Reduce Process Variability Research Articles

Framework-based on SQC and DMAIC to monitor die casting process parameters

The article attempts to reduce process variation and improve the product quality by using both off-line and online quality control techniques. This task may be achieved by developing an integrated model based on statistical quality control (SQC) and Six Sigma-based D-M-A-I-C (define-measure-analyse-improve-control). The goal of study was achieved by using various quality control tools viz. brainstorming sessions, histograms, failure mode effect analysis (FMEA), cause and effect diagram, control charts, principle component analysis and response surface methodology. Principle component analysis (PCA) was employed for building of the process representation and Hotelling T2 chart based on the principle component score was used for detection of abnormal behaviour. Further response surface methodology was used to optimise the process parameters. There was a reduction in the overall process variability by employment of optimised process parameters. The suggested integrated model seems to be effective for process monitoring of the proposed case study and may be used to monitor other similar processes as well.

Six Sigma implementation in a manufacturing unit - a case study

The Six Sigma approach has been increasingly adopted in the manufacturing sector in order to make the process robust to quality variations, thereby, improving the quality and performance. In any manufacturing industry, Shainin Six Sigma methodology is highly adopted because of lesser data collection required for the study. Six Sigma improves the process performance of the critical operational process, leading to better utilisation of resources, decrease in variations and maintaining consistent quality of the process output. The DMAIC approach has been followed here to solve an underlying problem of reducing process variation and the associated high defect rate. This paper describes an application of Six Sigma define, measure, analyse, improve and control (DMAIC) methodology in a manufacturing unit, which provides a framework to identify, quantify and eliminate sources of variation in an operational process to improve the quality. The current investigation focuses on using DMAIC methodology to reduce the rejections in the pasting process during product X manufacturing, resulting in a saving of Rs. 3.96 lakhs per annum

A case study: to reduce process variability of valve seat depth in cylinder head using Six Sigma methodology

The research has been carried out to introduce Six Sigma methodology in India, particularly in manufacturing SMEs. This paper deliberates the quality and productivity improvement in a cylinder head manufacturing enterprise through a case study and deals with an application of Six Sigma methodology in an industry which offers a framework to identify, quantify and abolish sources of variation in the manufacturing process of cylinder head, to optimise the operation variables, improve and endure performance viz. process yield through well-executed control plans. To support in the fulfilment of the purpose a practical DMAIC project was conducted. Six Sigma will provide a structure and training in tools, thereby ensuring that the tools are used at the right time and in the right way at the machining division. Application of Six Sigma methodology has resulted in enormous financial savings for the industry.

Metal Catalyzed Electrochemical Synthesis of Hydrocarbons from Epitaxial Graphene

Pt/Au transition metal catalysts are shown to significantly improve the electrochemical hydrogenation of low defect density epitaxial graphene (EG) layers ∼2ML thick on Si-face 6H-SiC Initial experiments using Pt nanoparticles deposited from aqueous solution showed large increases in hydrogenation, however, this method demonstrated large variability, attributed to the aqueous nature of the catalyst deposition, known to change the conductivity and reactivity of graphene, as well as clustering of Pt nanoparticles. To reduce process variability, 20 nm thick evaporated metals were used. From Raman, the average observed hydrogen cluster density ∼1011 cm−2 was similar to the average densities of intrinsic defects (1010,11 cm−2) and charge impurities (1011 cm−2) consistent with recent reports that these sites play a crucial role in the hydrogenation of EG. Au catalysts (3.23 ± 0.22%wt) showed slightly better H-incorporation than Pt (2.81 ± 0.03%wt), an observation we attribute to the fact that H2 dissociation is no longer the rate-limiting step, as H+ is readily available in the aqueous state. The electrochemical enablement of these hydrogenation pathways using catalysts offers new routes to carbon chemistries for hydrogen storage, bandgap engineered hydrocarbons as well as EG-based battery electrodes.

PLANTWIDE PERIODICAL DISTURBANCES ISOLATION AND ELIMINATION IN A PETROCHEMICAL UNIT

Reducing process variability is crucial to reach a more profitable operating point. Periodical disturbances, however, impose barriers to achieve this goal. Their effect can be strong since one disturbance that appears in a specific loop of a highly coupled plant can be seen in several loops. Thus, isolating their source and diagnosing their cause are essential. In this work, we describe the application of spectral independent component analysis to isolate a periodical disturbance that has a strong impact on the final variability in a polyethylene plant located in Southern Brazil. After the first analysis, the source was detected and the cause identified: valve stiction. To identify the cause (valve, bad tuning, or periodic disturbance), we used the methodology based on higher-order statistics. Once the valve problem had been overcome, the product variance was reduced by 93%.

