Machine Problem Research Articles

Minimizing the maximum lateness for scheduling with release times and job rejection

We study scheduling problems with release times and rejection costs with the objective function of minimizing the maximum lateness. Our main result is a PTAS for the single machine problem with an upper bound on the rejection costs. This result is extended to parallel, identical machines. The corresponding problem of minimizing the rejection costs with an upper bound on the lateness is also examined. We show how to compute a PTAS for determining an approximation of the Pareto frontier on both objective functions on parallel, identical machines. Moreover, we present an FPTAS with strongly polynomial time for the maximum lateness problem without release times on identical machines when the number of machines is constant. Finally, we extend this FPTAS to the case of unrelated machines.

An adaptive gravitational search algorithm for optimizing mechanical engineering design and machining problems

The gravitational search algorithm (GSA) is widely used for solving optimization problems because it performs in a superior manner as compared to various competing evolutionary and swarm-based metaheuristics. However, GSA frequently gets trapped in local optima due to a lack of solution diversity. Although chaotic gravitational search algorithm (CGSA) can resolve this issue to some extent but its degraded exploitation rate and convergence speed may not result in desired outcome. To this end, diversity-based chaotic GSA (DCGSA) has exhibited its capability to resolve the issues encountered with GSA and CGSA to a certain extent for unconstrained problems. DCGSA achieves this characteristic through the use of an adaptive gravitational constant, which varies according to the diversity values of the population. Since most real-world problems are subjected to some constraints, it is prudent to improve the performance of GSA in solving constrained optimization problems. The present study integrates the enhanced search capability of DCGSA with a generalized constraint handling mechanism to improve the performance of GSA in solving constrained problems. It is observed that DCGSA significantly outperforms GSA on both CEC (Congress on evolutionary computation) 2006, 2010 and 2017 functions and competes strongly with CGSA. Diversity analysis shows that the capability to balance exploration and exploitation rates is enhanced using CGSA and DCGSA. Furthermore, DCGSA algorithms outperform GSA and CGSA on real-world machining and CEC 2020 mechanical design problems. Comparison with state-of-the-art algorithms is made to analyze the performance of the algorithms from a larger perspective.

A Direct Contour Control Method for Free-Form Surface Machining Trajectories Based on Coordinate Transformation

The requirement for high-speed and high-precision contouring with free-form surfaces in the CNC machining process is increasing. The contour error is an essential criterion for the quality of CNC machining. For the problem of contour error control, the present research is mainly based on position tracking, and the control method directly aiming at contour control has not been well studied. This paper proposed a new type of coordinate transformation-based direct contour control method (CT-DCC) for free-form surface machining trajectories. By real-time monitoring of the contour error and the foot point of the free-form parametric trajectory, the contouring task is decomposed into the contour error control task in the normal direction and the velocity control problem in the feed direction. Using coordinate transformation, the output commands of the two tasks are converted to the axial motion command. CT-DCC completely eliminates position tracking in the traditional control to achieve direct control of the contour error, which provides a new solution for the contour control problem of free-form surface machining. Both the axial velocity control and the axial torque control-based CT-DCC scheme were studied, and the two-axis and three-axis direct contour controllers were tested. The simulation and experiment results show the feasibility of the proposed method.

Improved Scheme for Data Aggregation of Distributed Oracle for Intelligent Internet of Things.

Oracle is a data supply mechanism that provides real-world data for blockchain. It serves as a bridge between blockchain and the IoT world, playing a crucial role in solving problems such as data sharing and device management in the IoT field. The main challenge at this stage is determining how to achieve data privacy protection in distributed Oracle machines to safeguard the value hidden in data on the blockchain. In this paper, we propose an improved scheme for distributed Oracle data aggregation based on Paillier encryption algorithm, which achieves end-to-end data privacy protection from devices to users. To address the issue of dishonest distributed Oracle machines running out of funds, we have designed an algorithm called PICA (Paillier-based InChain Aggregation). Based on the aggregation on the Chainlink chain and the Paillier encryption algorithm, random numbers are introduced to avoid the problem of dishonest Oracle machines running out of funds. We use the traffic coverage method to solve the problem of exposed request paths in distributed Oracle machines. Simulation and experimental results show that in small and medium-sized IoT application scenarios with 10,000 data nodes, each additional false request in a single request will result in a delay of about 2 s in data acquisition and can achieve a request response time of 20 s. The proposed method can achieve user data privacy protection.

