Dokumen tersebut membahas tujuh alat bantu kualitas (QC tools) yang sering digunakan untuk menganalisis dan meningkatkan proses, yaitu stratifikasi, lembar data, grafik, diagram Pareto, histogram, diagram Ishikawa, dan diagram Tebar. Setiap alat dijelaskan fungsi dan cara pembuatannya."
Acceptance sampling untuk data variabelMahros Darsin
Dokumen tersebut membahas tentang acceptance sampling untuk data variabel. Dibahas mengenai keunggulan dan kelemahan metode sampling variabel dibandingkan dengan atribut, tipe-tipe rencana sampling variabel, dasar-dasar rencana sampling untuk variabel, serta peringatan dalam menggunakan metode tersebut. Juga dibahas mengenai standar military standard MIL STD 414 dan ANSI/ASQC Z1.9-1993 untuk rencana sampling variabel.
Mata kuliah ini membahas tentang pengetahuan dan kemampuan dalam pengendalian kualitas berdasarkan teori statistik serta perancangan standar kualitas, dengan materi pokok seperti manajemen kualitas, pengukuran kualitas, dan sistem pengendalian kualitas."
Dokumen tersebut membahas tujuh alat bantu kualitas (QC tools) yang sering digunakan untuk menganalisis dan meningkatkan proses, yaitu stratifikasi, lembar data, grafik, diagram Pareto, histogram, diagram Ishikawa, dan diagram Tebar. Setiap alat dijelaskan fungsi dan cara pembuatannya."
Acceptance sampling untuk data variabelMahros Darsin
Dokumen tersebut membahas tentang acceptance sampling untuk data variabel. Dibahas mengenai keunggulan dan kelemahan metode sampling variabel dibandingkan dengan atribut, tipe-tipe rencana sampling variabel, dasar-dasar rencana sampling untuk variabel, serta peringatan dalam menggunakan metode tersebut. Juga dibahas mengenai standar military standard MIL STD 414 dan ANSI/ASQC Z1.9-1993 untuk rencana sampling variabel.
Mata kuliah ini membahas tentang pengetahuan dan kemampuan dalam pengendalian kualitas berdasarkan teori statistik serta perancangan standar kualitas, dengan materi pokok seperti manajemen kualitas, pengukuran kualitas, dan sistem pengendalian kualitas."
Dokumen tersebut membahas tentang kontrol kualitas, tujuannya untuk menjaga kualitas produk sesuai rencana dengan melibatkan seluruh bagian. Metode yang dijelaskan adalah brainstorming, why-why analysis, dan fishbone diagram untuk mengidentifikasi akar masalah seperti cacat produk dan gangguan mesin.
MTM (Methods Time Measurement) adalah metode pengukuran waktu tidak langsung yang digunakan untuk menganalisis pekerjaan. MTM membagi pekerjaan menjadi delapan elemen gerakan dasar dan menentukan waktu untuk setiap elemen berdasarkan faktor-faktor seperti jarak, berat, dan ketelitian. Contoh kasus menggunakan MTM untuk menghitung waktu pemindahan bagian mesin dan perakitan bolpoin.
Dokumen tersebut membahas tentang Cara Pembuatan Kosmetik yang Baik (CPKB) yang merupakan pedoman wajib bagi industri kosmetik untuk menghasilkan produk yang memenuhi standar mutu dan keamanan serta mampu bersaing di pasar global. CPKB mencakup aspek-aspek seperti sistem manajemen mutu, personalia, fasilitas produksi, sanitasi, kontrol mutu, dan dokumentasi.
Ringkasan dokumen tersebut adalah sebagai berikut:
Fault Tree Analysis (FTA) adalah teknik untuk mengidentifikasi risiko yang berperan dalam kegagalan suatu sistem dengan menganalisis penyebab-penyebab kegagalan secara hierarkis dari atas ke bawah mulai dari kejadian puncak hingga akar penyebabnya. FTA digunakan untuk menentukan penyebab kemungkinan terjadinya kegagalan, menemukan tahap
FORM-01-Formulir Pemeriksaan Bahan Baku Pangan dan Kemasan.pdfhernuwaluyo
Formulir pemeriksaan bahan baku pangan dan kemasan digunakan untuk mencatat hasil pemeriksaan bahan baku yang diterima dari pemasok. Formulir ini mencakup informasi tentang kode pemasok, nama barang, kode batch produksi, jumlah diterima, kondisi pengangkutan, kondisi bahan baku segar atau olahan, kondisi kemasan dan label, serta status penerimaan.
Materi kuliah Perancangan Sistem Kerja & Ergonomi di Program Studi Teknik Industri xmembahas topik Lingkungan Kerja Bagian 2 tentang Kebisingan (Noise), Temperatur (Heat & Cold Stress), dan Getaran
Dokumen tersebut merupakan standar nasional Indonesia tentang persyaratan mutu air minum dalam kemasan. Dokumen tersebut menjelaskan tentang ruang lingkup, acuan, istilah, syarat mutu, cara pengambilan contoh, cara pengujian, syarat lulus pengujian, higiene, pengemasan, dan syarat penandaan untuk air minum dalam kemasan.
This document contains tables of compound interest factors for interest rates of 1/4%, 1/2%, and 3/4% compounded annually. The tables show the factors needed to calculate future and present values of single payments, uniform series payments, and arithmetic gradient series payments over a range of time periods from 1 to 480 years.
The document contains tables from the MIL-STD-105E standard for sampling inspection plans. It includes tables for single, double, and multiple sampling plans for normal, tightened, and reduced inspection at various acceptable quality levels. It also provides guidelines for implementing MIL-STD-105E, including determining sample size based on lot size, selecting the appropriate sampling plan table, and rules for switching between inspection levels.
