Abstract There are many efficient and effective constraint-handling mechanisms for constrained optimization problems. However, most of them evaluate all the individuals, including the worse individuals, which waste a lot of fitness evaluations. In this paper, halfspace partition mechanism based on constraint violation values is proposed. Since constraint violation information of individuals in current

Abstract A mining complex is an integrated value chain where the materials extracted from a group of mineral deposits are sent to different processing streams to produce sellable products. A major short-term decision in a mining complex is to determine the flow of materials that first includes deciding which handling facilities to send the extracted materials and then determining how to utilize the

Abstract This paper proposes a new machine learning method for fault detection using a reduced kernel partial least squares (RKPLS), in static and online forms, for handling nonlinear dynamic systems. The choice of the fault detection method has a vital role to improve efficiency and safety as well as production. The kernel partial least squares is a nonlinear extension of partial least squares. The

Abstract Tool wear monitoring has been increasingly important in intelligent manufacturing to increase machining efficiency. Multi-domain features can effectively characterize tool wear condition, but manual feature fusion lowers monitoring efficiency and hinders the further improvement of predicting accuracy. In order to overcome these deficiencies, a new tool wear predicting method based on multi-domain

Abstract AM, generally known as 3D printing, is a promising technology. Robotic AM enables the direct fabrication of products possessing complex geometry and high performance without extra support structures. Process planning of slicing and tool path generation has been a challenging issue due to geometric complexity, material property, etc. Simple and robust planar slicing has been widely researched

Abstract Although manufacturing technology has been developing rapidly, injection molding is still widely used for fabricating plastic parts with complex geometries and precise dimensions. Since the occurrence of faults in injection molding is inevitable, process optimization is desirable. Artificial intelligence (AI) methods are being successfully used for optimization in different branches of science

Abstract Sheet metal manufacturing is finding wide applications in automotive and aerospace industries. Handling of giant sheet materials in manufacturing industries is one of the key problems. Utilization of robots, viz SwarmItFIX, will address this problem and automate the fixturing process, which greatly reduces lead time and thus the production cost. Implementation of intelligence into the robots

Abstract A non-dominated sorting modified teaching–learning-based optimization (NSMTLBO) is proposed to obtain the optimum solution for a multi-objective problem related to machining Polytetrafluoroethylene. Firstly, an experimental design is done and the L27 orthogonal array with three-level of cutting speed \( \left( {V_{c} } \right) \), feed rate (f), depth of cut (ap) and nose radius \( \left(

Abstract The laser welding quality is determined by its welding statuses, and online welding statuses are depicted by the real-time signals captured from the welding process. A multiple-sensor system is designed to obtain information as comprehensive as possible for welding statuses monitoring. The multiple-sensor system includes an auxiliary illumination visual sensor system, an ultraviolet and visible

Abstract During the manufacturing of lithium-ion battery electrodes, it is difficult to prevent certain types of defects, which affect the overall battery performance and lifespan. Deep learning computer vision methods were used to evaluate the quality of lithium-ion battery electrode for automated detection of microstructural defects from light microscopy images of the sectioned cells. The results

Abstract The demand for optimization of manufacturing processes rises as a reflection of the highly competitive market environment that requires shorter lead time and lower production costs. Although some approaches to milling process optimization have been developed based on analytical model using average cutting parameters, they are not available for complex workpieces when cutting parameters are

Abstract Motivated by the increasing demand and highly customized products, accurate and up-to-date information about the manufacturing process become essential to meet these requirements. In manual assembly activities, performing theoretical planning in simulation environments is a crucial procedure to detect and avoid unreasonable assembly operations. However, the deviations between theoretical and

Abstract High-speed electrical discharge machining (EDM) is a nontraditional machining method using high electric energy to efficiently remove materials. In this paper, a novel pulse classification method was proposed based on the recurrent neural network (RNN) for high-speed EDM pulse analysis. This study is the first time that an RNN has been applied in high-speed EDM pulse analysis. Different from

Abstract Because the data generated in the complex industrial manufacturing processes is multi-sourced and heterogeneous, it brings a challenge for addressing decision-making optimization problems embedded in the whole manufacturing processes. Especially, for inconsistent lithium-ion cell screening as such a special problem, it is a tough issue to fuse data from multiple sources in a lithium-ion cell

Abstract The inspection of conductive particles after Anisotropic Conductive Film (ACF) bonding is a common and crucial step in the TFT-LCD manufacturing process since the number of high-quality conductive particles is a key indicator of ACF bonding quality. However, manual inspection under microscope is a time-consuming, tedious, and error-prone. Therefore, there is an urgent demand in industry for

