Serial Production Lines Research Articles

Throughput Approximation by Neural Network for Serial Production Lines With High Up/Downtime Variability

Throughput Approximation by Neural Network for Serial Production Lines With High Up/Downtime Variability

Modeling and Analysis of Productivity and Energy in Serial Production Lines With Setups

Modeling and Analysis of Productivity and Energy in Serial Production Lines With Setups

An Approach to Inline Monitoring of the Electrode State in Resistance Spot Welding

Resistance Spot Welding (RSW) is a key technology for joining car body parts. During the process, the upper and lower electrodes of the welding gun are subject to wear, which affects the electric current flow and thus, the quality of the spot weld. To counteract this effect, the electrode tip is dressed after a predefined number of spot welds. The frequency for tip dressing is based on experience and does currently not directly reflect the actual state of the electrode. This work provides an approach to inline electrode state monitoring to determine the ideal and demand-based point in time for electrode tip dressing based on process data. We avoid costly and time-intensive experimental labeling of electrode states and utilize changes in the dynamic electrical resistance between dressing cycles to represent the electrode wear state. To describe the changes in the dynamic electrical resistance curve, new features such as the peak time delay are calculated and visualized. We evaluate our approach with a real-world data set that stems from a dynamic and complex environment out of a series production line, which, in contrast to laboratory data, ensures a successful application of the proposed methods in an industrial setting.

Transient analysis of production performance and energy consumption in geometric flexible production systems

To meet the growing need for customized products, flexible production systems are gaining widespread application in modern factories. The inherent complexity of these flexible systems poses new challenges for production management. In this paper, we intend to contribute an operation-level analytical model for such systems, thus providing an effective evaluation tool for productivity and energy consumption analysis. Specifically, a multi-type serial production line with machines obeying the geometric reliability model and intermediate buffers having limited capacity is considered, in which K batches of parts are processed sequentially in each shift. Analytical solutions for key operation metrics are first developed for small systems using a Markov model. For multi-machine lines, a semi-analytical method based on aggregation is proposed. The effectiveness of the proposed analysis method is verified through simulation experiments. Besides, system properties are analyzed to provide insights for continuous improvement and optimization.

Opportunistic maintenance modeling for series production systems based on bottleneck by considering energy consumption and market demand

ABSTRACT Nowadays, series production systems are broadly used in various branches of industry. In these kinds of production systems, any disruption in production flow can significantly increase production cost and time; therefore, a proper maintenance strategy considerably reduces the company’s costs and enhance organizational performance. In this research, a novel bottleneck-centered opportunistic maintenance policy for a series production line is proposed. First of all, the bottleneck is detected by the Lowest Production Rate method. Then, preventive maintenance time interval, reliability threshold, and number of maintenance cycles are determined by the particle swarm optimization algorithm. Finally, a novel opportunistic maintenance strategy is proposed based on analyzing the cost savings of preventive maintenance advancement and postponement. To demonstrate the model’s effectiveness, the proposed bottleneck-based opportunistic maintenance model is compared with two other maintenance models, and the results confirmed that the proposed strategy could efficiently eliminate unnecessary production breaks and achieve cost reduction.

Operation state-based adaptive control in response to production disturbances of energy-efficient serial production lines

The energy-efficient control has emerged as one of the most promising measures to reduce the unproductive energy consumption of machines during production disruptions. Diverse production disturbances (PDs) are the fundamental cause of production interruptions that result in unproductive energy consumption. In this scenario, the diversity and randomness of PDs necessitate elaborative consideration in energy-efficient controls. This consideration is crucial to the effectiveness of energy-efficient controls, yet it has been generally overlooked. To this end, this paper presents an adaptive energy-efficient control (AEC) methodology to generate adaptive control decisions when machines encounter diverse PDs in serial production lines. As a first step towards AEC, a PD identification method driven by Markov Logic Network is introduced to identify ongoing PDs. To then estimate the system performances in terms of energy savings and throughputs, a system dynamics model for serial production lines is established based on max-plus algebra. Finally, an AEC policy is formulated to optimize unproductive energy consumption on a nearly global scale while maintaining throughputs. A case study was performed on a die-casting production line in order to validate the effectiveness of the methodology. The results indicated a 96.1% F1 score for PD identifications. Based on this, the AEC policy resulted in a 61.12% reduction in unproductive energy consumption, with a corresponding decrease in throughputs of only 0.65%. It was confirmed that the AEC methodology achieved greater energy savings while minimizing throughput losses compared to conventional energy-efficient control methods.

