Repetitive Production Processes Research Articles

The Use of Orthoses Made With 3D Printer In Upper Extremity Rehabilitation: A Review

Recent advancements in technology have brought about a significant transformation in the healthcare industry, particularly in the rehabilitation services sector, with the adoption of 3D (3 Dimensional) printing. This technology plays a crucial role in designing customized, aesthetically pleasing, lightweight, and eco-friendly orthoses for upper extremity rehabilitation. 3D scanning systems accurately predict the production process of orthoses, making it easier to maintain records of orthotic models. These recorded models make repetitive and batch production processes more efficient and accessible. However, the use of 3D printers in developing countries is limited due to high costs. Additionally, there is a lack of evidence-based practices. To overcome these challenges, efforts are underway to improve software interfaces for orthotic production and integrate 3D printed orthoses into upper extremity rehabilitation models. Further research is necessary to explore the optimal parameters for 3D printing in upper extremity orthotic production across diverse demographics.

Shewhart Control Charts Implementation for Quality and Production Management

Shewhart control charts are suitable for stable but repetitive production processes used for the subsequent identification of random deviations while indicating breached quality limits. They provide information on process variability and, at the same time, make it possible to obtain information on the reliability of monitored processes. The objective of this paper is to assess the quality characteristics of plastic mouldings for the needs of the automotive industry with the application of the control charts method, specifically Shewhart control charts. The Shewhart control charts were applied to evaluate the quality characteristics, or, more specifically, to evaluate the measured width and length of the produced plastic mouldings by statistical analysis. Statistical analyses show that the set parameters are not met in the first two days of the test series. An improvement in the process is observed on the last day of the test series. The process is well set, as confirmed by our verification of the stability of the process. An important condition for setting the control charts is to observe the correct chronological arrangement and regular acquisition of measured values. Solving tasks in the future must be oriented to an evaluation of the capability of the production process of the monitored product. The proposal for future research will be oriented toward the evaluation of this capability via process capability indices that derive continuous data by using the classical method.

Designing a No-Wait Cyclic Schedule for a Class of Concurrent Repetitive Production Processes

The concurrent repetitive manufacturing processes sharing resources according to a mutual exclusion protocol are considered. A system of the processes is a composition of subsystems that consist of n cyclic processes sharing one resource. In case of resource conflicts between the processes regarding access to the resources a certain priority is used (e.g. FIFO rule) which guarantees starvation-free access of the processes to the shared resources. A class of systems which are structurally deadlock-free is considered, i.e. resource requests of repetitive processes can’t create closed loop. For this class of systems is searched conflict-free, cyclic schedule for which the processes never wait for the resources, i.e. resource conflicts are avoided and a priority rule is not in the operation. A structure of production processes sharing resources and the operation times are defined. The problem of computing the starting times of the processes for which a no-wait cyclic schedule exists is formulated in a declarative way using constraint programming (CP) method. The necessary and sufficient conditions for existence of a no-wait schedule in the n-process system are given and used to compute the possible schedules of the processes. An illustrative example is presented.

Optimal control of an imperfect repetitive synchronised production environment

We develop optimal sublot policies to minimise total expected costs in a failure-prone two-stage, high-volume repetitive production process which manufactures batches of the same item over a long period of time. The first stage starts in control but may fail at an exponentially distributed random time, producing only good units before failure and only defective units afterward. Failure is detected upon encountering defective units in the second stage, at which time the first stage process may be restored to good condition. To enhance quality, a production lot in Stage 1 is subdivided into sublots which are transferred as soon as possible to Stage 2 for inspection and processing. We determine exact closed-form continuous solutions as well as an efficient algorithm for finding optimal integer sublots. We provide managerial insights as to what control mechanism would be adopted depending on the state of the production process parameters. [Received 14 May 2015; Revised 16 February 2016; Accepted 1 March 2016]

Using Runs Rules to Monitor an Attribute Chart for a Markov Process

For some repetitive production processes, the quality measure taken on the output is an attribute variable. An attribute variable classifies each output item into one of a countable set of categories. One of the simplest and most commonly used attribute variables is the one that classifies an item as either “conforming” or “nonconforming.” A tool used with a considerable amount of success in industry for monitoring the quality of a production process is the quality control chart. In this paper, a sequence of random variables, Xi, i = 1, 2, … , is used to classify an item as conforming or nonconforming under a stationary Markov chain model and under 100% sequential sampling. The sequence of random variables to be plotted on the control chart is Y, i = 1, 2, … , where Y1 counts the number of conforming items before the first nonconforming item and Yi, i = 2, 3,. counts the number of conforming items between the (i-l)th and the ih nonconforming items. In the literature, Yi is called the CRL (Conforming Run Length). The contribution of this paper to the literature is to present runs rules for an attribute control chart of this type. The efficiency of these charts is evaluated using the average run length (ARL) of the charts. The supplemental runs rules that are presented are two out of three values of Yi, i = 1,2, … falling below determined lower limits, four out of five values of Yi, i = 1,2, … falling below determined lower limits, and eight out of eight values of Yi, i = 1,2,... falling below determined lower limits.