This document discusses military standards for acceptance sampling, including Military Standard 105E and Military Standard 414. MIL STD 105E provides sampling schemes for attributes data using single, double, or multiple sampling plans. It describes normal, tightened, and reduced inspection levels based on a vendor's quality history. MIL STD 414 provides variables acceptance sampling plans that use sample sizes based on lot size and inspection level, assuming the quality characteristic is normally distributed. It includes plans based on sample standard deviation, range, and known process standard deviation. The document provides examples of using these standards to determine acceptance sampling plans.
Dokumen tersebut membahas tentang kontrol kualitas, tujuannya untuk menjaga kualitas produk sesuai rencana dengan melibatkan seluruh bagian. Metode yang dijelaskan adalah brainstorming, why-why analysis, dan fishbone diagram untuk mengidentifikasi akar masalah seperti cacat produk dan gangguan mesin.
MTM (Methods Time Measurement) adalah metode pengukuran waktu tidak langsung yang digunakan untuk menganalisis pekerjaan. MTM membagi pekerjaan menjadi delapan elemen gerakan dasar dan menentukan waktu untuk setiap elemen berdasarkan faktor-faktor seperti jarak, berat, dan ketelitian. Contoh kasus menggunakan MTM untuk menghitung waktu pemindahan bagian mesin dan perakitan bolpoin.
Dokumen tersebut membahas tentang Cara Pembuatan Kosmetik yang Baik (CPKB) yang merupakan pedoman wajib bagi industri kosmetik untuk menghasilkan produk yang memenuhi standar mutu dan keamanan serta mampu bersaing di pasar global. CPKB mencakup aspek-aspek seperti sistem manajemen mutu, personalia, fasilitas produksi, sanitasi, kontrol mutu, dan dokumentasi.
Ringkasan dokumen tersebut adalah sebagai berikut:
Fault Tree Analysis (FTA) adalah teknik untuk mengidentifikasi risiko yang berperan dalam kegagalan suatu sistem dengan menganalisis penyebab-penyebab kegagalan secara hierarkis dari atas ke bawah mulai dari kejadian puncak hingga akar penyebabnya. FTA digunakan untuk menentukan penyebab kemungkinan terjadinya kegagalan, menemukan tahap
FORM-01-Formulir Pemeriksaan Bahan Baku Pangan dan Kemasan.pdfhernuwaluyo
Formulir pemeriksaan bahan baku pangan dan kemasan digunakan untuk mencatat hasil pemeriksaan bahan baku yang diterima dari pemasok. Formulir ini mencakup informasi tentang kode pemasok, nama barang, kode batch produksi, jumlah diterima, kondisi pengangkutan, kondisi bahan baku segar atau olahan, kondisi kemasan dan label, serta status penerimaan.
Materi kuliah Perancangan Sistem Kerja & Ergonomi di Program Studi Teknik Industri xmembahas topik Lingkungan Kerja Bagian 2 tentang Kebisingan (Noise), Temperatur (Heat & Cold Stress), dan Getaran
Dokumen tersebut merupakan standar nasional Indonesia tentang persyaratan mutu air minum dalam kemasan. Dokumen tersebut menjelaskan tentang ruang lingkup, acuan, istilah, syarat mutu, cara pengambilan contoh, cara pengujian, syarat lulus pengujian, higiene, pengemasan, dan syarat penandaan untuk air minum dalam kemasan.
This document contains tables of compound interest factors for interest rates of 1/4%, 1/2%, and 3/4% compounded annually. The tables show the factors needed to calculate future and present values of single payments, uniform series payments, and arithmetic gradient series payments over a range of time periods from 1 to 480 years.
The document contains tables from the MIL-STD-105E standard for sampling inspection plans. It includes tables for single, double, and multiple sampling plans for normal, tightened, and reduced inspection at various acceptable quality levels. It also provides guidelines for implementing MIL-STD-105E, including determining sample size based on lot size, selecting the appropriate sampling plan table, and rules for switching between inspection levels.
This document discusses military standards for acceptance sampling, including Military Standard 105E and Military Standard 414. MIL STD 105E provides sampling schemes for attributes data using single, double, or multiple sampling plans. It describes normal, tightened, and reduced inspection levels based on a vendor's quality history. MIL STD 414 provides variables acceptance sampling plans that use sample sizes based on lot size and inspection level, assuming the quality characteristic is normally distributed. It includes plans based on sample standard deviation, range, and known process standard deviation. The document provides examples of using these standards to determine acceptance sampling plans.
La Norma Mil. Std. 105D establece procedimientos de muestreo para inspección por atributos. Proporciona métodos de muestreo normales y severos, así como reglas para cambiar entre ellos dependiendo de la calidad de los lotes. Ofrece tablas con letras código para determinar el tamaño de la muestra, curvas características de operación, y valores numéricos para apoyar la aplicación de los procedimientos de muestreo descritos. La norma busca forzar a los proveedores a producir al menos un producto con la cal
This document discusses quality control for garment manufacturing. It outlines the key aspects of quality control including establishing specifications, inspecting raw materials like fabric and threads, in-process inspection of sewing and assembly, final inspection of garments, methods like AQL sampling, and product testing for properties like colorfastness and durability. The goal of quality control is to detect defects at all stages of production and ensure garments meet specifications for attributes like measurements, appearance, and quality of construction.
This document provides strategies for constructing effective presentation slides. It discusses keeping slides simple, knowing the core message, focusing each slide on one main point, and repeating key information across multiple slides and presentation levels. Graphs, images, and other visual elements should be used to reinforce the message. An effective presentation follows a process of planning the situation, audience, theme, organization, and visual components to clearly convey the intended message.
El documento trata sobre diferentes temas relacionados con planes de muestreo para inspección y aceptación de lotes, incluyendo índices de calidad, curvas de operación, límites de calidad promedio de salida, comparación entre planes ANSI-ASQC Z1.4-2008 y C=0, y métodos de aceptación por muestreo. Explica conceptos como riesgo en muestreo, construcción e interpretación de curvas características de operación, y ventajas y desventajas del muestreo de aceptación.