Abstract In intelligent manufacturing, it is important to schedule orders from customers efficiently. Make-to-order companies may have to reject or postpone orders when the production capacity does not meet the demand. Many such real-world scheduling problems are characterised by processing times being dependent on the start time (time dependency) or on the preceding orders (sequence dependency), and

Abstract Data-based fault diagnosis is an important technology in modern manufacturing systems. However, most of these diagnosis methods assume that all the data should be identically distributed. In diagnosis tasks, this assumption means that these methods can only handle faults from the same working load. In real-world applications, the working load of the equipment varies for the different productions;

Abstract In industry 4.0, machines generate a lot of data about several kinds of events that occur in the production process. This huge quantity of information contains valuable patterns that allow prediction of important events in the appropriate instant. In this paper, we are interested in mining frequent chronicles in the context of industrial data. We introduce a general approach to preprocess

Abstract In industrial production, the characteristics of compressor vibration signal change with the production environment and other external factors. Therefore, to ensure the effectiveness of the model, the vibration signal prediction model needs to be updated constantly. Due to the complex structure of Long Short Term Memory (LSTM) network, the LSTM model is difficult to adapt to the scene of online

Abstract Efficient quality control is inevitable in the manufacturing of light-emitting diodes (LEDs). Because defective LED chips may be traced back to different causes, a time and cost-intensive electrical and optical contact measurement is employed. Fast photoluminescence measurements, on the other hand, are commonly used to detect wafer separation damages but also hold the potential to enable an

Abstract Nondestructive testing using X-ray imaging has been widely adopted in the defect detection of casting parts for quality management. Deep learning has been proved to be an effective way to detect defects in X-ray images. In this work, Feature Pyramid Network (FPN) which has been utilized broadly in many applications is adopted as our baseline. In FPN, there mainly exits two issues: firstly

Abstract As one of the demand-side programs, time-of-use (TOU) tariffs brings opportunities of maintaining power grid stability for electricity providers and chances of energy conservation for manufacturers, but it also brings challenge for enterprises to optimize scheduling schemes. This paper studies a two-stage parallel machine scheduling problem under TOU to minimize total electricity costs. The

Abstract A significant body of knowledge exists on inverse problems and extensive research has been conducted on data-driven design in the past decade. This paper provides a comprehensive review of the state-of-the-art methods and practice reported in the literature dealing with many different aspects of data-informed inverse design. By reviewing the origins and common practice of inverse problems

Abstract Due to the complexity of the high-pressure jet assisted turning, knowledge, and prediction of the cutting forces are essential for the planning of machining operations for maximum productivity and quality. However, it is well known that during processing using this procedure there are difficulties in collecting data. It is required to establish an adequate model that would make it possible

Abstract The implementation of cost leadership strategy can enable enterprises to obtain a lasting competitive advantage. In this paper, we construct a supply chain with two competing suppliers and two competing manufacturers. The suppliers act as the Stackelberg leaders, selling components to the follower manufacturers. The manufacturers use different sourcing strategies, one of which only uses the

Abstract Compacted graphite iron (CGI) plays an important role in contemporary manufacturing of automobile engine, and coated tool is the best choice for milling of CGI. But studies about the estimation of the wear of coated tool are still rare and incomplete. As tool wear is the main factor that affects the quality of machined surface, in this study, we proposed an intelligent model-adaptive neuro

Abstract In this paper, techniques for machining and micro-structuring dimples and grooves on the interior of cylinders using an active rotating tool are discussed. Microscopic dimples and grooves patterned on the inner surface of a cylinder act as lubrication and reduce friction. The active rotating tool presented here is equipped with a gap sensor that can measure the distance between the tool, workpiece

Abstract Ensemble learning method has shown its superiority in bearing fault diagnosis based on the condition based monitoring. Nevertheless, features extracted from the monitoring signals of bearing systems often contain interrelated and redundant components, leading to poor performances of the base classifiers in the ensemble. Moreover, the current ensemble methods rely on voting strategies to aggregate

Abstract With a high-speed, dynamic and continuous yarn manufacturing process, spinning production suffers from different problems of dynamic disturbances such as yarn breakage, machine breakdown, and yarn quality. Processing real-time tasks is critical for tackling these problems, except for satisfying the requirements of mass production. The existing spinning cyber-physical production systems (CPPS)

Abstract Surrogate models have been widely studied for optimization tasks in the domain of engineering design. However, the expensive and time-consuming simulation cycles needed for complex products always result in limited simulation data, which brings a challenge for building high accuracy surrogate models because of the incomplete information contained in the limited simulation data. Therefore,

The Editor-in-Chief has retracted this article (Joviæ et al. 2019) because validity of the content of this article cannot be verified. This article showed the evidence of substantial text overlap [most notably with the articles cited (Petkovic et al. 2016; Mladenovic et al. 2016)] and authorship manipulation. All authors disagree about this retraction.

