Types Of Quality Problems Research Articles

Implementasi SQC (Statistical Quality Control) dalam Proses Pascapanen Tebu di PG. Madukismo

PG. Madukismo is a company engaged in the processing of sugarcane into sugar. In the sugar production process, in terms of raw materials, there are still mixtures or components of sugarcane that are not needed and desired. This is caused by external and internal factors of the company which will certainly affect the quality of the products produced. The purpose of this study is to identify the quality of sugarcane by using Statistical Quality Control (SQC) analysis. This study uses the seven tools method for quality control with the results of the research showing the cause of dirty sugarcane is around 14.44% of the total received sugarcane due to other components besides sugarcane that are needed such as dry leaves, roots, soil and etc . It was found that the types of quality problems that occurred in sugarcane received by the factory in 2021 were gross 1 (K1) with a percentage of 82.6%, gross 2 (K2) with a percentage of 6.5% and gross 3 (K3) with a percentage of 10.9%. The quality problem of sugarcane received by the factory is caused by human factors (workers), materials, methods, machines and the environment.

A big data-driven root cause analysis system: Application of Machine Learning in quality problem solving

Root cause analysis for quality problem solving is critical to improve product quality performance and reduce the quality risk for manufacturers. Subjective conventional methods have been applied frequently in past decades. However, due to increasingly complex product and supply chain structures, diverse working conditions, and massive amounts of components, accuracy and efficiency of root cause analysis are progressively challenged in practice. Therefore, data-driven root cause analysis methods have attracted attention lately. In this paper, taking advantage of the availability of big operations data and the rapid development of data science, we design a big data-driven root cause analysis system utilizing Machine Learning techniques to improve the performance of root cause analysis. More specifically, we first propose a conceptual framework of the big data-driven root cause analysis system including three modules of Problem Identification, Root Cause Identification, and Permanent Corrective Action. Furthermore, in the Problem Identification Module, we construct a unified feature-based approach to describe multiple and different types of quality problems by applying a data mining method. In the Root Cause Identification Module, we use supervised Machine Learning (classification) methods to automatically predict the root causes of multiple quality problems. Finally, we illustrate the accuracy and efficiency of the proposed system and algorithms based on actual quality data from a case company. This study contributes to the literature from the following aspects: (i) the integrated system and algorithms can be used directly to develop a computer application to manage and solve quality problems with high concurrences and complexities in any manufacturing process; (ii) a general procedure and method are provided to formulate and describe a large quantity and different types of quality problems; (iii) compared with traditional methods, it is demonstrated using real case data that manufacturing companies can save significant time and cost with our proposed data-driven root cause analysis system; (iv) this study not only aims at improving the quality problem solving practices for a complex manufacturing process but also bridges a gap between the theoretical development of Machining Learning methods and their application in the operations management domain.

Hot Deformation of 18 % Ni Maraging Steels: A Review

Abstract The published literature makes reference to the excellent hot workability of 18 % nickel maraging steels. However, production of large cross-sectioned products of 18 % nickel maraging steels by hot deformation can lead to different types of quality problems, such as (i) inadequate recrystallization and grain structure aligned in working direction, resulting in directionality of mechanical properties; (ii) thermal embrittlement that leads to poor values of ductility and fracture toughness; and (iii) sharp increase in flow stress with decreasing hot working temperature, necessitating the application of high loads for deformation at relatively low temperatures. To overcome these problems, primarily two sets of studies have been carried out by researchers. One set of studies was centered around the development of an optimum post–hot work solution treatment. Two-stage solution treatments, as opposed to standard single-stage solution treatment at 820°C, have been developed and are shown to lead to a fine-grained microstructure and an excellent combination of strength and fracture toughness with a high degree of isotropy. It has been documented that such heat treatments can also rejuvenate the quality of the products produced through a nonoptimum hot deformation schedule. The other set of studies was focused on establishing deformation processing maps using hot isothermal uniaxial compression testing. Different grades of 18 % nickel maraging steels have been the subject of investigation. Based on these studies, regimes of strain rate and temperature have been identified for optimum processing. Regimes in which flow localization occurs and hence need to be avoided have been identified. Processing regimes for obtaining fine-grained microstructures have been identified; regimes that lead to coarse grains, which need to be avoided, have been established. Constitutive equations have been developed to predict the deformation behavior as an aid to the design of the hot deformation process.

