distinguishes unidirectional zones, which have the feature that the passage between these zones cannot lead to a deadlock

Digital twin (DT) technology is essential for achieving the fusion of virtual-real cyber-physical systems. Academics and companies have made great strides in the theoretical research and case studies of constructing the shop-floor digital twin (SDT), which is the premise of applying DT technology on the shop floor. A shop floor is a large complex system that involves many elements including people

In modern manufacturing, vision-based defect recognition is an essential technology to guarantee product quality, and it plays an important role in industrial intelligence. With the developments of industrial big data, defect images can be captured by ubiquitous sensors. And, how to realize accuracy recognition has become a research hotspot. In the past several years, many vision-based defect recognition

Pure reactive scheduling is one of the core technologies to solve the complex dynamic disturbance factors in real-time. The emergence of CPS, digital twin, cloud computing, big data and other new technologies based on the industrial Internet enables information acquisition and pure reactive scheduling more practical to some extent. However, how to build a new architecture to solve the problems which

Defect inspection of glass bottles in the beverage industrial is of significance to prevent unexpected losses caused by the damage of bottles during manufacturing and transporting. The commonly used manual methods suffer from inefficiency, excessive space consumption, and beverage wastes after filling. To replace the manual operations in the pre-filling detection with improved efficiency and reduced

Different formalisms and modeling frameworks have been developed to capture the discrete behavior of manufacturing systems. However a purely discrete model does not capture the continuous variables of machine-level operation. Previous research in modeling manufacturing systems has studied the quantity-quality and quantity-reliability coupling of production environments but their relationship to energy

Mass personalization is becoming a reality. It requires responsive and flexible manufacturing operations for producing individualized products in dynamic batch sizes at scale in a cost-effective way. Therefore, manufacturing systems should timely respond to meet changing demands and conditions in the factory, in the supply network, and in customer needs. However, current manufacturing systems fail

Control of production operations is one of the most cost-effective methods to improve energy efficiency in production lines. Although many research efforts have been devoted to utilizing production control to boost production energy efficiency, it is not clear how production dynamics change in reaction to the energy saving operations, which is important to capture energy saving opportunities. In this

This research aims to model manual assembly processes by parameterizing operator force readings, specifically for engine and coolant hose connections in an automotive manufacturing line. During automotive assembly, many processes are still performed manually by the human operator due to the complexity of automating the process or product with current technology. Processes include completing hose connections

Predictive maintenance (PdM) advocates for the usage of machine learning technologies to monitor asset's health conditions and plan maintenance activities accordingly. However, according to the specific degradation process, some health-related measures (e.g. temperature) may be not informative enough to reliably assess the health stage. Moreover, each measure needs to be properly treated to extract

Assembly torque monitoring is an important aspect of assembly process monitoring. Currently, in manual assembly lines, torque monitoring sensors are usually installed on assembly tools or assembly jigs. This approach is restricted by the working environment and assembly space. To this end, operators can use wearable devices containing sensors without being restricted by the tool and working spaces

In recent years, to attain a competitive edge, merely optimizing the production efficiency is no longer the whole story that the manufacturing enterprises concentrate on. Instead, the increasing energy costs are driving manufacturing enterprises to seek an energy-oriented approach further to improve the overall performance of the production systems. Therefore, an opportunistic maintenance policy under

Topology optimization has become a valuable design tool for structures to be fabricated by additive manufacturing (AM). However, during early stage design, parameters are frequently evolving, resulting in multiple similar TO runs. Especially when design for manufacturing principles expand the parameter space, this iterative process is computationally burdensome, and does not take advantage of redundant

Production processes are becoming increasingly complex and decentralized, spanning across multiple manufacturing areas, factories, and even countries. Thereby is the development of a holistic and data-continuous traceability system, which provides transparency of records for the entire manufacturing flow from raw materials to the final product, one of the main challenges. While some solutions already

Response surface modeling is an essential technique for identifying the optimal input parameters in a process, especially when the physical knowledge about the process is limited. It explores the relationships between the process input variables and the response variables through a sequence of designed experiments. Conventional response surface models typically rely on a large number of experiments