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Textile production is a very complex industrial process, whose planning still depends on experts' knowledge and experience. With traditional techniques, a great many process parameters have to be repeatedly computed and the optimization of process parameters is also getting more and more difficult. However the proliferation of a huge mass of data from real production has been creating many new opportunities for those working in textile science, engineering and business. The field of data mining (DM) and knowledge discovery from database (KDD) has emerged as a new discipline in engineering and computer science. This paper investigates data mining methods from the industrial database, and presents a novel DM-based intelligent model (DMIM) for worsted process decisions through an integral application of case-based reasoning (CBR) and artificial neural network (ANN) techniques. First, from the rich existing process database, CBR is able to retrieve and recommend a similar process case as a process template; then, by means of modification on these parameters in the existing cases, ANN model is used to predict the yarn quality and make the best process decision. The basic concept and system modeling are presented in this paper. An applied case with DMIM is also given to demonstrate that the best process decision can be made and important process parameters such as those for raw materials can be optimized.

This paper aims to study frictional characteristics of thin-walled tubes in the liquid impact forming (LIF) process.

LIF experiments under various impacting velocities were performed on SUS304 stainless steel tubes with various guiding lengths on a custom-designed measurement system to investigate the effects of impacting velocity and guiding length on the coefficient of friction (COF) in the guiding zone.

The results indicate that the COF changes dynamically in the guiding zone and decreases with the deformation process. The reduction range of the COF is wider in LIF than in both the conventional and pulsating hydroforming (THF), which may be contributed to the impacting velocities in a short time. Moreover, the COF decreases faster in the first half of the LIF process than in the second half. Under different impacting velocities and guiding lengths, the decreasing rate of the COF in the first half is more sensitive and obvious than that in the second half.

A method for determining the COF in the guiding zone in LIF is proposed and the frictional characteristics in LIF are studied. Comparing the COF of tubes in conventional THF, pulsating THF and the LIF process is valuable for improving and predicting the tubular formability in various hydraulic environments for industrial production.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2019-0269

Extensive efforts have been conducted on the real-time strategy (RTS) games. The purpose of this paper is the specific artificial intelligence (AI) challenges posed by RTS games; non-player character (NPC) is started out by collecting game-map resources to build up defenses and attack forces, to upgrade combat deployment.

The authors used weak AI fuzzy theory as the foundation for tunable development. With the fuzzy theory, the AI was more humanistic in its judgment process.

Well-developed AIs have been used brilliantly in various aspects in RTS games, especially in those developed by large production teams. For small production teams, how to develop an AI system in less time and at a lower cost is extremely important.

This study aimed to develop a system using player unit threat levels for NPC deployment and arrangement so that the further strategy would be adopted for NPCs in response to player actions.

The RTS games would become more challenging for players to play.

A kind of novel heterocyclic bisquaternary ammonium salt (MBQA) was successfully synthesised with metronidazole as matrix and dichloroethyl ether as the link agent. Weight loss measurement, potentiodynamic polarisation curves, electrochemical impedance spectroscopy and atomic force microscopy were used to evaluate the corrosion inhibiting performance of MBQA in simulated oilfield water. Experimental data revealed that MBQA acted as an inhibitor in the acidic environment and, furthermore, the compound was a mixed‐type inhibitor. It was found that inhibition efficiency increased with an increase in MBQA concentration at different temperatures. The process of inhibition was attributed to the formation of an adsorbed film on the metal surface, which protected the metal against corrosive agents.

The purpose of this paper is to study the corrosion behaviors of X70 steel under direct current (DC) interference at 0-1,200 A/m² in simulated soil solution.

The Tafel polarization curves of X70 steel under DC interference were tested using electrochemical method, the corrosion rate was calculated using weight-loss method and the change in steel surface was analyzed by optical microscopy.

The results showed that E-I polarization curves under 200-1,200 A/m² interference were linear; with an increase in the DC density, the corrosion potential of X70 steel shifted positively, solution pH after the weight-loss tests increased and corrosion rate increased linearly. A mathematical relationship between polarization resistance Rp and current density was established. Corrosion morphology indicated that pitting corrosion and crevice corrosion occurred on the X70 steel under DC interference in simulated soil solution.