Evaluation of Interior Staff and Subcontractor Performance

(1) Total Quality (TQ) is and ought to be the standard for evaluating interior staff and subcontractor performance. The three key elements of Total Quality Management (TQM) that were also the main criteria for evaluating interior staff and subcontractor performance, are enumerated by Cohen (1993) as follows:(2) Working with suppliers to ensure that the supplies utilized in the work processes are designed for the organization's use;(3) Continuous employee analysis of work processes to improve their functioning and reduce process variation; andClose communication with customers to identify and understand what they want and how they define quality. (p.18)This study aimed to explore which of the three variables is the subject organization weak in with the objective of eventually improving in this area.A survey questionnaire was randomly given out to fifty (50) select clients, customers, or patrons, subcontractors, or interior staff members. The questionnaires functioned as a gauge to measure the performance of the organization's interior staff and subcontractors against the given criteria.Furthermore, a thorough analysis of available literature and of previous studies on the matter was done for purposes of comparing and contrasting the results of the survey.

Performance Evaluation of Three PID Controller Tuning Algorithm on a Process Plant

Accurate tuning of controller in industrial process operation is prerequisite to system smooth operation which directly reduce process variability, improved efficiency, reduced energy costs, and increased production rates. Performance evaluation of a model based PID controller tuning algorithm on a chemical process plant is presented in this paper. The control action of three different PID controller tuning algorithms namely; Hagglund-Astrom, Cohen and Coon, and Ziegler-Nichols on the process plant was examined in a closed loop control configuration under normal operating condition and in the face of disturbance. LabVIEW software was used to model a chemical process plant from open loop control test data. The time domain response analysis of the controllers shows that each tuning algorithm exhibit different time response. Ziegler-Nichols algorithm shows the best performance with fastest rise time, settling time and was able to restore the system back to normal operating condition in a short time when subjected to disturbance compare to Cohen & Coon controller and Hagglund-Astrom algorithm settings.

Advanced Semiconductor Manufacturing Using Big Data

This paper describes the development and the actual utilization of fab-wide fault detection and classification (FDC) for the advanced semiconductor manufacturing using big data. In the fab-wide FDC, the collection of equipment's big data for the FDC judgment is required; hence, we developed the equipment monitoring system that handles the data in a superior method in high speed and in real time. We succeeded in stopping equipment and lots automatically when the equipment was detected as fault condition. In addition, we developed the environment that enables immediate data collection for analysis by the data aggregation and merging functions, which extracts keys correlating to yield from the equipment's parameter. Furthermore, we succeeded in development of the high-speed and high-accuracy process control system that implemented virtual metrology and the run-to-run function for the purpose to reduce process variation.

Process analytical technologies in food industry - challenges and benefits: A status report and recommendations.

Process analytical technologies in food industry - challenges and benefits: A status report and recommendations.

Variation source identification of multistage manufacturing processes through discriminant analysis and stream of variation methodology: a case study in automotive industry

Product quality problem is a critical issue for multistage manufacturing processes, especially in continuous production lines whereby quality characteristics are measured at the end of the line. Therefore, it is important to reduce process variation by identifying its sources and eliminating its causes. In this regard, a novel approach, to identify the source of variation in multistage manufacturing processes through integration of the Fisher's linear discriminant analysis and the stream of variation methodology, is proposed. Linear discriminant analysis separates the variation of the quality characteristics through the manufacturing process stages while the stream of variation methodology is used for variation propagation modeling in multistage manufacturing processes. Finally, the future deviation is assigned into the analysis in order to identify the source of variation. With an illustrative case study, it is concluded that the proposed approach improves fault diagnosis of continuous production lines in multistage manufacturing processes.