Cogging torque reduction by utilizing the unequal rotor slot arc method for FSPM Machines

Cogging torque is a crucial and offending problem for electric machines that consist permanent magnets. Flux switching permanent magnet (FSPM) machines have magnets embedded inside the stator coils. Due to the interaction between the stator magnetic flux and the rotor steel, cogging torque occurs. To express this interaction, Magnetic Permeance Distribution Model is implemented in this study. To minimize the cogging torque, unequal rotor slot arc (URSA) technique is developed and cogging torque is calculated by using finite element method. An optimization study is performed to get the minimum value of the cogging torque under reasonable output torque. Results prove that, the proposed URSA method have promising outcomes for cogging torque reduction.

Analysis of Overall Equipment Effectiveness (OEE) as an Effort to Increase The Productivity Filling Line of Lithos Packaging Lubricant

This research was conducted at a company engaged in the lubricants business. This study examines the filling station lithos packaging. The lithos packaging filling station consists of 6 filling lines (FL), namely FL-1, FL-2, FL-3, FL-4, FL-5, and FL-6. From each filling, the line will be observed and analyzed in the format of the automation machine program. Based on the observations and analysis, it was found that FL-4 has an availability rate, performance rate, and quality rate below standard, so it is necessary to identify the cause of the low Overall Equipment Effectiveness (OEE) value of FL-4. This study analyzes the factors causing the low Overall Equipment Effectiveness (OEE) score on FL-4 using Six Big Losses and Fishbone diagrams. From the research results, it was found that several factors caused the less productive FL-4, namely breakdown losses with a value of 48%, setup and adjustment losses with a value of 3%, idling and minor stoppages with a value of 7%, reduced speed losses with a value of 41%, and defect losses with a value of 1%. An analysis was carried out using a fishbone diagram to determine the root cause of the high value of breakdown losses, reduced speed losses followed by idle and minor stoppage losses, and setup and adjustment losses. Based on the fishbone diagram, it was found that the factors causing the FL-4's unproductivity in terms of machine factors, method factors, human factors, material factors, and environmental factors. The causes of unproductive FL-4 are damage to the machine, downtime, the operator's less responsiveness to machine problems, operator expertise, decreased work ethic, defective materials, late material stocks, and hot room temperatures. Based on this, the proposed improvements given are to schedule machine maintenance at a specified time (preventive maintenance), not by the factor of damage (breakdown maintenance), make data guide for product change for each machine and attach it to each machine in full so that This downtime can be reduced, placing technical operators who focus on each filling line at least one technical operator, to speed up downtime and not disrupt the production process, add fans to speed up air circulation to reduce heat in the area and change material suppliers to get better quality.

Rigid-flexible coupling dynamic modelling and dynamic accuracy analysis of planar cam four-bar mechanism with multiple clearance joints

Combination mechanism of conjugate cam and four-bar is a complex type of multi-body system, in which the coupling problems such as cam profile law, flexible deformation of connecting rods, and multiple joint clearance collision make it difficult to model and optimise its dynamics. Just as the beating-up mechanism of high-speed rapier looms for multi-layer weaving is a representative conjugate cam four-bar mechanism. In response to the current development of textile machinery in the direction of high-speed and precision, and in order to improve the work efficiency and operational accuracy of the conjugate cam four-bar beating-up mechanism, the following work is done for the purpose of taking into account the multi-joint clearance and the flexible rod based on the mechanism. Firstly, based on Kane's equation, a multi-body dynamic model with multiple clearances is established combined with clearance collision theory. Secondly, a dynamic model of a rigid-flexible coupling system with multiple revolute clearances is established using finite element method combined with modal synthesis technology. Subsequently, the variables separation method and mode superposition method are used to solve the dynamic equations of the coupled system. Finally, numerical examples are used to analyse the effects of clearance quantity, clearance size, mode truncation order, cam speed, and component material properties on the dynamic response and accuracy of the system. The results show that, in the rotational speed range of 600–800 rpm, the dynamic performance of the system is little affected by the speed; Through comparative analysis of multiple materials, it is found that selecting carbon fibre composite materials has the smallest impact on the motion accuracy of the system. In this paper, the clearance collision model, finite element model and rigid-flexible coupling dynamic model are integrated, which are applied to the cam-linkage combined multi-body mechanism, and the dynamic problems of the actual machine are analysed and solved.