Development and Numerical Optimization of a System of Integrated Agents for Serial Production Lines

In modern high-volume industries, the serial production line (SPL) is of growing importance due to the inexorable increase in the complexity of manufacturing systems and the associated production costs. Optimal decisions regarding buffer size and the selection of components when designing and implementing an SPL can be difficult, often requiring complex analytical models, which can be difficult to conceive and construct. Here, we propose a model to evaluate and optimize the design of an SPL, integrating numerical simulation with artificial intelligence (AI). Numerous studies relating to the design of SPL systems have been published, but few have considered the simultaneous consideration of a number of decision variables. Indeed, the authors have been unable to locate in the published literature even one work that integrated the selection of components with the optimization of buffer sizes into a single framework. In this research, a System of Integrated Agents Numerical Optimization (SIGN) is developed by which the SPL design can be optimized. A SIGN consists of a components selection system and a decision support system. A SIGN aids the selection of machine tools, buffer sizes, and robots via the integration of AI and simulations. Using a purpose-developed interface, a user inputs the appropriate SPL parameters and settings, selects the decision-making and optimization techniques to use, and then displays output results. It will be implemented in open-source software to broaden the impact of the SIGN and extend its influence in industry and academia. It is expected that the results of this research project will significantly influence open-source manufacturing system design and, consequently, industrial and economic development.

Simultaneous control of multiple machines for energy efficiency: a simulation-based approach

Energy efficiency is crucial in contemporary industry and controlling the resource power state by switching off/on commands is a promising measure. The control problem of deciding when to switch off/on the machines depending on the state of the system at a given time is not trivial due to the effect the control might have on the system production rate. Threshold-based policies using buffer occupancy information to control the machines can be effectively used to reduce energy consumption. Nevertheless, highly complex control policies are difficult to be applied and costly to be managed in practice. Buffer-based threshold policies to control multiple machines simultaneously in a serial production line for energy efficiency purposes are analysed in this work. The optimal control minimises the energy consumption while assuring a certain target production rate for the system. The effects of controlling different combinations of machines simultaneously with different number of thresholds have been investigated through numerical experiments with discrete event simulation. Insights regarding the trade-off between the complexity of the control and the performance gains are provided. The proposed policy works effectively and the effect of a proper selection of the controlled machines or thresholds is significant.

A fast and efficient analytical method for throughput evaluation of unreliable series-parallel production lines

In this paper, an efficient throughput evaluation method for large series-parallel production lines is developed. The parallel machines follow the exponential reliability model and are not necessarily identical. The proposed method considers different aggregation techniques to transform each group of parallel machines into an equivalent station. Then, the throughput of the obtained buffered serial production line is evaluated using an analytical approach. This evaluation method is based on the analysis of buffer states using a birth-death Markov process. The strength of the approach lies in the analysis of only full and empty buffer states rather than all buffer states. Consequently, through the comparative numerical study against simulation and recent literature, the proposed approach demonstrates its accuracy and efficiency in evaluating throughput of large complex series-parallel systems in only few seconds. In addition, aggregation techniques used are compared and some relevant theoretical properties are derived.

