In recent years, as one of the new advanced manufacturing modes, cloud manufacturing has been received wide attentions around the world. The technology of cloud manufacturing intergrades the services-oriented techniques as well as manufacturing processes based on cloud computing. With the aid of the cloud computing platforms, the manufacturing services are provided in manufacturing clouds. However

Wafer bin maps (WBM) provides crucial information regarding process abnormalities and facilitate the diagnosis of low-yield problems in semiconductor manufacturing. Most studies of WBM classification and analysis apply a statistical-based method or machine learning method operating on raw wafer data and extracted features. With increasing WBM pattern diversity and complexity, the useful features for

The fault diagnostics of rotating components are crucial for most mechanical systems since the rotating components faults are the main form of failures of many mechanical systems. In traditional diagnostics approaches, extracting features from raw input is an important prerequisite and normally requires manual extraction based on signal processing techniques. This suffers of some drawbacks such as

Decision rules for machining method chains mined from historical machining documents can help technologists quickly design new machining method chains. However, the main factor that limits the practical application of existing rough set models is that the boundary regions are too large. Therefore, a decomposition-reorganization method (DRM) is proposed to mine rules for machining method chains. First

Research on scheduling problems is an evergreen challenge for industrial engineers. The growth of digital technologies opens the possibility to collect and analyze great amount of field data in real-time, representing a precious opportunity for an improved scheduling activity. Thus, scheduling under uncertain scenarios may benefit from the possibility to grasp the current operating conditions of the

In spite of many efforts made a complete model of machine spinning processes, due to its complexity, multidimensionality of the decision space and the present state of knowledge, is unachievable. The paper addresses the issues of constructing a local process model to enable the search for a locally optimal course of the process, within a short time and with the cost as low as possible. Comparison was

Today, the companies apply lean or customized production methods, which enable the production of different kinds of products in small quantities, to meet different customer demands. But, the increase in the product variety leads to an increase in the number of setups and thus production time. The companies aim to reduce the setup time by improving activities and by eliminating the problems causing

In semiconductor manufacturing industry, traditional scheduling rules are not conducive to improving production capacity to autonomously adjust based on real-time status. To fill this gap, this study proposes a dynamic dispatching rule based on self-organization (DDRSO) to autogenerate optimal scheduling scheme through mechanisms of interaction, coordination and competition. Besides, an extended DDRSO

Probe card that serves as the carrier of die and the transmitter of information is an indispensable test interface for integrated circuit testing. The probe card extracts the electrical signal of chip and sends it to the prober to screen defectives. In the process of interface transmission, signal disturbance or attenuation will lead to functional errors, and the output result will be different from

In this paper, an automated layer defect detection system for construction 3D printing is proposed. Initially, a step-by-step procedure is implemented to develop a deep convolutional neural network that receives images as input and is able to distinguish concrete layers from other surrounding objects through semantic pixel-wise segmentation. Using data augmentation techniques, 1M labeled images are

Production control systems (PCSs) control the flow of jobs in a production system. The selection of a suitable PCS in the context of make-to-order (MTO) is challenging, due to the characteristics of MTO businesses and the number of parameters or factors that comprise a PCS. The literature that compares PCSs in the MTO context reported contradictory results. In fact, there is a gap in the literature

A supply chain network architecture is a key element of designing and modeling a supply chain to better understand the cost and time associated with the distribution of products with available resources and market locations. Due to the large size of combinations for product design and supplier choices; descriptive, predictive and prescriptive analytics are needed to design, control and then improve

During the batch assembly analysis of linear axis of machine tool, assembly quality evaluation is crucial to reduce assembly quality fluctuations and improve efficiency. This study presented a data-driven modeling approach for evaluating assembly quality of linear axis based on normalized mutual information and random sampling with replacement (NMI-RSWR) variable selection method, synthetic minority

In this paper, the complex control dynamics of a predator–prey Lotka–Volterra chaotic system are studied. The main purpose is to control the chaotic trajectories of two-predator one-prey system which was introduced by Samardzija and Greller (Bull Math Biol 50(5):465–491. https://doi.org/10.1007/BF02458847, 1988). Lyapunov based nonlinear control and sliding mode control methods are used. The other

With the increasing market features of globalization, service and customization, the way manufacturers conduct manufacturing business is changing. Under this background, Cloud Manufacturing (CMfg) emerges as a new networked manufacturing model. However, CMfg is immature in many aspects, especially in exception handling of service composition execution. Due to the complexity of the enterprise manufacturing

With the grouting material as focus, this study aims to guarantee that the joint with tenon and mortise constructing a metro station has the necessary waterproof performance, which shall not be poorer than the mechanical properties of the concrete with joints itself, and fine structural integrity. With groutability at low-temperature and available operational time as basis, low-temperature epoxy was

After publication, the authors realized that they failed to z-normalize the subsequences for BFDD, making it perform much worse than a correct implementation, such as detection on xmitdb_1108_2.