Scheduling parallel batch processing machines: A case study in the semiconductor industry

This paper presents a mathematical programming-based solution approach for the scheduling problem of batch-processing parallel machines with eligibility constraints. A case study has been presented in the semiconductor industry, where the ovens are scheduled for the underfill cure operation of products. The case includes constraints, such as oven-product eligibility restrictions, loading constraints for the batching of products for ovens, daily production requirements, and oven capacity constraints. In this study, we also assess the difference between creating batches of a single product type or different product types to be allocated to the ovens. The case study results have shown that the proposed models, in comparison to the current situation, increase the occupancy rate of ovens. The execution of the models aids the company in gaining visibility on the scheduling of ovens and successfully managing the production plan and order commitment. The proposed models have been effective and supportive of the semiconductor company.

A survey of the application and technical improvement of the multi-armed bandit

In recent years, the multi-armed bandit (MAB) model has been widely used and has shown excellent performance. This article provides an overview of the applications and technical improvements of the multi-armed bandit machine problem. First, an overview of the multi-armed bandit problem is presented, including the explanation of a general modeling approach and several existing common algorithms, such as -greedy, ETC, UCB, and Thompson sampling. Then, the real-life applications of the multi-armed bandit model are explored, covering the fields of recommender systems, healthcare, and finance. Then, some improved algorithms and models are summarized by addressing the problems encountered in different application domains, including the multi-armed bandit considering multiple objectives, the mortal multi-armed bandits, the multi-armed bandit considering contextual side information, combinatorial multi-armed bandits. Finally, the characteristics, trends of changes among different algorithms, and applicable scenarios are summarized and discussed.

A multi-factor combined traffic flow prediction model with secondary decomposition and improved entropy weight method

Traffic flow (TF) prediction is vital in traffic systems. Aiming at the effect of noise on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an improved CEEMDAN based on amplitude-aware permutation entropy (AAPE), named ICEEMDAN, is proposed. Aiming at the parameter selection problems on variational mode decomposition (VMD), VMD improved by pelican optimization algorithm (POA) is proposed, called PVMD. Aiming at the parameter selection problems of bidirectional long short-term memory (BiLSTM) and kernel extreme learning machine (KELM), BiLSTM improved by sea-horse optimizer (SHO), named SHOBiLSTM, and KELM improved by nutcracker optimizer algorithm (NOA), named NOAKELM are proposed, respectively. Since the chaotic characteristics of TF, multi-factor combined TF prediction model with ICEEMDAN, PVMD, SHOBiLSTM, NOAKELM, improved entropy weight method is proposed. First, use ICEEMDAN to decompose TF and get intrinsic mode functions (IMFs). Second, refined composite multiscale dispersion entropy (RCMDE) is used to divide IMFs into the fuzzy and detail components, fuzzy components are secondarily decomposed by PVMD, IMFs and factors are multi-factor predicted by SHOBiLSTM and NOAKELM. Finally, improved entropy weight method is proposed to weight SHOBiLSTM and NOAKELM results. TF of the UK highway proves the superiority. Experiments indicate that the proposed model is better than other nine models with RMSE, MAE, MAPE and R2 of 29.5868, 22.0431, 0.0701 and 0.9947 for TF I. The outcomes suggest this proposed model precedes nine models at 99% confidence level, providing data basis and theoretical for travel plans and traffic system.