Optimized Buffer Allocation and Repair Strategies for Series Production Lines

Optimized Buffer Allocation and Repair Strategies for Series Production Lines

Energy-efficient on/off control in serial production lines with Bernoulli machines

Energy-efficient on/off control in serial production lines with Bernoulli machines

A Data-Driven Indirect Estimation of Machine Parameters for Smart Production Systems

Automated measurement of the machine reliability parameters for a production system enables a continuous update of the mathematical model of the system, which can be used for various analyses toward productivity improvement. However, the continuous update may be impeded by some machines of which automated parameter measurements are out of order. Such a situation has been observed, for instance, when some of the machines in the line cannot save log files or Internet of Things devices that measure these machines stop functioning. In this context, this article addresses the problem of estimating the reliability parameters of those machines while avoiding a direct manual measurement (by humans) of uptime and downtime. It turns out that those parameters can be computed using buffer-related data of the neighboring machines along with the system information. With this, a continuous update of the model is possible even though some machines stop recording their status in an automated manner. The method is indirect as opposed to direct manual measurement. The results are derived for synchronous serial production lines with Bernoulli and also exponential reliability characteristics. Our simulation studies verify the accuracy of the proposed estimation methods.

Energy and Productivity Analysis in Serial Production Lines With Setups

Energy and Productivity Analysis in Serial Production Lines With Setups

Energy consumption control in the two-machine Bernoulli serial production line with setup and idleness

In recent years, the topic of sustainable manufacturing system design with energy saving features has received increasing attention. For a typical production line setting, the machines are subject to random breakdown and restart besides blockage and starvation, and they are usually connected via buffer areas with finite capacity. In the present study, the energy consumption of the serial production line with two Bernoulli machines is considered, with an aim to minimise the total energy consumption under a desired production rate. The energy consumption of the two-machine system is composed of the energy needed to setup, to remain idle, and for actual manufacturing. In order to minimise the total energy expenditure, a nonlinear fractional polynomial optimisation model is constructed, which is first converted to a nonlinear polynomial optimisation problem. Then the property of the total energy cost is analysed via the sum of squares (SOS) method. To speed up the solving process, a new heuristic approach named energy consumption saving (ECS) algorithm is proposed considering the monotonicity and local optimality of the energy cost function. Finally, by presenting optimal configurations of the production line with different throughputs, buffer capacities, and energy parameters, a simulation-based study is performed to validate the SOS and ECS algorithms.

Performance analysis and optimisation of Bernoulli serial production lines with dynamic real-time bottleneck identification and mitigation

With the rapid advances of Industry 4.0 technologies, developing rigorous, model-based algorithms for production system performance metrics calculation, control, and optimization has become a critical task to inject intelligence into the smart manufacturing practice, thus facilitating automated decision-making on the factory floor. This paper is intended to contribute to this area. Specifically, we consider serial production lines with finite buffers and machines following the Bernoulli reliability model. In addition, we assume that one can dynamically control the allocation of a shared workforce via real-time production bottleneck identification and mitigation. We first derive formulas to calculate the performance metrics of two-machine systems. Then, we extend the results to multi-machine cases by developing an aggregation-based analytical algorithm, whose accuracy is verified using numerical experiments. Based on this performance evaluation method, we study the optimization of the control policy. In particular, a space reduction technique is applied that decomposes the system into several sub three-machine lines and search algorithms are proposed to identify a good-quality control policy in the reduced policy space. Numerical experiments are used to demonstrate the efficacy of the optimized policy. An illustrative example is given to provide insights into the structure and operation of an effective control policy.

Deep multi-agent reinforcement learning for multi-level preventive maintenance in manufacturing systems

Designing preventive maintenance (PM) policies that ensure smooth and efficient production for large-scale manufacturing systems is non-trivial. Recent model-free reinforcement learning (RL) methods shed lights on how to cope with the non-linearity and stochasticity in such complex systems. However, the action space explosion impedes RL-based PM policies to be generalized to real applications. In order to obtain cost efficient PM policies for a serial production line that has multiple levels of PM actions, a novel multi-agent modeling is adopted to support adaptive learning by modeling each machine as cooperative agent. The evaluation of system-level production loss is leveraged to construct the reward function. An adaptive learning framework based on value-decomposition multi-agent actor–critic algorithm is utilized to obtain PM policies. In simulation study, the proposed framework demonstrates its effectiveness by leading other baselines on a comprehensive set of metrics whereas the centralized RL-based methods struggles to converge to stable policies. Our analysis further demonstrates that our multi-agent reinforcement learning based method learns effective PM policies without any knowledge about the environment and maintenance strategies.