Making Operational Strategies of Asphalt Teams Explicit to Reduce Process Variability

The on-site construction process undertaken by asphalt teams has a critical impact on pavement quality. Process improvement and learning require explicit information about the process. However, current on-site operational activities and key parameters are, in general, not systematically monitored and mapped. The lack of process information makes it difficult for contractors and asphalt teams to distinguish between good and poor practices and to improve. Although technologies to make the on-site process explicit are becoming widely available, their adoption has been slow. To overcome this knowledge gap regarding explicit information about the on-site construction process, this paper proposes a framework and utilizes technologies for the systematic monitoring and mapping of on-site activities and key parameters. Various technologies and sensors, such as a global positioning system (GPS), a laser linescanner, and infrared cameras, make it possible to track the on-site movements of machinery and asphalt temperatures during construction. This framework was applied and refined during 29 asphalting projects in the Netherlands, creating an extensive set of on-site process data. Considerable variability was found in the delivered asphalt temperatures, the asphalt cooling, the compaction process and density progression, and the movements of machinery. This variability offers opportunities where action could be taken to improve process quality by reducing process variability. The framework and explicit data can help asphalt teams to verbalize their tacit knowledge and make their own processes and choices transparent and further promotes learning processes. This paper contributes to a deeper understanding of the on-site construction process and highlights how to encourage technology adoption in construction

Mathematical Approach for Determining the Number of Kanbans: A Step towards Non-Stock Production in Manufacturing Scenario

Just-in-time (JIT) production is a philosophy that calls for reducing work-in-process (WIP) inventory to aid process improvement and reduce process variability. Kanban act as the nerve of a JIT production system whose functions is to direct materials just-in-time to workstations in stages of manufacturing, and pass information as to what and how much to produce. Indeed, the number of kanbans between two adjacent workstations decides the inventory level of that pair of workstations. With the objective of minimizing WIP inventory level, is developed for deciding the optimum number of kanbans. In the present work the mathematical model for calculating optimum number of kanbans and solution procedure are illustrated with a numerical example.

Technical Report: New developments in Total Quality Management

This paper considers the application of Total Quality Management (TQM) to the aerospace and defence industries. An introduction to the history, definition and principles of TQM is given first. Briefly, TQM involves a customer focus, the prevention of defects rather than the detection, reducing chronic waste in time and material, pursuing continuous quality improvements, employing structured problem–solving methods, using statistical tools to reduce process variation, and recognizing the importance of people in the total process. The role and importance of process variation in the quality of products or services is discussed. The circumstances of applying TQM to the organizations described is presented. An account of the experiences and results of some sample companies adopting the TQM philosophy is discussed. Many of the organizations considered are adopting this philosophy as a means of adapting to and surviving in a rapidly changing business environment.

A systematic literature review of Six Sigma practices in education

Six Sigma is a continuous improvement methodology aimed at reducing process variation. As Six Sigma implementation has spread from manufacturing to service and healthcare, the need for teaching the methodology in higher education emerged to prepare students for industry. The purpose of this paper is to present a systematic review the relevant literature of Six Sigma in higher education. Insights on best practices and trends for integrating Six Sigma in higher education systems are also provided.

The importance of training for sustainable growth: Example of Six Sigma implementation

Improvement of the business environment, education and training are the basis for sustainable growth. Implementation of Six Sigma (6σ) concept and appropriate techniques and tools for quality management contribute to the efficiency and effectiveness of the business as a whole, in a manner that organization achieves and maintains competitive advantage by reducing process variability, costs of scraps, price and need for control, shortening production cycle and increasing product quality, customer satisfaction and profit. Successful implementation of Six Sigma (6σ) concept requires involvement of all employees and appropriate level of training.

Reducing breast cancer chemotherapy treatment delays by improving the transition from surgical to medical oncology.

51 Background: Delayed chemotherapy is associated with adverse clinical outcomes for breast cancer patients. Few studies have examined the processes of care within administrative staff and providers’ control that might affect the timeliness of breast cancer care. Coordinating transitions of care from surgical to medical oncology to eliminate variation is essential in ensuring timely chemotherapy for patients after surgery. This study evaluated the time of transition from surgery to medical oncology when administrative change of practice was implemented. Methods: We studied 192 consecutive breast cancer patients who received adjuvant chemotherapy. The interval between last definitive surgery and initiation of chemotherapy was calculated by integrating billing and scheduling data. Using process improvement methods a multidisciplinary team identified the opportunity to reduce delays in care coordination by scheduling the surgery, surgical post-op appointment, and medical oncology follow-up appointments simultaneously. Furthermore, responsibility for scheduling the medical oncology follow-up appointment was shifted and standardized from medical to surgical oncology administrative staff. Criteria for acceptable timeliness of appointments and escalation pathways for when provider availability was limited were established. The intervention targeted patients whose initial consultation with a breast surgeon and medical oncologist occurred on the same day, the standard practice at our institution. Results: Implementation of the interventions decreased the time from surgery to chemotherapy by six days. The standard deviation also declined, suggesting that the intervention reduced process variability. Conclusions: Standardizing administrative practices between breast surgery and medical oncology has led to a significant decrease in the time from last definitive surgery to initiation of chemotherapy and in the variability of that delay. [Table: see text]