A restless bandit approach for capacitated condition based maintenance scheduling

AbstractThis paper considers the maintenance scheduling problem of multiple non-identical machines deteriorating over time. The deterioration gradually decreases a machine’s performance, which results in revenue losses due to lower output quality. The maintenance cost is dependent on the degradation state, and the number of maintenance activities that can be carried out simultaneously is restricted by the number of maintenance workers. Our main goal is to propose a heuristic with low complexity that consistently produces solutions close to the optimal strategy for problems of real size. We cast the problem as a restless bandit problem and propose an index based heuristic (Whittle’s index policy) which can be computed efficiently. We also provide a lower bound that can be computed by linear programming. We numerically compare the performance of the index heuristic with alternative policies. In addition to achieving superior performance over failure-based and threshold policies, Whittle’s policy numerically converges to our lower bound when the number of machines is moderately high and/or maintenance workload is high.

A Novel Approach to Surface Roughness Virtual Sample Generation to Address the Small Sample Size Problem in Ultra-Precision Machining.

Surface roughness is one of the main bases for measuring the surface quality of machined parts. A large amount of training data can effectively improve model prediction accuracy. However, obtaining a large and complete surface roughness sample dataset during the ultra-precision machining process is a challenging task. In this article, a novel virtual sample generation scheme (PSOVSGBLS) for surface roughness is designed to address the small sample problem in ultra-precision machining, which utilizes a particle swarm optimization algorithm combined with a broad learning system to generate virtual samples, enriching the diversity of samples by filling the information gaps between the original small samples. Finally, a set of ultra-precision micro-groove cutting experiments was carried out to verify the feasibility of the proposed virtual sample generation scheme, and the results show that the prediction error of the surface roughness prediction model was significantly reduced after adding virtual samples.

Research on the Application of Precision Detection Technology in Machining and Manufacturing

In recent years, the speed of China's social and economic development continues to accelerate, and the quality requirements of machining and manufacturing have been improved. At present, there are problems in machining and manufacturing in China, which cannot guarantee the effect of quality control, which is not conducive to the improvement of the precision of machining products, and the application of precision detection technology in machining and manufacturing can comprehensively detect the precision of products. This paper accurately analyzes quality problems and provide support for improving the quality of machining and manufacturing. Based on this, this paper analyzes the significance of the application of precision detection technology in machining and manufacturing, and puts forward the application measures to provide help for improving the quality of machining and manufacturing.

Machinability of elliptical ultrasonic vibration milling γ-TiAl: Chip formation, edge breakage, and subsurface layer deformation

Superior strength and high-temperature performance make γ-TiAl vital for lightweight aero-engines. However, its inherent brittleness poses machining problems. This study employed Elliptical Ultrasonic Vibration Milling (EUVM) to address these problems. Considering the influence of machining parameters on vibration patterns of EUVM, a separation time model was established to analyze the vibration evolutionary process, thereby instructing the cutting mechanism. On this basis, deep discussions regarding chip formation, cutting force, edge breakage, and subsurface layer deformation were conducted for EUVM and Conventional Milling (CM). Chip morphology showed the chip formation was rooted in the periodic brittle fracture. Local dimples proved that the thermal effect of high-speed cutting improved the plasticity of γ-TiAl. EUVM achieved a maximum 18.17% reduction in cutting force compared with CM. The force variation mechanism differed with changes in the cutting speed or the vibration amplitude, and its correlation with thermal softening, strain hardening, and vibratory cutting effects was analyzed. EUVM attained desirable edge breakage by achieving smaller fracture lengths. The fracture mechanisms of different phases were distinct, causing a surge in edge fracture size of γ-TiAl under microstructural differences. In terms of subsurface deformation, EUVM also showed strengthening effects. Noteworthy, the lamellar deformation patterns under the cutting removal state differed from the quasi-static, which was categorized by the orientation angles. Additionally, the electron backscattering diffraction provided details of the influence of microstructural difference on the orientation and the deformation of grains in the subsurface layer. The results demonstrate that EUVM is a promising machining method for γ-TiAl and guide further research and development of EUVM γ-TiAl.