Conditional and marginal performance of the Poisson CUSUM control chart with parameter estimation

Cumulative Sum (CUSUM) type control charts are widely used in industry because of their effectiveness for process control. The Poisson CUSUM is a powerful and easy-to-implement control chart, which is appropriate for monitoring the counts of nonconformities in a unit from a repetitive production process. In the literature, control chart performances are generally evaluated under the assumption of known in-control process parameters. However, in-control process parameters are rarely known in practice and often parameter estimates from a reference sample are used instead. As a consequence of the additional variability introduced by parameter estimation, operational performance of a control chart might differ from the expected performance when the parameters are known. In this paper, effect of estimated process mean on the conditional and marginal performance of the Poisson CUSUM chart are quantified. The Markov Chain approach is used for calculating the aspects of the run length distribution. The effect of estimation on the in-control average run length performance is shown to be significant. Sample-size recommendations are provided.

Attribute Charts for Monitoring a Dependent Process

AbstractFor some repetitive production processes, the quality measure taken on the output is an attribute variable. An attribute variable classifies each output item into one of a countable set of categories. One of the simplest and most commonly used attribute variables is the one which classifies an item as either ‘conforming’ or ‘non‐conforming’. A tool used with a considerable amount of success in industry for monitoring the quality of a production process is the quality control chart. Generally a control charting procedure uses a sequence, $X_1 ,X_{2,} \ldots ,X_t, \ldots$ of the quality measures to make a decision about the quality of the process. How this sequence is used to make a decision defines the control chart. In order to design a control chart one must consider how the underlying sequence, $X_1 ,X_{2,} \ldots,X_t, \ldots,$ is modeled. The sequence is often modeled as a sequence of independent and identically distributed random variables. For many industrial processes, this model is appropriate, but in others it may not be. In this paper, a sequence of random variables, $X_i ,\ i = 1,2,\ldots,$ is used to classify an item as conforming or non‐conforming under a stationary Markov chain model and under 100% sequential sampling. Two different control charting schemes are investigated. Both schemes plot a sequence of measures on the control chart, $Y_i ,\ i = 1,2,\ldots$ that count the number of conforming items before a non‐conforming item. The first scheme signals as out‐of‐control if a value of $Y_i ,\ i = 1,2,\ldots$ falls below a certain lower limit. The second scheme signals as out‐of‐control if two out of two values of $Y_i ,\ i = 1,2,\ldots$ fall below a certain lower limit. The efficiency of both of the control charts is evaluated by the average run length (ARL) of the chart and the power of the chart to detect a shift in the process. The two out of two scheme is shown to have high power and a large ARL given certain parameter values of the process. An example of the two out of two scheme is provided for the interested reader. Copyright © 2006 John Wiley & Sons, Ltd.

The Effect of Estimated Parameters on Poisson EWMA Control Charts

Performance of control charts is generally evaluated with the assumption that the process parameters are known. In many control chart applications, however, the process parameters are rarely known and their estimates from an in-control reference sample are used instead. In such cases, the moments of the run length distribution depend on the values of the estimated parameters. The Poisson exponentially weighted moving average (EWMA) is an effective control chart in situations where the number of nonconformities per unit from a repetitive production process is monitored. The objective of this paper is to study the effect of estimating the mean on the performance of the Poisson EWMA control chart. We make use of the Markov Chain approach. Sample-size recommendations and some concluding comments are provided.

The Quality of Standard, Routine and Nonroutine Processes

Efforts to increase organizational effectiveness using standardization and quality techniques have been successful in repetitive production and administrative processes but less so when dealing with nonroutine processes typical of professional organizations. Routines are defined very broadly in organization theory as either mind-numbing repetition, repositories of knowledge, or effortful accomplishments. In this article, processes are analysed as systems with distinct input assessment, algorithms and output-generating action phases. These are structured differently depending on how they are set up to deal with variation (deviations from explicit targets) and variety (distinct but functionally equivalent targets). Thus processes can be classified into three types. Standard processes are set up to deal with a single variety using binary logic. Routine processes can distinguish a limited amount of variety using fuzzy logic. Nonroutine processes are open systems in which unrestricted variety is interpreted and assigned meaning. The implications of these process types are discussed in terms of the identification of quality errors and defects, change and learning.

Team time: a model for developing self‐directed work teams

Distinguishes two different forms of team: the han and the self‐directed work team. The main differences between these forms relate to autonomy and the number of hierarchical levels. Argues that the ideal typical conditions under which both forms flourish are different. Whereas the han form is typically applied in a highly repetitive production process in a country with a number of specific national conditions, namely Japan, the self‐directed work team seems preferable in a dynamic environment in western countries. Implementing and developing such teams is by no means an automatic process. Presents a heuristic model to guide these processes.

Scheduling Method of Lot Operating Sequence in Semiconductor Fabrication

This paper discusses the lot scheduling method in a semiconductor fabrication, which is a typical repetitive production process. As for the algorithm to schedule lot operating sequence, simulated annealing (SA) method is applied to this scheduling formulated in the combinatorial optimization problem between machines and silicon wafer lots. To accelerate convergence of SA computation keeping up the solution quality, a new technique for reduction of the problem size and improvement of neighborhood creation are developed. Moreover, in our system, material flow in every process is simulated to consider not only the lot inventory but also the future lot arrival.

Dynamic analysis of repetitive decision-free discreteevent processes: The algebra of timed marked graphs and algorithmic issues

A model to analyze certain classes of discrete event dynamic systems is presented. Previous research on timed marked graphs is reviewed and extended. This model is useful to analyze asynchronous and repetitive production processes. In particular, applications to certain classes of flexible manufacturing systems are provided in a companion paper. Here, an algebraic representation of timed marked graphs in terms of reccurrence equations is provided. These equations are linear in a nonconventional algebra, that is described. Also, an algorithm to properly characterize the periodic behavior of repetitive production processes is descrbed. This model extends the concepts from PERT/CPM analysis to repetitive production processes.