Design of a nanopower current reference with reduced process variability

In this paper we present the design of a 0.18 μm CMOS current reference based on a variability-aware approach, in such a way to obtain a very low process sensitivity of the reference current. Its relative standard deviation is 1.4 % based on measurements performed over 23 samples from a single batch. The requirement of low process sensitivity is met together with the very low power consumption of 290 nW, at the cost of a large area occupation of 0.245 mm2. Key to obtain this result are the use of the "classical" bipolar bandgap topology, which can be optimized for low-power/low-spread operation so as to outperform MOS-based bandgap circuits, and the use of devices that are intrinsically more stable towards process, such as diffusion resistors.

31.1: A 3.5Gbps/Lane Intra‐panel Interface with a PVT‐robust VCO Based CDR for UD TV Applications in 0.18μm High‐voltage CMOS Technology

AbstractA CMOS voltage‐controlled oscillator (VCO) with robustness under PVT variation is proposed, and its application to a PLL‐based CDR is also presented. In the VCO design, voltage regulator is used to suppress voltage variation and improved voltage‐to‐current (V/I) converter is used to mitigate temperature variation and varactor‐tuned ring oscillator is used to reduce process variation. For broad bandwidth, comparator adopts the active negative feedback circuit and Signal Integrity (SI) on PCB channel is also improved considering via effect. Simulation and test results under 0.18‐μm high‐voltage CMOS technology show that the VCO output frequency range is from 300MHz to 700MHz and the maximum intra‐panel interface operating speed is up to 3.5Gbps per lane for UD LCD‐TV applications.

Optical In-Situ Plasma Process Monitoring Technique for Detection of Abnormal Plasma Discharge

Advanced semiconductor manufacturing technology requires methods to maximize tool efficiency and improve product quality by reducing process variability. Real-time plasma process monitoring and diagnosis have become crucial for fault detection and classification (FDC) and advanced process control (APC). Additional sensors may increase the accuracy of detection of process anomalies, and optical monitoring methods are non-invasive. In this paper, we propose the use of a chromatic data acquisition system for real-time in-situ plasma process monitoring called the Plasma Eyes Chromatic System (PECS). The proposed system was initially tested in a six-inch research tool, and it was then further evaluated for its potential to detect process anomalies in an eight-inch production tool for etching blanket oxide films. Chromatic representation of the PECS output shows a clear correlation with small changes in process parameters, such as RF power, pressure, and gas flow. We also present how the PECS may be adapted as an in-situ plasma arc detector. The proposed system can provide useful indications of a faulty process in a timely and non-invasive manner for successful run-to-run (R2R) control and FDC.

An orthogonal arrays approach to robust parameter designs methodology

Robust parameter design methodology was originally introduced by Taguchi [14] as an engineering methodology for quality improvement of products and processes. A robust design of a system is one in which two different types of factors are varied; control factors and noise factors. Control factors are variables with levels that are adjustable, whereas noise factors are variables with levels that are hard or impossible to control during normal conditions, such as environmental conditions and raw-material properties. Robust parameter design aims at the reduction of process variation by properly selecting the levels of control factors so that the process becomes insensitive to changes in noise factors. Taguchi [1415] proposed the use of crossed arrays (inner–outer arrays) for robust parameter design. A crossed array is the cross-product of an orthogonal array (OA) involving control factors (inner array) and an OA involving noise factors (outer array). Objecting to the run size and the flexibility of crossed arrays, several authors combined control and noise factors in a single design matrix, which is called a combined array, instead of crossed arrays. In this framework, we present the use of OAs in Taguchi's methodology as a useful tool for designing robust parameter designs with economical run size.