All tests were conducted at a relative higher DC density (200-1,200 A/m²). The linear fitting method is proposed to fit data of Tafel polarization curves under DC interference. This study provides guidelines for safe operation of X70 steel pipelines.

This paper explores the impact of both government subsidies and decision makers' loss-averse behavior on the determination of transportation build-operate-transfer (BOT) concession periods based on cumulative prospect theory (CPT). The prospect value of a transportation project under traffic risk can be formulated according to the value function for gains and losses and the decision weight for gains and losses. As an extra income for investors, government subsidy is designed for highly risky aspects of BOT transportation projects: uncertain initial traffic volumes and fluctuating growth rates.

A decision-making model determining the concession period of a transportation BOT project is proposed by using the Monte-Carlo simulation method based on CPT, and the effects of risky behaviors of private investors on concession period decision making are analyzed. A subsidy method related to the internal rate-of-return (IRR) corresponding to a specific initial traffic volume and growth rate is proposed. The case of an actual BOT highway project is examined to illustrate how the method proposed can be used to determine the concession period of a transportation BOT project considering decision makers' loss-averse behavior and government subsidy. Contingency analysis is discussed to cope with possible misestimating of key factors such as initial traffic volume and cost coefficients. Sensitivity analysis is employed to investigate the impact of CPT parameters on the concession period decisions. An actual BOT case which failed to attract private capital is introduced to show the practical application. The results are then interpreted to conclude this paper.

Based on comparisons drawn between a concession period decision-making model considering the psychological behaviors of decision makers and a model not considering them, the authors conclude that the concession period based on CPT is distinctly different from that of the loss-neutral model. The concession period based on CPT is longer than the loss-neutral concession period. That is, loss-averse private investors tend to ask for long concession periods to make up for losses they will face in the future. Government subsidies serve as extra income for investors, allowing appointed profits to be secured sooner. For the benefit side of contingency variables, the normal state of initial traffic volume, average annual traffic growth rate and bias degree and the government subsidy need to be paid close attention during the project life span. For the cost side of contingency variables, the annual operating cost variable has a significant impact on the length of predicted concession period, while the large-scale cost variable has minor impact.

With an actual BOT highway project, the determination of transportation BOT concession periods based on the psychological behaviors of decision makers is analyzed in this paper. As the psychological behaviors of decision makers heavily impact the decision-making process, the authors analyze their impacts on concession period decision making. Government subsidy is specifically designed for various states of initial traffic volume and fluctuating growth rates to cope with corresponding high risks and mitigate private investors' loss-averse behaviors. Contingency analysis and sensitivity analysis are discussed as the estimated values of parameters may not be authentic in actual situations.

The paper aims to introduce the process flow of electroless nickel (EN) plating on carbon fiber surfaces, the effect of former processing on the properties of coating and the dynamics of the process.

The coated fibers were mounted in cold-setting epoxy resin, and transverse cross-section of the coated fibers were examined under an optical microscope to ascertain the thickness, uniformity and continuity of the coating over the fiber surface. The coating morphology was studied by using a scanning electron microscope (SEM). This study also determined the activation energy and electrical properties of EN coated on carbon fibers.

Activation temperatures have a greater impact on the quality of EN. At a temperature of 80°C, the EN layer prepared was uniform and compact and fully coated the carbon fibers. The optimum components of the EN plating process is NiSO₄: 28 g/L; NaH₂PO₂: 30 g/L; NaAc: 20 g/L; Na₃C₆H₅O₇:10 g/L; C₄O₆H₂KNa: 2 g/L; (NH₄)₂SO₄: 18 g/L; thiourea and lead acetate: trace; operating conditions: pH = 8.5, temperature: 70°C; time: 0.5 h). The activation energy of the EN plating on carbon fiber is 12 kJ/mol, and the electrical conductivity of nickel-plated carbon fiber in 80 mL of distilled water is 16.5 μs/cm.

