In the wake of COVID-19, the production demand of medical equipment is increasing rapidly. This type of products is mainly assembled by hand or fixed program with complex and flexible structure. However, the low efficiency and adaptability in current assembly mode are unable to meet the assembly requirements. So in this paper, a new framework of human-robot collaborative (HRC) assembly based on digital

New product development to enhance companies’ competitiveness and reputation is one of the leading activities in manufacturing. At present, achieving successful product design has become more difficult, even for companies with extensive capabilities in the market, because of disorganisation in the fuzzy front end (FFE) of the innovation process. Tremendous amounts of information, such as data on customers

Two-beam laser welding (TBLW) is an advanced process for precise, low distortion joining of cylindrical miniature parts. The process is composed of a laser source, optics and various actuators, which form a sophisticated system for control and maintenance in high volume manufacturing. A well-established method for identifying welding defects and ensuring welding quality is the monitoring of plasma

This paper presents an intelligent feature recognition method for STEP-NC-compliant manufacturing based on artificial bee colony (ABC) algorithm and back propagation (BP) neural network. In the method, after extracting the geometric and topological information from its STEP AP203 neutral file, the minimum subgraphs of a part are firstly constructed based on the concavity and convexity judgment algorithm

Since the late 2019, the COVID-19 pandemic has been spread all around the world. The pandemic is a critical challenge to the health and safety of the general public, the medical staff and the medical systems worldwide. It has been globally proposed to utilise robots during the pandemic, to improve the treatment of patients and leverage the load of the medical system. However, there is still a lack

This paper proposes a resilience dynamics modeling and control approach for a reconfigurable electronic assembly line under disruptions. A Digital Twin (DT) platform is developed as the basis for resilience analysis, and open reconfigurable architectures (ORAs) are introduced to support reconfiguration of the assembly line. The time-delays of disruptions are identified and used to characterize their

Aiming at the path planning and decision-making problem, multi-automated guided vehicles (AGVs) have played an increasingly important role in the multi-stage industries, e.g., textile spinning. We recast a framework to investigate the improved genetic algorithm (GA) on multi-AGV path optimization within spinning drawing frames to solve the complex multi-AGV maneuvering scheduling decision and path

As the manufacturing industry is approaching implementation of the 4th industrial revolution, changes will be required in terms of scheduling, production planning and control as well as cost-accounting departments. Industry 4.0 promotes decentralized production and hence, cost models are required to capture costs of products and jobs within the production network considering the utilized manufacturing

The vision-based welding status recognition (WSR) provides a basis for online welding quality control. Due to the severe arc and fume interference in the welding area and limited computational resources at the welding edge nodes, it becomes a challenge to mine the most discriminative feature contained in welding images by using a lightweight model. In this paper, we propose an improved three-dimensional

The latest developments in industry involved the deployment of digital twins for both long and short term decision making, such as supply chain management, production planning and control. Modern production environments are frequently subject to disruptions and consequent modifications. As a result, the development of digital twins of manufacturing systems cannot rely solely on manual operations. Recent

Non-numerical data, such as images and inspection records, contain information about industrial system degradation, but they are rarely used for failure prognostic tasks given the difficulty of automatic analysis. In this work, we present a novel method for prognostics using multimodal data, i.e. both numerical and non-numerical data. The proposed method is based on the development of a multi-branch

Counterfeiting is a significant problem for safety-critical systems, since cyber-information, such as a quality control certification, may be passed off with a flawed counterfeit part. Safety-critical systems, such as planes, are at risk because cyber-information cannot be provably tied to a specific physical part instance (e.g., impeller). This paper presents promising initial work showing that using

Process monitoring in additive manufacturing may allow components to be certified cheaply and rapidly and opens the possibility of healing defects, if detected. Here, neural networks (NNs) and convolutional neural networks (CNNs) are trained to detect flaws in layerwise images of a build, using labeled XCT data as a ground truth. Multiple images were recorded after each layer before and after recoating

The COVID-19 pandemic has disrupted the supply chain for personal protective equipment (PPE) for medical professionals, including N95-type respiratory protective masks. To address this shortage, many have looked to the agility and accessibility of additive manufacturing (AM) systems to provide a democratized, decentralized solution to producing respirators with equivalent protection for last-resort