Este documento describe el muestreo para aceptación, el cual se utiliza para decidir si un lote de producción cumple con los requisitos de calidad acordados. Explica que el muestreo para aceptación involucra inspeccionar una muestra del lote y aceptar o rechazar el lote completo en base a los resultados. También define conceptos clave como planes de muestreo simples, dobles y múltiples, y discute las ventajas y desventajas del muestreo para aceptación en comparación con la inspección del 100% de las
Sistema de muestreo de aceptacion utilizando la norma ANSI/ASQ Z 1.4michael1220
en esta pequeña presentancion pero muy importante usted podra encontrar las tablas de Dodge-Roming el uso de los planes de muestreo sencillo atte: ING. Maykoll Perez
This document establishes sampling procedures and reference tables for inspection by attributes. It provides sampling plans and procedures to determine the quality of lots or batches of items based on the probabilistic occurrence of defects. The plans and tables contained within can be used to guide the development of an inspection strategy that provides an effective and efficient approach to attaining required technical quality levels.
El documento describe el proceso de muestreo y los tipos de planes de muestreo, incluyendo muestreo aleatorio, estratificado, por atributos y por variabilidad. También explica las clases de planes de muestreo como simple, doble y secuencial/múltiple y los niveles de aceptabilidad de calidad como defecto crítico, mayor y menor. Además, define la tabla militar como una herramienta para realizar muestreos totales o parciales de medicamentos y dispositivos médicos que llegan a una farmacia
Muestreo Aceptacion por atributos GeneralidadesLuis Dicovskiy
Este documento describe los conceptos y tipos de muestreo de aceptación para el control de calidad. El muestreo de aceptación permite inspeccionar solo una muestra de un lote para decidir si aceptar o rechazar el lote completo. Se describen los planes de muestreo simple, doble y múltiple, así como los planes de muestreo por atributos y variables. Finalmente, se explica cómo establecer los niveles de calidad aceptable y tolerable para diseñar un plan de muestreo simple.
The document discusses various techniques for calculating integrals or antiderivatives. It explains that integration is the reverse process of differentiation and involves calculating the area under a function's curve. Some key techniques covered include: using basic integration rules for polynomials, separating fractions into partial fractions, making substitutions to transform integrals into standard forms, and applying trigonometric identities to integrals with trigonometric functions. It also discusses evaluating definite integrals between limits by calculating the antiderivative at the upper and lower bounds and taking the difference.
Dokumen tersebut membahas tentang konversi satuan suhu antara Celcius, Fahrenheit, Rankine, dan Kelvin. Juga membahas tentang fase padat, cair, dan gas, serta suhu kritis beberapa gas seperti oksigen, nitrogen, dan hidrogen. Terdapat pula penjelasan mengenai tabel uap, uap jenuh, dan uap super panas.
Analisis kimia meliputi analisis kualitatif untuk mengidentifikasi komponen dan analisis kuantitatif untuk menentukan perbandingan komponen. Metode analisis meliputi gravimetri, volumetri, elektrokimia, spektrofotometri, dan kromatografi. Analisis volumetri melibatkan pengukuran volume larutan standar yang bereaksi dengan larutan yang diuji untuk menentukan kadar zat tertentu.
Dokumen ini membahas tentang spektrofotometri UV-Vis dan pendekatan kasus untuk menganalisis spektrum yang ditampilkan. Berisi pertanyaan-pertanyaan untuk mengidentifikasi jenis kasus dan hubungan antara dua gambar spektrum yang ditampilkan.
Materi kuliah tentang Suku banyak. Cari lebih banyak mata kuliah Semester 1 di: http://muhammadhabibielecture.blogspot.com/2014/12/kuliah-semester-1-thp-ftp-ub.html
Membranes are thin films that allow some types of matter to pass through while restricting others. They work by exploiting differences in molecular size, shape, or solubility. There are various types of membrane processes that separate components of a solution based on factors like pressure, concentration, voltage, or temperature differences. These include reverse osmosis, microfiltration, ultrafiltration, nanofiltration, and others. Membrane processes have many applications in food processing, such as concentrating fruit juices, separating whey from cheese, and purifying water and wastewater. Key factors that determine membrane performance include thickness, molecular structure, chemical composition, and configuration.
Mata kuliah matematika tentang Limit dan kekontinuan. Cari lebih banyak materi kuliah semester 3 di: http://muhammadhabibielecture.blogspot.com/2014/12/kuliah-semester-1-thp-ftp-ub.html
Dokumen tersebut membahas tentang titrasi kimia sebagai metode analisis kuantitatif untuk menentukan konsentrasi larutan dengan menggunakan larutan standar. Dibahas pula peralatan dan prosedur titrasi asam-basa seperti penggunaan buret, erlenmeyer, indikator, serta reaksi antara asam dan basa.
The document provides tables from MIL-STD-105E for sample size selection and acceptance/rejection numbers. It includes single, double, and multiple sampling plans for normal, tightened, and reduced inspection levels across a range of lot/batch sizes and acceptable quality levels. The tables allow users to determine appropriate sampling parameters based on their lot/batch size, inspection level, and quality requirements.
1. Statistical process control (SPC) methods are useful for mass production but not for job shop production where each product is unique. Quality in job shops can be ensured by controlling input quality, process conditions, and output quality checks.
2. The quality control manager should be involved in planning quality, not just policing it. However, the quality control function needs separation from production to allow an independent quality assessment.
3. Specification limits define the acceptable performance range for customers, while control limits are set narrower by the producer to monitor the process and ensure it stays within specifications. Process capability affects whether control limits can keep the process within specifications.
Statistical Process Control (SPC) is used to monitor production processes and detect issues that cause poor quality. Control charts created from sample data show if a process is behaving normally or abnormally. There are different types of control charts for attributes (pass/fail data) and variables (measured data). Patterns in control charts can indicate when a process has shifted or become more variable, signaling the need for corrective action. SPC is applied not just to manufacturing but also services to monitor quality measures over time.