This paper determined the optimum processing conditions and the activation energy of the EN plating on carbon fiber.

In this paper, with the goal of reducing the fuel consumption of UAV, the engine performance optimization is studied and on the basis of aircraft/engine integrated control, the minimum fuel consumption optimization method of engine given thrust is proposed. In the case of keeping the given thrust of the engine unchanged, the main fuel flow of the engine without being connected to the afterburner is optimally controlled so as to minimize the fuel consumption.

In this study, the reference model real-time optimization control method is adopted. The engine reference model uses a nonlinear real-time mathematical model of a certain engine component method. The quasi-Newton method is adopted in the optimization algorithm. According to the optimization variable nozzle area, the turbine drop-pressure ratio corresponding to the optimized nozzle area is calculated, which is superimposed with the difference of the drop-pressure ratio of the conventional control plan and output to the conventional nozzle controller of the engine. The nozzle area is controlled by the conventional nozzle controller.

The engine real-time minimum fuel consumption optimization control method studied in this study can significantly reduce the engine fuel consumption rate under a given thrust. At the work point, this is a low-altitude large Mach work point, which is relatively close to the edge of the flight envelope. Before turning on the optimization controller, the fuel consumption is 0.8124 kg/s. After turning on the optimization controller, you can see that the fuel supply has decreased by about 4%. At this time, the speed of the high-pressure rotor is about 94% and the temperature after the turbine can remain stable all the time.

The optimal control method of minimum fuel consumption for the given thrust of UAV is proposed in this paper and the optimal control is carried out for the nozzle area of the engine. At the same time, a method is proposed to indirectly control the nozzle area by changing the turbine pressure ratio. The relevant UAV and its power plant designers and developers may consider the results of this study to reach a feasible solution to reduce the fuel consumption of UAV.

Fuel consumption optimization can save fuel consumption during aircraft cruising, increase the economy of commercial aircraft and improve the combat radius of military aircraft. With the increasingly wide application of UAVs in military and civilian fields, the demand for energy-saving and emission reduction will promote the UAV industry to improve the awareness of environmental protection and reduce the cost of UAV use and operation.

This paper focuses on assembly line performance of an automotive body shop that builds body‐in‐white (BIW) assembly utilizing about 700+ process robots. These robots perform various operations such as welding, sealing, part handling, stud welding and inspection. There is no accurate tool available for the plant personnel to predict the future throughput based on plant's data. The purpose of this paper is to provide future throughput performance prediction based on plant data using Box‐Jenkins' ARMA model.

The following data were collected for five major assembly lines. First, the assembly machine‐in‐cycle time: the assembly line machines include robots that perform various functions like load, welding or sealing and unloading parts; the manual operators loading cycle time to the production fixtures. The conveyors act as buffers in between stations, and also feed to the production cells, and carry parts from station to station. The conveyors' downtime and uptime were also part of the machine‐in‐cycle time; second, the number of units produced from the beginning to the end of the assembly line; third, the number of fault occurrences in the assembly line due to various machine breakdowns; fourth, the machine availability percentage – i.e. the machine is readily available to perform its functions (the machine blocked upstream (starving) and blocked down (downstream) state is considered here); fifth, the actual efficiency of the machine measured in percentage based on output percentage; sixth, the expected number of units at designed efficiency.

In summary, this research paper provided a systematic development of a forecast model based on Box‐Jenkin's ARMA methodology to analyze the complex assembly line process performance data. The developed ARMA forecast models proved that the future prediction can be accurately predicted based on the past plant performance data. The developed ARMA forecast models predicted the future throughput performance within 99.52 percent accuracy. The research findings were validated by the actual plant performance data.

In this study, the automotive assembly process machines (robots, conveyors and fixtures) production data were collected, statistically analyzed and verified for viable ARMA model verification. The verified ARMA model has been used to predict the plant future months' throughput with 99.52 percent accuracy, based on the plant production data. This research is unique because of its practical usage to improve production.