Spare parts warehouse re-layout design with kaizen 5S implementation to reduce wasted time searching for machine parts

Increasing the productivity and efficiency of production machines cannot be separated from the availability of machine spare parts in the warehouse department. Delays in the delivery of spare parts if machine problems occur often become a problem that disrupts efficiency. This delay was caused by the search for engine spare parts in the warehouse taking too long to find. The long search time was caused by the irregular placement of the materials and the messy condition of the warehouse. This research aims to make improvements to the goods search process to make it faster and more effective in the warehouse area and increase warehouse capacity using warehouse re-layout. This research method uses the implementation of warehouse re-layouts combined with the application of Kaizen 5S in controlling the condition of machine spare parts. This research found that the reason for the long search time for machine spare parts was due to the lack of knowledge of warehouse operators and the absence of training regarding the application of the 5S method, as well as the careless placement of spare parts in warehouses that were not neatly arranged. This research has resulted in a reduction in search time for engine spare parts from 6.4 minutes to 4 minutes per spare part unit or a decrease of 36.5%. Meanwhile, the results of the warehouse re-layout resulted in an increase in warehouse capacity from 80 pallets to 316 pallets, an increase of 395%.

Feedback stabilization of probabilistic finite state machines based on deep Q-network.

As an important mathematical model, the finite state machine (FSM) has been used in many fields, such as manufacturing system, health care, and so on. This paper analyzes the current development status of FSMs. It is pointed out that the traditional methods are often inconvenient for analysis and design, or encounter high computational complexity problems when studying FSMs. The deep Q-network (DQN) technique, which is a model-free optimization method, is introduced to solve the stabilization problem of probabilistic finite state machines (PFSMs). In order to better understand the technique, some preliminaries, including Markov decision process, ϵ-greedy strategy, DQN, and so on, are recalled. First, a necessary and sufficient stabilizability condition for PFSMs is derived. Next, the feedback stabilization problem of PFSMs is transformed into an optimization problem. Finally, by using the stabilizability condition and deep Q-network, an algorithm for solving the optimization problem (equivalently, computing a state feedback stabilizer) is provided. Compared with the traditional Q learning, DQN avoids the limited capacity problem. So our method can deal with high-dimensional complex systems efficiently. The effectiveness of our method is further demonstrated through an illustrative example.

An application of mathematical programming to a real case of the unrelated parallel machine problem

An application of mathematical programming to a real case of the unrelated parallel machine problem

On undecidability of unary non-nested PFP-operators for one successor function theory

We investigate the decidability of first-order logic extensions. For example, it is established in A. S. Zolotov’s works that a logic with a unary transitive closure operator for the one successor theory is decidable. We show that in a similar case, a logic with a unary partial fixed point operator is undecidable. For this purpose, we reduce the halting problem for the counter machine to the problem of truth of the underlying formula. This reduction uses only one unary non-nested partial fixed operator that is applied to a universal or existential formula.

Dynamic task allocation of hybrid flow shop for machines in parallel with different speeds based on an MLD prediction model

ABSTRACT The current hybrid flow shop task allocation usually assumes a static manufacturing environment, which cannot effectively handle uncertain events in the production process. To address this, the dynamic task allocation problem for machines in parallel with different speeds is studied, and a Mixed Logical Dynamical (MLD) model for predicting the state of the production system is established. A dynamic allocation method based on Model Predictive Control (MPC) is proposed. The proposed novel method integrates dynamic model with rolling optimization to better deal with uncertain events in production process by decomposing the overall planning problem into smaller local planning models. Numerical results show that the proposed method outperform the traditional global planning method and rule-based allocation method in terms of time and job processing rate. In addition, through the Plant Simulation software, the simulation results are consistent with the numerical results, which fully proves the effectiveness of the proposed method.

Parallel batch processing machines scheduling for SLM additive manufacturing in cloud manufacturing

For the scheduling problem of parallel batch processing machines for Selective Laser Melting (SLM) additive manufacturing in cloud manufacturing environments, an Improved Discrete Artificial Bee Colony algorithm(IABC) is proposed by considering the service time of the product as well as introducing the mathematical model of the problem. Firstly, a population initialization strategy is introduced to improve the quality of initial solutions and accelerate the convergence of the population.Secondly, through three neighborhood search operators, the transition from the original nectar source to the new nectar source is achieved for employed bees and following bees. Furthermore, a local search strategy and a modified abandonment pattern are proposed to increase population diversity and prevent the algorithm from falling into local optima.Finally, the performance of the algorithm is analyzed, and the IABC algorithm was applied to 36 examples. The effectiveness and advantages of IABC in solving the studied problem are verified through a large number of simulation experiments.