Industrial environments such as manufacturing and transportation industries usually involve fleets of identical systems that must carry out several missions interspersed with scheduled finite breaks. Given the limited amount of maintenance resources and time available, only a restricted number of maintenance actions can be performed on selected components to ensure a pre-specified performance level

Companies are seeking more and more to offer customized goods and services to customers to be able to satisfy their needs. Several methods emerged to fulfill the needs of customization without affecting the performance of the company. Modularity has been considered as an effective method to address the challenges regarding variety management in the product and service domain. It has been addressed

As a rapid developing additive manufacturing (AM) technology, selective laser melting (SLM) provides a promising way for intelligent manufacturing. The SLM part quality depends largely on the process parameters in the manufacturing process. Therefore, understanding the relationships between the input process parameters and the output part performances is critical to improve the part quality. In this

The surface quality of tungsten heavy alloy parts has an important influence on its service performance. The accurate on-line prediction of surface roughness in ultra-precision cutting of tungsten heavy alloy has always been the difficulty of research. In this paper, the ultrasonic elliptical vibration cutting technology is used for ultra-precision machining of tungsten heavy alloy. Based on the idea

Tool condition monitoring (TCM) in numerical control machines plays an essential role in ensuring high manufacturing quality. The TCM process is conducted according to the data obtained from one or more of a variety of sensors, among which acoustic sensors offer numerous practical advantages. However, acoustic sensor data suffer from strong noise, which can severely limit the accuracy of predictions

With the advance in Industry 4.0, smart industrial monitoring has been proposed to timely discover faults and defects in industrial processes. Steel is widely used in manufacturing equipment, and steel surface defect inspection is of great significance to the normal operation of steel equipment in manufacturing workshops. In steel defect inspection systems, industrial inspection robots generate images

Variation propagation modelling in multistage machining processes through use of analytical approaches has been widely investigated for the purposes of dimension prediction and variation source identification. Yet the variation prediction of complex features is non-trivial task to model mathematically. Moreover, the application of the variation propagation approaches and associated variation source

The buffer allocation problem (BAP) aims to determine the optimal buffer configuration for a production line under the predefined constraints. The BAP is an NP-hard combinatorial optimization problem and the solution space exponentially grows as the problem size increases. Therefore, problem specific heuristic or meta-heuristic search algorithms are widely used to solve the BAP. In this study two population-based

To adapt to the flexibility characteristics of modern manufacturing enterprises and the dynamics of manufacturing subsystems, promote collaboration in manufacturing functions, and allocate production resources in a reasonable manner, a mathematical model of integrated process planning and scheduling (IPPS) problems was developed to optimize the global performance of manufacturing systems. To solve

The modelling of machining processes by means of machine-learning algorithms is still based on principles that are especially adapted to mechanical approaches, in which very few inputs are varied with little repetition of experimental conditions. These principles might not be ideal to achieve accurate machine-learning models and they are certainly not aligned with the practicalities of industrial machining

The incipient bearing fault diagnosis is crucial to the industrial machinery maintenance. Developed based on the blind source separation, blind source extraction (BSE) has recently become the focus of intensive research work. However, owing to certain industrial restrictions, the number of sensors is usually less than that of the source signals, which is defined as an underdetermined BSE problem to

Identifying the optimal sequence of joining is an exhaustive combinatorial optimization problem. On each assembly, there is a specific number of weld points that determine the geometrical deviation of the assembly after joining. The number and sequence of such weld points play a crucial role both for sequencing and assembly planning. While there are studies on identifying the complete sequence of welding

A multistage manufacturing process (MMP) consists of several consecutive process stages, each of which has multiple machines performing the same functions in parallel. A manufacturing path (simply referred to as path) is defined as an ordered set indicating a record of machines assigned to a product at each process stage of an MMP. An MMP usually produces products through various paths. In practice