With the gradual promotion and in-depth application of cloud manufacturing platform, some deep-seated problems restricting enterprises to go to the cloud are gradually exposed. Lack of trust and security of cloud manufacturing platforms are the bottleneck problems. How to ensure the credit of both trading sides and reduce the transaction risk and transaction cost are still urgent problems to be solved

Smart factory is the main keyword in the field of manufacturing processes about the fourth industrial revolution. To realize the smart factory, making all pieces of device into smart devices that are connected to the centralized system to enable a real-time exchange of information is essential. Sound can be efficient means to make devices as smart devices because sound can contain the status information

Robotic assembly plays a principal role in intelligent manufacturing and Industry 4.0. Well-informed coordination between the robotic arms and the control modules is of paramount importance in the design and planning of automated systems. Given the time-extensive nature of assembly sequence planning and the need for labor-intensive coding and coordination, knowledge-based automated systems are much-needed

The economically successful implementation of Industry 4.0 methods in industrial companies requires a structured introduction process. The main objective of such a structured implementation process is the case-specific analysis and evaluation of available Industry 4.0 methods to select the most suitable ones for an individual company. The presented methodology aims to establish a financial, strategic

The contribution examines the acceptance of Augmented Reality (AR) in assembly scenarios by a model-based approach for acceptance evaluation. After a critical literature research and analysis, a proprietary model for acceptance measurement is developed, which includes and synthesizes previous models and simplifies them considerably for the purpose of industrial assembly. Consequently, a structural

Mass customized production brings great uncertainty to the computer-aided process planning (CAPP). Current CAPP methods based on heuristic optimization assume in advance that manufacturing resources are static and make a deterministic plan that cannot cope with the uncertainty of the manufacture environment. As a promising method in solving complex and dynamic decision-making problems, deep reinforcement

Image-based registration methods have been widely used in augmented assembly systems. However, many challenges remain to be solved, such as low robustness and poor timeliness during registration. This paper presents a deep learning approach for registration. To reduce the workload of data collection, an automatic picture generation method is offered for deep learning algorithm, and a dataset is built

Brackets are an essential component in aircraft manufacture and design, joining parts together, supporting weight, holding wires, and strengthening joints. Hundreds or thousands of unique brackets are used in every aircraft, but manufacturing a large number of distinct brackets is inefficient and expensive. Fortunately, many so-called “different” brackets are in fact very similar or even identical

Automatic control systems are critical sub-systems that ensure the reliable and efficient operation of a modern manufacturing system collaborating with other sub-systems. Critical to the performance of control systems are aging actuators that will ultimately result in an unstable control process and consequently failure of the supported manufacturing system. In this paper, by using the Nyquist stability

Manufacturing systems are often prone to disruptions that break the continuity of operations and prevent them from reaching their planned performance. This paper presents a Situation-Aware Manufacturing System framework that is applied to identify and predict disruptions, to evaluate their impact and to react timely to repair the affected processes, by coupling the capabilities of contemporary Industry

With the popularity of service-oriented manufacturing mode, the customer quantities of the online manufacturing service platforms are growing exponentially. To improve the user-friendliness and convenience of online platforms, the personalized service recommendation for different customer requirement is an effective means. However, since manufacturing services usually appear in the form of composite

Ultrasonic welding is a well-known process for joining thermoplastics and has recently been introduced for joining carbon fiber reinforced polymer (CFRP) composite materials in the automotive industry. As a new joining method for CFRP materials, an understanding of the impact of the welding process on weld attributes and joint performance such as lap-shear strength is needed, as are methods to effectively

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapidly achieved global pandemic status. The pandemic created huge demand for relevant medical and personal protective equipment (PPE) and put unprecedented pressure on the healthcare system within a very short span of time. Moreover, the supply chain system faced extreme disruption as a result of the frequent and severe lockdowns across

Automated material handling systems (AMHSs) with double closed-loops and shortcuts have been widely adopted by general 300mm semiconductor fabrication enterprises due to their higher transportation efficiency, more flexible vehicle routing, and lower vehicle congestion compared with those traditional ones with single-loop. An accurate and efficient performance analysis model is crucial to quickly and