Abstract The extraction of meaningful features from the monitoring of laser processes is the foundation of new non-destructive quality inspection methods for the manufactured pieces, which has been and remains a growing interest in industry. We present ConvLBM, a novel approach to monitor Laser Based Manufacturing processes in real-time. ConvLBM uses a Convolutional Neural Network model to extract

Abstract Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of visual inspection. Deep-learning methods have become the most suitable approaches for this task. They allow the inspection system to learn to detect the surface anomaly by simply showing it a number of exemplar

Abstract Owing to the increasing concerns about customer needs in the current competitive market, the identification and incorporation of customer requirements (CRs) into product configuration designs have raised the interest of both researchers and practitioners. Most of the design methodologies focus on explicit technical domains to define CRs into specific design parameters directly. However, the

Abstract Near-net-shaped jet engine blade machining process have better performance on both reducing material waste during production and improving work reliability in service, while precision machining of this blade is very challengeable and difficult due to its positioning difficulty and low stiffness. This paper propose that reasonable fixture and adaptive CNC machining technology can provide a

Abstract Deformation prediction is the basis of deformation control in manufacturing process planning. This paper presents an on-line part deformation prediction method using a deep learning model during numerical control machining process, which is different from traditional methods based on finite element simulation of stress release prior to the actual machining process. A fourth-order tensor model

Abstract Cloud manufacturing (CMfg) is a new service-oriented manufacturing paradigm in which shared resources are integrated and encapsulated as manufacturing services. When a single service is not able to meet some manufacturing requirement, a composition of multiple services is then required via CMfg. Service composition and optimal selection (SCOS) is a key technique for creating an on-demand quality

Abstract This work proposes a new methodology and mathematical formulation to address the facility layout problem. The goal is to minimise the total material handling cost subjected to production-derived constraints. This cost is a function of the distance that the products should cover within the facility. The first idea is to use the \( {\text{A}}^{ *} \) algorithm to identify the distances between

Abstract This article intends to provide a computational tool that integrates and provides optimized solutions to two interdependent problems called Optimized Billing Sequencing (OBS) and Optimized Picking Sequence (OPS). These problems are addressed separately by the existing literature and refer respectively to the optimization of billing and picking processes in a typical warehouse with low-level

Abstract The costs of preventive maintenance have been extensively studied by scholars across all preventive optimization model disciplines. However, one phenomenon fails to be fully studied: breakdown and breakdown maintenance costs. We set out to fill this gap in this study. This study considered the cost of equipment preventive maintenance, and the breakdown maintenance cost caused by an accidental

Abstract This paper describes a new transfer learning method for modeling sensor time series following multiple different distributions, e.g. originating from multiple different tool settings. The method aims at removing distribution specific information before the modeling of the individual time series takes place. This is done by mapping the data to a new space such that the representations of different

Abstract Unnatural control chart patterns (CCPs) usually correspond to the specific factors in a manufacturing process, so the control charts have become important means of the statistical process control. Therefore, an accurate and automatic control chart pattern recognition (CCPR) is of great significance for manufacturing enterprises. In order to improve the CCPR accuracy, experts have designed

Abstract Nonlinear friction is the limiting factor in using motor current signals to estimate the load of machine tools. The inertia of the axis and the positional dependency of the friction add another degree of complexity. The work focuses on industrial machining centers with ball-screw driven stages as they are used in metal cutting. The approach uses Internal low-frequency signals from the NC controller

Monitoring the performance of manufacturing equipment is critical to ensure the efficiency of manufacturing processes. Machine-monitoring data allows measuring manufacturing equipment efficiency. However, acquiring real and useful machine-monitoring data is expensive and time consuming. An alternative method of getting data is to generate machine-monitoring data using simulation. The simulation data

Prognostics and health management (PHM) technologies reduce time and costs for maintenance of products or processes through efficient and cost-effective diagnostic and prognostic activities. PHM systems use real-time and historical state information of subsystems and components to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and

This paper concerns the development of a design methodology and its demonstration through a prototype system for performance modeling and optimization of manufacturing processes. The design methodology uses a Modelica simulation tool serving as the graphical user interface for manufacturing domain users such as process engineers to formulate their problems. The Process Analytics Formalism, developed

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present