This document provides an outline for a chapter on statistical process control from an operations management textbook. It covers the basics of statistical process control including control charts for attributes and variables. It discusses control chart patterns and how to apply statistical process control to services. Control charts establish control limits to monitor if a production process is within normal variability or suggests an issue like non-random variation.
Here is another simplified presentation about the process of Distracter Analysis in Educational Assessments. Included here are terminologies, reminders, definitions, as well as process in conducting it. Thank you. Namaste.
Statistical process control ppt @ doms Babasab Patil
This document provides an overview of statistical process control (SPC). It discusses the basics of SPC including control charts for attributes and variables. Control charts monitor a production process to detect issues. Attribute charts like p-charts and c-charts monitor defects, while variable charts like x-bar and R-charts monitor measured values. The document also discusses applying SPC to services and provides examples of constructing and interpreting control charts using Excel and Minitab. Process capability and identifying special causes of variation from control chart patterns are also covered.
Acceptance sampling is used to determine if the quality level of a production lot is acceptable. It provides an economical alternative to 100% inspection. Key aspects include determining the quality level, ensuring it is within limits, and basing acceptance on sample size (n), acceptance number (c), and number of defectives (d). If d is less than or equal to c, the lot is accepted. If d is greater than c, the lot is rejected. Typical applications involve drawing a sample, inspecting it, and using statistical criteria to decide whether to accept or reject the entire lot.
This document discusses statistical quality control (SQC) and its three categories: descriptive statistics, statistical process control (SPC), and acceptance sampling. It describes how SPC uses control charts to monitor quality characteristics and identify variations in processes. Control charts for variables monitor characteristics like mean and variability, while control charts for attributes count discrete values. The document also covers process capability analysis using metrics like Cp and Cpk to assess how well a process meets specifications. It compares ±3 sigma and ±6 sigma quality standards.
The document contains data from test sections and questions. It includes the number of questions answered correctly and incorrectly in various test sections, as well as difficulty levels for questions. It also shows how to calculate raw scores from the number of questions answered correctly and use conversion tables to determine scaled scores for the math, critical reading and writing sections of a practice SAT exam.
The document discusses quality control and statistical quality control. It defines quality as properties valued by consumers and quality control as maintaining standards through testing samples. The goal of quality control is to eliminate nonconformities and wasted resources at lowest cost. Statistical quality control uses statistical tools like descriptive statistics, acceptance sampling, and statistical process control to measure and control variation in processes. Examples are provided of x-bar and R charts to determine if a gluing process is in control, as well as P and C charts to monitor defects and complaints.
This document provides an overview of statistical quality control (SQC). It describes the three main categories of SQC as descriptive statistics, statistical process control (SPC), and acceptance sampling. Key aspects of SPC covered include identifying sources of variation, using control charts for variables and attributes, calculating process capability indices, and the concepts of six-sigma quality. Acceptance sampling is introduced as inspecting a sample from a batch to determine if the entire batch meets quality standards.
This document provides an overview of statistical quality control (SQC). It describes the three main categories of SQC as descriptive statistics, statistical process control (SPC), and acceptance sampling. Control charts are discussed as a key SPC tool used to monitor processes and identify variations. The concepts of process capability, six sigma quality levels, and acceptance sampling plans are also introduced.
This document discusses statistical process control and control charts. It defines the goals of control charts as collecting and visually presenting data to see when trends or out-of-control points occur. Process control charts graph sample data over time and show the process average and upper and lower control limits. Attribute control charts indicate whether points are in or out of tolerance, while variables charts measure attributes like length, weight or temperature over time. Examples are provided to illustrate p-charts, R-charts and X-bar charts using hotel luggage delivery time data.
Response Surface Regression - a useful tool for data mining, historical data analysis, and identifying critical factors in your process optimization efforts.
The document describes an experiment to determine which factors affect the loosening of threaded inserts in butterfly plates. The factors tested were tooling, cati-coat application, minor diameter, pitch diameter, and insert type. Analysis found that tooling, cati-coat, and minor diameter had significant main effects, while the interaction of tooling and cati-coat also had a significant effect. To minimize insert movement, the optimal settings are using the old tooling, no cati-coat application, and a minor diameter of 0.171 inches.
The document provides an overview of statistical quality control (SQC) including definitions, characteristics, causes of variation, methods, process control charts, acceptance sampling, and risks. It discusses control charts for variables like X-bar, R, and sigma charts and attributes like p, np, and C charts. Acceptance sampling involves inspecting lots to determine if they meet quality standards and addresses producer's and consumer's risks. Single, double, and multiple sampling plans are described.
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1. The document discusses statistical quality control (SQC) methods including statistical process control (SPC), descriptive statistics, acceptance sampling, control charts, process capability analysis, and six sigma.
2. SPC uses control charts to monitor quality characteristics and identify sources of variation. Descriptive statistics are used to describe data distributions and central tendencies.
3. Acceptance sampling randomly inspects batches to determine acceptance or rejection. Control charts like X-bar, P, and C charts help monitor different quality characteristics.
4. Process capability analysis compares process variation to specification limits using metrics like Cp and Cpk. Six sigma aims for very low defect levels.
Lipids are an important structural component of cells and serve as an energy source. They include fatty acids, glycerolipids, phospholipids, sphingolipids, and sterols. Lipids are digested in the stomach and small intestine where enzymes break them into fatty acids and monoacylglycerols absorbed by intestinal cells. Chylomicrons transport the products into lymph and blood circulation. Fatty acids are used for energy production or stored as triglycerides in adipose tissue. Cholesterol is an important component of cell membranes and a precursor for bile acids, hormones, and vitamins. Eicosanoids are hormone-like compounds derived from fatty acids that regulate processes like blood pressure, inflammation, and
Spl Pengolahan Limbah Gas FTP UB 150702072311-lva1-app6892Muhammad Luthfan
Pencemaran udara dan pengolahan limbah gas merupakan masalah lingkungan yang penting. Dokumen ini membahas sumber-sumber pencemar udara, jenis pencemaran, serta metode pengendalian pencemaran secara teknis dan non-teknis melalui pengaturan hukum dan penggunaan teknologi seperti electrostatic precipitator, wet scrubber, dan lainnya.
