Assembly Line Control Research Articles

Decentralized Tact Time Control in Assembly Simplifying Robust Control of Assembly Lines via the I4.0 Box

Decentralized Tact Time Control in Assembly Simplifying Robust Control of Assembly Lines via the I4.0 Box

Statistical process control of assembly lines in manufacturing

Data collection is often a time-consuming activity and sometimes real-time required. Moving to automation could bring multiple benefits, but sometimes it may not be convenient. In this paper four different situations are analyzed, and for each of them a re-engineered solution enabled by information integration for automating the data collection, if applicable, is proposed. More into detail, the data collection is performed so as to apply a Statistical Process Control for quality management purposes on four different operations, taken as case studies and carried out on a filling machine produced by an Italian company. Statistical Process Control consists in determining two process capability indexes whose values, for completeness, are then compared with the relating Six Sigma level. One of the peculiarities of these case studies is that before collecting the measurements, the systems and instruments were validated through the ANOVA Gage Reproducibility & Repeatability method. This is somehow an innovative procedure, since quite often the preliminary validation step is neglected, thus involving the risk of inaccurate and distorted outcomes.

Energy-Aware Model Predictive Control of Assembly Lines

This paper presents a model predictive approach to the energy-aware control of tasks’ execution in an assembly line. The proposed algorithm takes into account both the need for optimizing the assembly line operations (in terms of the minimization of the total cycle time) and that of optimizing the energy consumption deriving from the operations, by exploiting the flexibility added by the presence of a local source of renewable energy (a common scenario of industries that are often equipped, e.g., with photovoltaic plants) and, possibly, also exploiting an energy storage plant. The energy-related objectives we take into account refer to the minimization of the energy bill and the minimization of the peaks in the power injected and absorbed from the grid (which is desirable also from the perspective of the network operator). We propose a mixed-integer linear formulation of the optimization problem, through the use of H-infinite norms, instead of the quadratic ones. Simulation results show the effectiveness of the proposed algorithm in finding a trade-off that allows keeping at a minimum the cycle time, while saving on the energy bill and reducing peak powers.

A Parallel Deep Reinforcement Learning Framework for Controlling Industrial Assembly Lines

Decision-making in a complex, dynamic, interconnected, and data-intensive industrial environment can be improved with the assistance of machine-learning techniques. In this work, a complex instance of industrial assembly line control is formalized and a parallel deep reinforcement learning approach is presented. We consider an assembly line control problem in which a set of tasks (e.g., vehicle assembly tasks) needs to be planned and controlled during their execution, with the aim of optimizing given key performance criteria. Specifically, the aim will be that of planning the task in order to minimize the total time taken to execute all the tasks (also called cycle time). Tasks run on workstations in the assembly line. To run, tasks need specific resources. Therefore, the tackled problem is that of optimally mapping tasks and resources to workstations, and deciding the optimal execution times of the tasks. In doing so, several constraints need to be respected (e.g., precedence constraints among the tasks, constraints on needed resources to run tasks, deadlines, etc.). The proposed approach uses deep reinforcement learning to learn a tasks/resources mapping policy that is effective in minimizing the resulting cycle time. The proposed method allows us to explicitly take into account all the constraints, and, once training is complete, can be used in real time to dynamically control the execution of tasks. Another motivation for the proposed work is in the ability of the used method to also work in complex scenarios, and in the presence of uncertainties. As a matter of fact, the use of deep neural networks allows for learning the model of the assembly line problem, in contrast with, e.g., optimization-based techniques, which require explicitly writing all the equations of the model of the problem. In order to speed up the training phase, we adopt a learning scheme in which more agents are trained in parallel. Simulations show that the proposed method can provide effective real-time decision support to industrial operators for scheduling and rescheduling activities, achieving the goal of minimizing the total tasks’ execution time.

Statistical Process Control of assembly lines in a manufacturing plant: Process Capability assessment

Abstract Among the main strategies adopted by companies for enhancing their competitive advantage as well as for improving the internal efficiency is the quality management. Several tools can be involved when dealing with this issue; one of these is the Statistical Process Control, which includes the employment of statistical methods and metrics to monitor and control a process’ quality. In this paper, indeed, two statistical metrics are involved for assessing the process capability of a filler machine produced by an Italian company operating in the food context. Specifically, two processes are inspected: the slewing ring-pinion backlash and the handling clamps height check, both showing excellent performances after having carried out the control and provided appropriate adjustments. Results are also compared with those obtained from the Six Sigma theory, another tool involved for quality controls which is in line with principles of lean manufacturing. Moreover, for the second process, a software was implemented for speeding up operations and achieving benefits in terms of time. The reliability of these analysis is confirmed by the application of the ANOVA Gage R&R tool, which allowed to assess the precision of the measurement system involved.

Data Flow of Flexible Assembly Line Control Systems

The data flow processing is an important part of flexible assembly line control system. First, the system frame of the assembly system is described in detail in the paper, then, the designs of the cell computer data flow and processing functions are expatiated, the paper is focused on the sequence for telegram, product change, pallet control, rework between the cell computer and SoftPLC in IPC. The research has provided a practical, well-established data flow to improve the reliability, flexibility and effectiveness of assembly line control systems.