Research of Reverse Engineering on Dimensional Accuracy of Parts in Digital Casting Process

Dimensional accuracy has been recognized as the main challenges for castings that we have to overcome as manufacturing industry is proceeding at an astounding rate. The advent of rapid casting has already changed the casting patterns, which means that foundry of metal part is no longer restricted by difficulties of tooling processing especially in the case of complex design castings. It is true that this new technology which is time-saving and high-precision relatively may create new types of quality problems. The overall effort of the present article is to evaluate the dimensional accuracy of sand mold and metal part throughout the digital casting process so that a more accurate dimensional compensation could be applied to the next time. The dimensional accuracy of sand mold manufactured by 3D Printer and the final aluminum-based tire mold casted by the sand mold have been discussed quantitatively by using three-dimensional laser scanner. The effect of different directions on dimensional deviation of important surface is also discussed in this paper. It is found that the difference of sand mold mainly depends on the deviation in printing direction and a downward trend of the patterned surface would appear after the post curing. For the metal casting, appropriate dimensional compensation 1mm/m upon the pattern surface in Z direction is a good choice for further designing. This reverse engineering approach is applicable to not only the tire mold forming but also other industrially relevant parts manufacturing.

What fixes can improve cancer care quality at safety-net hospitals?

243 Background: Safety-net hospitals are at higher risk of poor quality cancer care and vary in levels of quality performance. Types of quality problems may differ and require different interventions. We undertook this study to identify organizational factors affecting quality and assess the early findings of the innovation’s impact on hand-offs between specialists. Methods: As part of a RCT testing a web-based tracking and feedback innovation to close referral loops, new breast cancer cases are rapidly ascertained, and post-surgical medical and radiation oncologist visits are assessed. Surgeons are alerted to patients who do not show to oncology visits to stimulate action. Of 153 enrolled women (98 Intervention/ 55 Control), 4% are white. In addition, we conducted 90 key informant interviews in 9 safety-net hospitals in the NYC metropolitan area and performed a qualitative comparative analysis (QCA), a method to assess causal complexity, to identify organizational factors associated with poor quality. Results: No-shows at oncology appointments triggering surgeon feedback occurred for 5/98 (5%) new breast cancer patients. QCA revealed 6 conditions associated with poor quality-defined as underuse of needed adjuvant treatment: information sharing; follow-up; system support; patient-centered culture; flexibility and private practice. All high-performing hospitals had a patient-centered culture, strong follow-up approaches and shared information. Some of these high-quality sites had robust system support while others had creative workarounds (flexibility) or were responsive to their private practice patients. Overall, these pathways had high consistency (.88) and coverage (.72). Only hospitals with both poor follow-up and system support activated the web-based feedback. Conclusions: Safety-net hospitals with poor follow-up capabilities may benefit from an innovation designed to improve hand-offs across specialties. This trial continues to accrue; we are monitoring the innovation’s effectiveness. Clinical trial information: NCT01544374.

Intensity assignments from historical earthquake data: issues of certainty and quality

The use of macroseismic data in assessing parameters for historical earthquakes for use in seismic hazard assessment has thrown more attention on the way in which these data are treated. The processes involved in selecting which macroseismic data from a historical earthquake survive to the present day can be modelled as a series of filters, most of which are outside the control of the seismologist/historian, and which cause distortion in the resulting picture of the earthquake. The ways in which the data become distorted should be taken into account when interpreting the data as intensity values. One can usefully discriminate between the certainty of an intensity assignment (how well the data fits the scale) and the quality of an intensity assignment (how well one can trust that the value is a true reflection of what really happened). The expression of uncertainty is usually in the form of ranged intensity values; the expression of quality requires an extra symbol or rating of some sort. A system is presented for three types of quality problems: reliability of intensity assessment, locational certainty or uncertainty, and veracity of the original data. Each of these is treated as a binary variable, giving a final quality code ranging from 0 (best) to 7 (worst). This single integer quality code preserves three types of information which can then be expanded as required by computer programs designed to handle macroseismic data.

Web information quality as seen from the libraries

Discusses different types of quality problems having their roots in the Internet and some possible solutions. Gives a provisional definition of “World Wide Web information quality”. Presents some of the findings from an empirical survey of Danish academic and business users of the Internet. Concludes that only natural intelligence will be able to cope with the enormous task of filtering and distilling usable and useful information needles from the Internet haystack. Development of new and more efficient tools for the retrieval of quality information is extremely urgent.