Listeria adalah bakteri Gram positif yang dapat diisolasi dari tanah dan silase. Bakteri ini dapat menyebabkan infeksi seperti septicemia, meningitis, dan infeksi janin pada wanita hamil yang dapat menyebabkan keguguran. Populasi rentan terhadap listeriosis antara lain wanita hamil, sistem kekebalan tubuh lemah, kanker, dan lansia. Diagnosis pasti membutuhkan isolasi bakteri dari darah atau cairan serebrosp
Dasar Keteknikan (Dastek) Pengolahan Pangan FTP UB 150529064527-lva1-app6891Muhammad Luthfan
The document discusses food process engineering which includes converting raw materials into ready or processed foods through various unit operations like heat transfer, drying, evaporation, and mechanical separations. It explains that food processes can be broken down into a small number of basic unit operations and outlines the aims of the food industry as extending shelf life, increasing variety, providing nutrients, and generating income. Food processes are usually represented through flow charts that show the flow of materials and energy.
PUP (Perencanaan Unit Pengolahan) Utilitas Air 160704042806Muhammad Luthfan
Dokumen tersebut membahas tentang utilitas pabrik yang meliputi pengolahan air dan limbah, pembangkit listrik, dan pembangkit uap. Dokumen ini juga menjelaskan siklus air, parameter kualitas air, dan proses pengolahan air untuk keperluan industri."
Electrophoresis is a method used to separate molecules like DNA or proteins based on their size and charge. An electric current passed through a gel matrix like agarose or polyacrylamide causes the molecules to separate as they migrate through the gel at different rates. Smaller, less charged molecules move faster through the pores in the gel. Factors like gel concentration, buffer conditions, and voltage applied affect the resolution of separation. After electrophoresis, gels are typically stained to visualize the separated DNA or protein bands.
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
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You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
1. KUMPULAN TABEL MIL-STD-105E
Ir. Budi Nurtama, M.Agr.
2008
Program Studi Supervisor Jaminan Mutu Pangan
PROGRAM DIPLOMA
Institut Pertanian Bogor
2. i
Tables for MIL-STD-105E *)
Page
Table I. Sample-Size Code Letters. 1
Table II-A. Single Sampling Plans for Normal Inspection (Master Table). 2
Table II-B. Single Sampling Plans for Tightened Inspection (Master Table). 3
Table II-C. Single Sampling Plans for Reduced Inspection (Master Table). 4
Table III-A. Double Sampling Plans for Normal Inspection (Master Table). 5
Table III-B. Double Sampling Plans for Tightened Inspection (Master Table). 6
Table III-C. Double Sampling Plans for Reduced Inspection (Master Table). 7
Table IV-A. Multiple Sampling Plans for Normal Inspection (Master Table). 8
Table IV-A.(continued) 9
Table IV-B. Multiple Sampling Plans for Tightened Inspection (Master Table). 10
Table IV-B.(continued). 11
Table IV-C. Multiple Sampling Plans for Reduced Inspection (Master Table). 12
Table IV-C.(continued). 13
Table V-A. Average Outgoing Quality Limit Factors for Normal Inspection (Single Sampling). 14
Table V-A. Average Outgoing Quality Limit Factors for Tightened Inspection (Single Sampling). 15
Table VIII. Limit Numbers for Reduced Inspection. 16
*)
Farnum, Nicholas R. 1994. Modern Statistical Quality Control and Improvement. Wadsworth, Inc., California.
3. ii
Tahapan Implementasi MIL-STD-105E
1. Tentukan AQL (Acceptable Quality Level) berdasarkan perjanjian produsen dan pelanggan.
2. Tetapkan modus dan tingkat inspeksi (jika tidak, gunakan Normal Inspection, Level II ).
3. Tentukan ukuran lot (lot size).
4. Gunakan tabel Sample Size Code Letters untuk memilih huruf kode yang sesuai.
5. Tetapkan tipe prosedur pengambilan sampel : single, double, atau multiple sampling.
6. Gunakan tabel yang berkaitan dengan prosedur pengambilan sampel terpilih (tahap 5) dan modus/tingkat inspeksi (tahap 2) untuk mendapatkan ukuran sampel
dan angka penerimaan (Ac) serta angka penolakan (Re). Dalam kasus dimana suatu rancangan tidak ada untuk ukuran lot dan AQL-nya, hati-hati mengikuti
tanda panah dalam tabel ke rancangan terdekat yang ada.
7. Mulailah gunakan rancangan tersebut dan catat penerimaan dan penolakan sehingga switching rule dapat diterapkan. Jika switching dilakukan, tentukan modus
dan tingkat dan ulangi tahap 4-6 untuk mendapatkan rancangan pengambilan sampel yang baru.
SWITCHING RULE for MIL-STD-105E
• Dari Normal ke Tightened Inspection : setelah 2 lot ditolak dari 2, 3, 4, atau 5 lot berurutan
• Dari Tightened ke Normal Inspection : setelah 5 lot berurutan diterima
• Dari Normal ke Reduced Inspection : jika (i) 10 lot berurutan diterima, dan (ii) jumlah total nonconforming items dalam 10 lot tersebut tidak melebihi batas
pada Table VIII. Limit Numbers for Reduced Inspection., dan (iii) produksi stabil/mantap, dan (iv) disetujui oleh otoritas
yang bertanggungjawab.
• Dari Reduced ke Normal Inspection : jika (i) suatu lot tunggal ditolak, atau (ii) suatu lot hanya sebagian dapat diterima (dhi. jumlah nonconforming items
diantara angka diterima dan ditolak), atau (iii) produksi menjadi tidak stabil, atau (iv) kondisi-kondisi lain yang
menjamin bahwa inspeksi Normal dapat dilakukan.
4. 1
Table I. Sample-Size Code Letters.