PLC Simulation Control of Assembly Line

This paper mainly introduces the application of PLC simulation control in industrial production, and requires the use of configuration software and PLC (SIMEINS S7-200) control system connection, the use of lower computer implementation, the host computer monitoring method to build complete assembly line simulation control system. Through the PLC simulation control and confi guration monitoring, this paper implements the assembly line control and monitoring.

Research of the Architecture of the Flexible Assembly Line Based on the Extension Method and Multi-Agent

With the combination of the extension method and multi-agent, the assemble process of the motor and its subassembly was analyzed to build the corresponding Agent and multi-Agent System model; The architecture of the flexible assembly line was established based on the established model. Finally, with the technology of fieldbus and industrial control, a flexible assembly line control system was developed based on the Profibus-DP and it provided the effective solution for the combination of theory and practical application and the applicable model for the establishment of the architecture of the flexible assembly line.

A Method for Setting the Optimal Production Status Based on Genetic Algorithm in a Discrete Assembly System

In a discrete assembly system, setting its optimal production status is one of the key works in assembly line balancing. Based on analyzing the objectives of assembly line control, a general flow for setting the optimal production status is proposed, and a method to identify rapidly the setting objects of production status is introduced. Then an optimal configuration solution for production status and its solving method in a station of an assembly line are established based on the genetic algorithm. At last, a wing assembly line is set as an example to validate this method, and the result shows that this method can provide a solution to optimize production status parameters for each station in this assembly line, which can reduce the resource idle time and cost, and so its resource utilization rate is improved.

Controlling mixed-model assembly lines in real-time by using distributed systems

The production of large quantities is frequently handled by the use of assembly lines. These systems yield significant reductions of variable costs for the production of homogenous products. But due to increasing competition and differentiated demands, today it is no longer sufficient to offer only standardized products. To combine the wishes and requirements of the customers with the advantages of an efficient flow line production, different product variants are produced simultaneously on the same mixed-model assembly line. Therefore, the task of controlling such production processes is becoming more complex since an efficient production execution has to be realized in spite of the occurring oscillating work content of the different variants. Additionally, unforeseen disturbances in the production process can compromise its planned execution. To deal with these problems, this paper proposes a new approach for an adaptive real-time control of assembly lines that extends the pure sequencing problem by the integration of specific line-balancing aspects and the mapping of consequences of possible disturbance scenarios. Such a scenario could be, for instance, the loss of a worker, a material bottleneck or a machine breakdown. To guarantee an efficient continuation of the production process, the controlling instrument reacts instantly to a disturbance by adapting the current plan in a very short time, consisting of only a few cycle times, to reduce the additional costs caused by the disturbances.

Fuzzy operator allocation for balance control of assembly lines in apparel manufacturing

Production processes in apparel manufacturing typically involve hybrid assembly lines. In order to perform at predetermined production rates, careful balance control of the sewing operations on these assembly lines is very important. The right number of operators to be moved in and out of a sewing section has to be accurately determined. In this paper, the authors extend the literature on fuzzy logic applications to control systems by proposing a simple, yet effective, rule-based system that captures the knowledge of experienced supervisors in a set of fuzzy rules. These rules specify how balance control can be achieved based on the level of difference between; (1) the actual and target buffer level; and (2) the actual and target section performance. Since these parameters are normally expressed in linguistic terms, they are encoded in rules expressed with fuzzy logic. The number of operators to be moved in and out of a sewing section can, therefore, be determined by means of fuzzy inferences. To evaluate the effectiveness of the system, we compared its performance to the decisions made by experienced supervisors at a large shirt factory. The system was found to increase in production efficiency by 30%.

The ALPACA line balancing system

The ALPACA line balancing system is an interactive computer-based system for the planning and control of assembly lines upon which mixed models with a large option mix are to be assembled. The ALPACA system facilities the assignment of work to operators along the assembly line and provides immediate evaluations of the effects of the assignment of work. Work is assigned and initially evaluated on the basis of forecasted model mix and option mix percentages. The operator's work assignment is further evaluated by passing an actual vehicle build sequence of models complete with all specified options past the operation (via a simulation) to highlight any extreme overcycle or idle conditions which are not apparent from average values based on the forecasted model mix and option mix percentages. ALPACA gives the industrial engineer and the line supervisor, working together as a team, the ability to balance an assembly line quickly and react rapidly to product engineering changes and/or schedule changes.

Assembly Line Control Models in Automotive Industry

Publisher Summary This chapter discusses assembly line model within the framework a production control and information computer system consisting of five basic functional areas. It describes the production control area where the problem of finding an optimum sequence of different vehicle types and options that are to be completed assembly line was solved. The relevant mathematical model is based on the daily vehicle schedule and considers assembly timing data of different vehicle types, loading of each work center and technological restrictions. The control of movements of selected parts and conveyor control are integral parts of the algorithm. The total system problem area was divided according the nature of various production, planning and control activities into the following main functional areas: (1) production planning, (2) production scheduling, (3) production monitoring and reporting, (4) production control, (5) quality control.