Spatiotemporal processes commonly exist in manufacturing. Modeling and monitoring such processes are crucial for ensuring high-quality production. For example, ultrasonic metal welding is an important industrial-scale joining technique with wide applications. The surfaces of ultrasonic welding tools evolve in both spatial and temporal domains, resulting in a spatiotemporal process. Close monitoring

The customer’s order (CO) issues have received growing attention in the scheduling research community. In the CO design, a customer’s order consists of several components, and the processed orders are assigned to m parallel machines. The completion time of an order is assumed at the time at which all components in a customer’s order are finished. In the published articles, the processing times of all

Previous works have shown that only relying on measurement data is generally insufficient to identify root causes of the dimensional variation for the complex manufacturing system. It is also well known that for underdetermined multistage assembly processes (MAPs), the number of measurements is less than that of process errors so that the traditional methods are not available for the variation source

The industrial internet of things allows manufacturers to acquire large amounts of data. This opportunity, assuming the right methods are available, allows manufacturers to find anomalies that arise during manufacturing system operation. Data acquired from a manufacturing system are usually in the forms of time series. This paper proposes a new method that can detect anomalies in time series data.

Freeform surfaces are widely used in many industries for various purposes, and multi-axis CNC machining is the most adopted method for machining them due to its high accuracy and flexibility. The prevailing type of cutter used in machining freeform surfaces is the so-called ball-end type, which is simply a hemisphere and thus has a constant curvature on the cutter surface. Naturally, for a complex

Joint production system control is a challenge for researchers and a daily defy for managers and practitioners. The large concern comes from the interdependence between the system states and the control actions. Several analytical models have addressed these issues but remain inefficient because they are based on many simplifying assumptions for mathematical tractability (mainly concerning the system

Motivated by a real problem, in this paper, a new multi-period production-distribution planning (PDP) for perishable products with a fixed lifetime in a seller-buyer system is developed. The objective is to maximize the seller’s profit conditional on the optimality of the buyer in a three-level supply chain (SC), including the factories, distribution centers, and retailers. The factories and distribution

Tool condition monitoring (TCM) and remaining useful life (RUL) prediction is of great practical significance for any machining process to ensure machining quality and reduce the machine tool downtime. At the standpoint of workshop management, the current TCM has two drawbacks. (i) Continuously acquiring data without distinguishing the working states of the machine tool and the machining tasks will

Fault diagnosis is an essential technique to ensure the safety in modern industry. With the development of smart manufacturing, deep learning (DL) has been widely used to handle with massive mechanical data in fault diagnosis. However, the individual DL method suffers from the low generalization ability. In this research, a new improved snapshot ensemble Convolutional Neural Network (ISECNN) is proposed

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning

Tool deterioration is a common issue in Numerical Control (NC) machining, which directly affects part quality, production efficiency and manufacturing cost. Due to the complexity of machining, multiple deterioration forms of tool are involved during the tool deterioration process, which imposes a significant challenge for tool condition prediction because of the coupling effects among different deterioration

The industrial scenario is undergoing exponential changes, mainly due to the different technologies that emerge quickly and the ever increasing demand. As a consequence, the number of processing devices and systems in the industries’ architectures is also increasing. Entities connectivity, physical/virtual joint functioning, interactivity, interoperability, self-organization, smart decision making

In the context of an automated assembly line and flexible manufacturing, good layout planning of the production facilities aids in improving production efficiency and reducing production costs. The current research on the facility layout problems mainly focuses on the static ones with the fixed flow between facilities, including the parallel row ordering problem (PROP). Based on existing research on

Concepts and expectations of cyber-physical production systems (CPPSs) underscore their advantages over the present bespoke manufacturing systems. However, transitioning from traditional manufacturing to its CPPS counterpart remains a challenge. Although a CPPS can be realized, a gap between theory and practice exists considering the response of a CPPS to system disturbances, such as machine breakdowns

Key performance indicators (KPIs) modeling and control is important for efficient design and operation of complex manufacturing production systems. This paper proposes to implement the KPI control based on KPI modeling and stochastic optimization. The KPI relationship is first approximated using ordered block model and pair-copula construction (OBM-PCC) model, which is a non-parametric model that facilitates