Lot or batch size
Special Inspection Levels General Inspection Levels
S-1 S-2 S-3 S-4 I II III
2 to 8
9 to 15
16 to 25
A
A
A
A
A
A
A
A
B
A
A
B
A
A
B
A
B
C
B
C
D
26 to 50
51 to 90
91 to 150
A
B
B
B
B
B
B
C
C
C
C
D
C
C
D
D
E
F
E
F
G
151 to 280
281 to 500
501 to 1,200
B
B
C
C
C
C
D
D
E
E
E
F
E
F
G
G
H
J
H
J
K
1,201 to 3,200
3,201 to 10,000
10,001 to 35,000
C
C
C
D
D
D
E
F
F
G
G
H
H
J
K
K
L
M
L
M
N
35,001 to 150,000
150,001 to 500,000
500,001 and over
D
D
D
E
E
E
G
G
H
J
J
K
L
M
N
N
P
Q
P
Q
R
5. 2
Table II - A. Single Sampling Plans for Normal Inspection (Master Table).
Sample
size
code
letter
Sample
size
Acceptable Quality Levels (normal inspection)
0.010 0.015 0.025 0.040 0.065 0.10 0.15 0.25 0.40 0.65 1.0 1.5 2.5 4.0 6.5 10 15 25 40 65 100 150 250 400 650 1000
Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re
A
B
C
2
3
5 0 1
0 1
0 1
1 2
1 2
2 3
1 2
2 3
3 4
2 3
3 4
5 6
3 4
5 6
7 8
5 6
7 8
10 11
7 8
10 11
14 15
10 11
14 15
21 22
14 15
21 22
30 31
21 22
30 31
44 45
30 31
44 45
D
E
F
8
13
20 0 1
0 1
0 1
1 2
1 2
2 3
1 2
2 3
3 4
2 3
3 4
5 6
3 4
5 6
7 8
5 6
7 8
10 11
7 8
10 11
14 15
10 11
14 15
21 22
14 15
21 22
21 22
30 31
30 31
44 45
44 45
G
H
J
32
50
80 0 1
0 1
0 1
1 2
1 2
2 3
1 2
2 3
3 4
2 3
3 4
5 6
3 4
5 6
7 8
5 6
7 8
10 11
7 8
10 11
14 15
10 11
14 15
21 22
14 15
21 22
21 22
K
L
M
125
200
315 0 1
0 1
0 1
1 2
1 2
2 3
1 2
2 3
3 4
2 3
3 4
5 6
3 4
5 6
7 8
5 6
7 8
10 11
7 8
10 11
14 15
10 11
14 15
21 22
14 15
21 22
21 22
N
P
Q
500
800
1250 0 1
0 1
0 1
1 2
1 2
2 3
1 2
2 3
3 4
2 3
3 4
5 6
3 4
5 6
7 8
5 6
7 8
10 11
7 8
10 11
14 15
10 11
14 15
21 22
14 15
21 22
21 22
R 2000 1 2 2 3 3 4 5 6 7 8 10 11 14 15 21 22
= Use first sampling plan below arrow. If sample size equals or exceeds lot or batch size, do 100 percent inspection. Ac = Acceptance number.
= Use first sampling plan above arrow. Re = Rejection number.
6. 3
Table II - B. Single Sampling Plans for Tightened Inspection (Master Table).
Sample
size
code
letter
Sample
size
Acceptable Quality Levels (tightened inspection)
0.010 0.015 0.025 0.040 0.065 0.10 0.15 0.25 0.40 0.65 1.0 1.5 2.5 4.0 6.5 10 15 25 40 65 100 150 250 400 650 1000
Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re
A
B
C
2
3
5 0 1
0 1
1 2
1 2
2 3
1 2
2 3
3 4
2 3
3 4
5 6
3 4
5 6
8 9
5 6
8 9
12 13
8 9
12 13
18 19
12 13
18 19
27 28
18 19
27 28
41 42
27 28
41 42
D
E
F
8
13
20 0 1
0 1
0 1
1 2
1 2
2 3
1 2
2 3
3 4
2 3
3 4
5 6
3 4
5 6
8 9
5 6
8 9
12 13
8 9
12 13
18 19
12 13
18 19
18 19
27 28
27 28
41 42
41 42
G
H
J
32
50
80 0 1
0 1
0 1
1 2
1 2
2 3
1 2
2 3
3 4
2 3
3 4
5 6
3 4
5 6
8 9
5 6
8 9
12 13
8 9
12 13
18 19
12 13
18 19
18 19
K
L
M
125
200
315 0 1
0 1
0 1
1 2
1 2
2 3
1 2
2 3
3 4
2 3
3 4
5 6
3 4
5 6
8 9
5 6
8 9
12 13
8 9
12 13
18 19
12 13
18 19
18 19
N
P
Q
500
800
1250 0 1
0 1
0 1
1 2
1 2
2 3
1 2
2 3
3 4
2 3
3 4
5 6
3 4
5 6
8 9
5 6
8 9
12 13
8 9
12 13
18 19
12 13
18 19
18 19
R
S
2000
3150
0 1
1 2
1 2 2 3 3 4 5 6 8 9 12 13 18 19
= Use first sampling plan below arrow. If sample size equals or exceeds lot or batch size, do 100 percent inspection. Ac = Acceptance number.
= Use first sampling plan above arrow. Re = Rejection number.
7. 4
Table II - C. Single Sampling Plans for Reduced Inspection (Master Table).