Accurate identification of rolling bearing faults is quite significant for the stable operation of mechanical systems. However, for practical diagnosis issues, it is difficult to obtain abundant labeled data due to the change of operating conditions and complex working environment, which puts forward higher requirements on the ability of the diagnosis methods. To tackle the mentioned problem, a novel

The order batching problem is a combinatorial optimization problem that arises in the warehouse order picking process. In the order batching problem, the aim is to find groups of orders and picking routes of these groups to minimize distance travelled by the order picker. This problem is encountered especially in manual order picking systems where the capacity of picking vehicle is limited. Solving

Chatter in machining results in poor workpiece surface quality and short tool life. An accurate and reliable chatter detection method is needed before its complete development. This paper applies a novel p-leader multifractal formalism for chatter detection in milling processes. This novel formalism can discover internal singularities rising on unstable signals due to chatter without prior knowledge

Solar cells represent one of the most important sources of clean energy in modern societies. Solar cell manufacturing is a delicate process that often introduces defects that reduce cell efficiency or compromise durability. Current inspection systems detect and discard faulty cells, wasting a significant percentage of resources. We introduce Cell Doctor, a new inspection system that uses state of the

In this study, an indirect tool monitoring was developed based on the installation of a gap sensor in measuring the signal related to the tool behavior during the drilling process. Eleven types of twist drills with different tool conditions were utilized to differentiate the sensorial signals based on the tool states. A statistical analysis was conducted in the signal processing, by extracting the

The acceptance of the machined surfaces not only depends on roughness parameters but also in the flatness deviation (Δfl). Hence, before reaching the threshold of flatness deviation caused by the wear of the face mill, the tool inserts need to be changed to avoid the expected product rejection. As current CNC machines have the facility to track, in real-time, the main drive power, the present study

This paper suggests a novel robust formulation designed for optimizing the parameters of the turning process in an uncertain environment for the first time. The aim is to achieve the lowest energy consumption and highest precision. With this aim, the current paper considers uncertain parameters, objective functions, and constraints in the offered mathematical model. We proposed several uncertain models

The machining processes on the advanced machining workshop floor are becoming more sophisticated with the interdependent intrinsic processes, generation of ever-increasing in-process data and machining domain knowledge. To manage and utilize those above effectively, an industrial dataspace for machining workshop (IDMW) is presented with a three-layer framework. The IDMW architecture is Schema Centralized–Data

The electrode state is an important factor affecting the welding quality in the process of resistance spot welding, but there is still lack of an effective method to monitor the electrode wear state. In this paper, a novel online monitoring method of electrode wear state is proposed by exploring the variation pattern of dynamic resistance. The evaluation method of time series similarity is important

Process parameters of injection molding are the key factors affecting the final quality and the molding efficiency of products. In the traditional automatic setting of process parameters based on case-based reasoning, only the geometric features of molds are considered, which may not be the representative feature of products and cause the reasoning process to fail. This problem of failure manifests

ABC analysis is an efficient and easy-to-use methodology to classify inventory based on a single or multi-criteria basis that may consist of thousands of items. The first study by Dickie (Fact Manag Maint 109(7):92–94, 1951), based on a single criterion, is considered to be limited now. New studies focus on Multi-Criteria-Inventory Classification (MCIC) since such an extension of the criteria fits

One of the most important parameters in machining process is tool wear. Thus, monitoring the wear of cutting tools is essential to ensure product quality, increase productivity, reduce environmental impact and avoid catastrophic damages. As wear is related to the vibrations of the process, the vibration signal is commonly used to monitor the process non-intrusively. Traditional wear monitoring techniques

A network of randomly deployed sensor nodes which shares limited resources like bandwidth, buffer, queue, and battery powered nodes is known as wireless sensor network. Such network must have energy, to avoid the chances of congestion because congested network degrades the performance of network. Congestion may occur due to several reasons like data packet collision, transmission channel contention

The research on the robotic assembly line balancing problem (RALBP) was originated for the first time nearly three decades ago. This problem is under the umbrella of the assembly line balancing problem in which robots and automated equipment are employed to take on human workers’ roles to form a flexible assembly line. In this review paper, the development and generalisation throughout the time of

Achieving high processing quality for chemical mechanical planarization (CMP) in semiconductor manufacturing is difficult due to the distinct process variations associated with this method, such as drift and shift. Run-to-run control aims to maintain the targeted process quality by reducing the effect of process variations. The goal of controller learning is to infer an underlying output–input reverse

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

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

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

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

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

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

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

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

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

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

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

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

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

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