Remaining useful life (RUL) prediction plays an important role in increasing the availability and productivity of industrial manufacturing systems. This paper proposes a joint classification-regression scheme for multi-stage RUL prediction. First, the time domain and frequency domain features are extracted from various types of raw sensory data (e.g., acoustic, current, vibration and temperature) to

The estimation of remaining useful life (RUL) of machinery is a major task in prognostics and health management (PHM). Recently, prognostic performance has been enhanced significantly due to the application of deep learning (DL) models. However, only few authors assess the uncertainty of the applied DL models and therefore can state how certain the model is about the predicted RUL values. This is especially

In the last years, the increase of data availability together with enhanced computation capabilities empowered researchers to conceive production planning and control methods with real-time inputs. Literature is rich with techniques for using simulation to take production planning and control decisions online. However, it is generally impractical to test these approaches on real systems, and experiments

Facility layout problems focus on the assignment of departments into the facility layout by considering the minimization of total costs. A well-designed facility result in efficient material flows in transportation. For this reason, the problem should be considered with the changes in demands that cause different material flows between departments. While the aim of the static facility layout problem

An inventory control problem of a hybrid manufacturing/remanufacturing system under stochastic demand is studied. The paper proposes a successive substitution strategy between prime, budget and low budget segments of a market. Meaning a manufactured product may substitute for a remanufactured product at a discounted rate. The success of this proposal proportionally is dependent on the discount rate

Today, due to increased market competition, the integration of production and distribution decisions into the supply chain leads to efficiency improvements. Therefore, production routing models have been developed to optimize production and distribution, simultaneously. In recent years, since, the product life cycle has become shorter than in the past, product return policies with fast response times

The paper proposes an energy consumption cross-level model for a demand driven machine tool working in a manufacturing system. The aim is to optimize performance of manufacturing system engaging more than one organizational level, in this case the machine and the system levels. The paper exploits a former study on Pareto optimal Minimum Energy-Time functions, representing the best possible machine

Recently, as the development of information technologies and personalized needs, the Industrial Internet platform based manufacturing service collaboration becomes the main method for manufacturing collaboration, where multiple interest-independent stakeholders involve. However, the distrust between stakeholders and the platform (distrust among collaborators and the doubts of data accuracy) hinders

Product design information represents not only the carrier of design but also the significant digital assets of businesses. At present, manufacturing is facing an environment with mass, fragmented, real-time, and multi-scene digital information in the process of product design. To improve the availability of information resources and the efficiency of information reuse as well as to achieve the sharing

Digital twin takes Industrial Internet as a carrier deeply coordinating and integrating virtual spaces with physical spaces, which effectively promotes smart factory development. Digital twin-based big data learning and analysis (BDLA) deepens virtual and real fusion, interaction and closed-loop iterative optimization in smart factories. This paper proposes a digital twin-based big data virtual and

In a manufacturing facility, the decision maker considers customer satisfaction as an essential priority in which customer due dates should be met. The cell loading, scheduling and the total manpower are considered among the main decisions in a manufacturing system. Three performance measures are studied in this paper and they are the number of tardy jobs, total manpower, and average flow time. Two

A new model for multi-plant production planning is developed. As the important actual features of some manufacturers, non-repeated setup and aperiodic shipment are appropriately introduced into the multi-plant production planning model and the corresponding constraints are accurately linearized. The new model is also applicable in the case of periodic shipment or backorder prohibition. Its effectiveness

Laser powder bed fusion (LPBF) is a technique of additive manufacturing (AM) that is often used to construct a metal object layer-by-layer. The quality of AM builds depends to a great extent on the minimization of different defects such as porosity and cracks that could occur by process deviation during machine operation. Therefore, there is a need to develop new analytical methods and tools to equip

Personalized production has emerged as a result of the increasing customer demand for more personalized products. Personalized production systems carry a greater amount of uncertainty and variability when compared with traditional manufacturing systems. In this paper, we present a smart manufacturing system using a multi-agent system and reinforcement learning, which is characterized by machines with