Sample
size
code
letter
Sample
size
Acceptable Quality Levels (reduced inspection)
0.010 0.015 0.025 0.040 0.065 0.10 0.15 0.25 0.40 0.65 1.0 1.5 2.5 4.0 6.5 10 15 25 40 65 100 150 250 400 650 1000
Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re
A
B
C
2
2
2 0 1
0 1
0 1
0 2
0 2
1 3
1 2
1 3
1 4
2 3
2 4
2 5
3 4
3 5
3 6
5 6
5 6
5 8
7 8
7 8
7 10
10 11
10 11
10 13
14 15
14 15
14 17
21 22
21 22
21 24
30 31
30 31
D
E
F
3
5
8 0 1
0 1
0 1
0 2
0 2
1 3
0 2
1 3
1 4
1 3
1 4
2 5
1 4
2 5
3 6
2 5
3 6
5 8
3 6
5 8
7 10
5 8
7 10
10 13
7 10
10 13
10 13
14 17
14 17
21 24
21 24
G
H
J
13
20
32 0 1
0 1
0 1
0 2
0 2
1 3
0 2
1 3
1 4
1 3
1 4
2 5
1 4
2 5
3 6
2 5
3 6
5 8
3 6
5 8
7 10
5 8
7 10
10 13
7 10
10 13
10 13
K
L
M
50
80
125 0 1
0 1
0 1
0 2
0 2
1 3
0 2
1 3
1 4
1 3
1 4
2 5
1 4
2 5
3 6
2 5
3 6
5 8
3 6
5 8
7 10
5 8
7 10
10 13
7 10
10 13
10 13
N
P
Q
200
315
500 0 1
0 1
0 1
0 2
0 2
1 3
0 2
1 3
1 4
1 3
1 4
2 5
1 4
2 5
3 6
2 5
3 6
5 8
3 6
5 8
7 10
5 8
7 10
10 13
7 10
10 13
10 13
R 800 0 2 1 3 1 4 2 5 3 6 5 8 7 10 10 13
= Use first sampling plan below arrow. If sample size equals or exceeds lot or batch size, do 100 percent inspection. Ac = Acceptance number.
= Use first sampling plan above arrow. Re = Rejection number.
= If the acceptance number has been exceeded, but the rejection number has not been reached; accept the lot, but reinstate normal inspection.
8. 5
Table III - A. Double Sampling Plans for Normal Inspection (Master Table).
Sample
size
code
letter
Sample
Sample
size
Cumula
tive
sample
size
Acceptable Quality Levels (normal inspection)
0.010 0.015 0.025 0.040 0.065 0.10 0.15 0.25 0.40 0.65 1.0 1.5 2.5 4.0 6.5 10 15 25 40 65 100 150 250 400 650 1000
Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re
A
B First
Second
2
2
2
4
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
17 22
37 38
25 31
56 57
C First
Second
3
3
3
6
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
17 22
37 38
25 31
56 57
D First
Second
5
5
5
10
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
17 22
37 38
25 31
56 57
E First
Second
8
8
8
16
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
17 22
37 38
25 31
56 57
F First
Second
13
13
13
26
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
G First
Second
20
20
20
40
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
H First
Second
32
32
32
64
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
J First
Second
50
50
50
100
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
K First
Second
80
80
80
160
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
L First
Second
125
125
125
250
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
M First
Second
200
200
200
400
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
N First
Second
315
315
315
630
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
P First
Second
500
500
500
1000
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
Q First
Second
800
800
800
1600
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
R First
Second
1250
1250
1250
2500
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
8 9
5 9
12 13
7 11
18 19
11 16
26 27
= Use first sampling plan below arrow. If sample size equals or exceeds lot or batch size, do 100 percent inspection. Ac = Acceptance number.
= Use first sampling plan above arrow. Re = Rejection number.
= Use corresponding single sampling plan (or alternatively, use double sampling plan below, where available).
9. 6
Table III - B. Double Sampling Plans for Tightened Inspection (Master Table).
Sample
size
code
letter
Sample
Sample
size
Cumula
tive
sample
size
Acceptable Quality Levels (normal inspection)
0.010 0.015 0.025 0.040 0.065 0.10 0.15 0.25 0.40 0.65 1.0 1.5 2.5 4.0 6.5 10 15 25 40 65 100 150 250 400 650 1000
Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re
A
B First
Second
2
2
2
4
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
15 20
34 35
23 29
52 53
C First
Second
3
3
3
6
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
15 20
34 35
23 29
52 53
D First
Second
5
5
5
10
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
15 20
34 35
23 29
52 53
E First
Second
8
8
8
16
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
15 20
34 35
23 29
52 53
F First
Second
13
13
13
26
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
G First
Second
20
20
20
40
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
H First
Second
32
32
32
64
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
J First
Second
50
50
50
100
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
K First
Second
80
80
80
160
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
L First
Second
125
125
125
250
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
M First
Second
200
200
200
400
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
N First
Second
315
315
315
630
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
P First
Second
500
500
500
1000
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
Q First
Second
800
800
800
1600
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
R First
Second
1250
1250
1250
2500
0 2
1 2
0 3
3 4
1 4
4 5
2 5
6 7
3 7
11 12
6 10
15 16
9 14
23 24
S First
Second
2000
2000
2000
4000
0 2
1 2
= Use first sampling plan below arrow. If sample size equals or exceeds lot or batch size, do 100 percent inspection. Ac = Acceptance number.
= Use first sampling plan above arrow. Re = Rejection number.
= Use corresponding single sampling plan (or alternatively, use double sampling plan below, where available).
10. 7
Table III - C. Double Sampling Plans for Reduced Inspection (Master Table).