Dynamic performance prediction in flexible production lines with two geometric machines

Due to the intelligent manufacturing strategies deployed and being implemented in recent years, flexible production is widely used on the factory floor at present. While most of the existing investigations are concerned with the steady-state of systems, very few results have been published on systems' dynamic behaviour. However, since flexible production is sometimes carried out according to customised orders, most of which are in relatively medium or even small sizes, part or all the production of such systems are in the dynamic process. As a result, the dynamic characteristics of production systems are of significant importance. Meanwhile, dynamic performance is regarded as an important direction in the research of manufacturing systems. In this work, for serial flexible production lines with two geometric machines and one finite buffer, we study the problems of dynamic performance evaluation, system behavioural properties, and energy-efficient operation control. Specifically, the model of production lines under consideration is formulated. Then, an analytical method to assess the dynamic performance of such flexible production lines is proposed. Moreover, the behavioural properties of the systems are also investigated. Finally, we study the problem of control parameters selection for the system under operation control.

Data-Enabled Permanent Production Loss Analysis for Serial Production Systems With Variable Cycle Time Machines

Real time production performance evaluation plays a vital role in diagnosing manufacturing system health status and achieving productivity improvements. However, most existing studies on system performance evaluation are based on steady state analysis and focused on the production system with fixed cycle time machines. The real-time performance evaluation for a manufacturing system with variable cycle time machines, although typical for a large number of realistic scenarios, has been mostly ignored. The development of smart manufacturing and increasingly available sensor data have provided unprecedented opportunities to carry out thorough analysis on the real-time performance of such complex systems. In this letter, we developed a data-enabled methodology to efficiently identify and predict the real-time permanent production loss for a serial production line with variable cycle time machines. The concept and evaluation method of opportunity window are introduced to facilitate the permanent production loss estimation. Numerical case studies are presented to demonstrate the effectiveness of the proposed methods for opportunity window evaluation and production loss identification.

Data-driven dynamic bottleneck detection in complex manufacturing systems

Production (throughput) bottlenecks are the critical stations defining and constraining the overall productivity of a system. Effective and timely identification of bottlenecks provide manufacturers essential decision input to allocate limited maintenance and financial resources for throughput improvement. However, identifying throughput bottleneck in industry is not a trivial task. Bottlenecks are usually non-static (shifting) among stations during production, which requires dynamic bottleneck detection methods. An effective methodology requires proper handling of real-time production data and integration of factory physics knowledge. Traditional data-driven bottleneck detection methods only focus on serial production lines, while most production lines are complex. With careful revision and examination, those methods can hardly meet practical industrial needs. Therefore, this paper proposes a systematic approach for bottleneck detection for complex manufacturing systems with non-serial configurations. It extends a well-recognized bottleneck detection algorithm, “the Turning Point Method”, to complex manufacturing systems by evaluating and proposing appropriate heuristic rules. Several common industrial scenarios are evaluated and addressed in this paper, including closed loop structures, parallel line structures, and rework loop structures. The proposed methodology is demonstrated with a one-year pilot study at an automotive powertrain assembly line with complex manufacturing layouts. The result has shown a successful implementation that greatly improved the bottleneck detection capabilities for this manufacturing system and led to an over 30% gain in Overall Equipment Effectiveness (OEE).

Accuracy of Semi-Analytical and Numerical Approaches in the Evaluation of Serial Bernoulli Production Lines

The manufacturing industry has a great impact on the economic growth of countries. It is, therefore, crucial to master the skills of the production system by mathematical tools that enable the evaluation of the production systems’ performance measures. Four mathematical approaches toward the modeling of steady-state behavior of serial Bernoulli production lines were considered in this study, namely, the analytical approach, the finite state method, the aggregation procedure, and numerical modeling. The accuracy of the performance measures determined using the semi-analytical methods and the numerical approach was validated using numerous theoretical examples and the results obtained using the analytical model. All of the considered methods demonstrated relevant reliability, regardless of the different theoretical backgrounds.