Sample
size
code
letter
Sample
Sample
size
Cumula
tive
sample
size
Acceptable Quality Levels (reduced inspection)
0.010 0.015 0.025 0.040 0.065 0.10 0.15 0.25 0.40 0.65 1.0 1.5 2.5 4.0 6.5 10 15 25 40 65 100 150 250 400 650 1000
Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re
A
B
C
D First
Second
2
2
2
4
0 2
0 2
0 3
0 4
0 4
1 5
0 4
3 6
1 5
4 7
2 7
6 9
3 8
8 12
5 10
12 16
7 12
18 22
11 17
26 30
E First
Second
3
3
3
6
0 2
0 2
0 3
0 4
0 4
1 5
0 4
3 6
1 5
4 7
2 7
6 9
3 8
8 12
5 10
12 16
7 12
18 22
11 17
26 30
F First
Second
5
5
5
10
0 2
0 2
0 3
0 4
0 4
1 5
0 4
3 6
1 5
4 7
2 7
6 9
3 8
8 12
5 10
12 16
G First
Second
8
8
8
16
0 2
0 2
0 3
0 4
0 4
1 5
0 4
3 6
1 5
4 7
2 7
6 9
3 8
8 12
5 10
12 16
H First
Second
13
13
13
26
0 2
0 2
0 3
0 4
0 4
1 5
0 4
3 6
1 5
4 7
2 7
6 9
3 8
8 12
5 10
12 16
J First
Second
20
20
20
40
0 2
0 2
0 3
0 4
0 4
1 5
0 4
3 6
1 5
4 7
2 7
6 9
3 8
8 12
5 10
12 16
K First
Second
32
32
32
64
0 2
0 2
0 3
0 4
0 4
1 5
0 4
3 6
1 5
4 7
2 7
6 9
3 8
8 12
5 10
12 16
L First
Second
50
50
50
100
0 2
0 2
0 3
0 4
0 4
1 5
0 4
3 6
1 5
4 7
2 7
6 9
3 8
8 12
5 10
12 16
M First
Second
80
80
80
160
0 2
0 2
0 3
0 4
0 4
1 5
0 4
3 6
1 5
4 7
2 7
6 9
3 8
8 12
5 10
12 16
N First
Second
125
125
125
250
0 2
0 2
0 3
0 4
0 4
1 5
0 4
3 6
1 5
4 7
2 7
6 9
3 8
8 12
5 10
12 16
P First
Second
200
200
200
400
0 2
0 2
0 3
0 4
0 4
1 5
0 4
3 6
1 5
4 7
2 7
6 9
3 8
8 12
5 10
12 16
Q First
Second
315
315
315
630
0 2
0 2
0 3
0 4
0 4
1 5
0 4
3 6
1 5
4 7
2 7
6 9
3 8
8 12
5 10
12 16
R First
Second
500
500
500
1000
0 2
0 2
0 3
0 4
0 4
1 5
0 4
3 6
1 5
4 7
2 7
6 9
3 8
8 12
5 10
12 16
= Use first sampling plan below arrow. If sample size equals or exceeds lot or batch size, do 100 percent inspection.
= Use first sampling plan above arrow.
Ac = Acceptance number.
Re = Rejection number.
= Use corresponding single sampling plan (or alternatively, use double sampling plan below, where available).
= If, after the second sample, the acceptance number has been exceeded, but the rejection number has not been reached, accept the lot, but reinstate normal inspection.
11. 8
Table IV - A. Multiple Sampling Plans for Normal Inspection (Master Table).
Sample
size
code
letter
Sample
Sample
size
Cumula
tive
sample
size
Acceptable Quality Levels (normal inspection)
0.010 0.015 0.025 0.040 0.065 0.10 0.15 0.25 0.40 0.65 1.0 1.5 2.5 4.0 6.5 10 15 25 40 65 100 150 250 400 650 1000
Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re
A
B
C ++
++
++
++
++
++
++
++
++
++
++
++
++
++
++
++
++
++
++
++
D First
Second
Third
Fourth
Fifth
Sixth
Seventh
2
2
2
2
2
2
2
2
4
6
8
10
12
14
# 2
# 2
0 2
0 3
1 3
1 3
2 3
# 2
0 3
0 3
1 4
2 4
3 5
4 5
# 3
0 3
1 4
2 5
3 6
4 6
6 7
# 4
1 5
2 6
3 7
5 8
7 9
9 10
0 4
1 6
3 8
5 10
7 11
10 12
13 14
0 5
3 8
6 10
8 13
11 15
14 17
18 19
1 7
4 10
8 13
12 17
17 20
21 23
25 26
2 9
7 14
13 19
19 25
25 29
31 33
37 38
4 12
11 19
19 27
27 34
36 40
45 47
53 54
6 16
17 27
29 39
40 49
53 58
65 68
77 78
E First
Second
Third
Fourth
Fifth
Sixth
Seventh
3
3
3
3
3
3
3
3
6
9
12
15
18
21
# 2
# 2
0 2
0 3
1 3
1 3
2 3
# 2
0 3
0 3
1 4
2 4
3 5
4 5
# 3
0 3
1 4
2 5
3 6
4 6
6 7
# 4
1 5
2 6
3 7
5 8
7 9
9 10
0 4
1 6
3 8
5 10
7 11
10 12
13 14
0 5
3 8
6 10
8 13
11 15
14 17
18 19
1 7
4 10
8 13
12 17
17 20
21 23
25 26
2 9
7 14
13 19
19 25
25 29
31 33
37 38
4 12
11 19
19 27
27 34
36 40
45 47
53 54
6 16
17 27
29 39
40 49
53 58
65 68
77 78
F First
Second
Third
Fourth
Fifth
Sixth
Seventh
5
5
5
5
5
5
5
5
10
15
20
25
30
35
# 2
# 2
0 2
0 3
1 3
1 3
2 3
# 2
0 3
0 3
1 4
2 4
3 5
4 5
# 3
0 3
1 4
2 5
3 6
4 6
6 7
# 4
1 5
2 6
3 7
5 8
7 9
9 10
0 4
1 6
3 8
5 10
7 11
10 12
13 14
0 5
3 8
6 10
8 13
11 15
14 17
18 19
1 7
4 10
8 13
12 17
17 20
21 23
25 26
2 9
7 14
13 19
19 25
25 29
31 33
37 38
G First
Second
Third
Fourth
Fifth
Sixth
Seventh
8
8
8
8
8
8
8
8
16
24
32
40
48
56
# 2
# 2
0 2
0 3
1 3
1 3
2 3
# 2
0 3
0 3
1 4
2 4
3 5
4 5
# 3
0 3
1 4
2 5
3 6
4 6
6 7
# 4
1 5
2 6
3 7
5 8
7 9
9 10
0 4
1 6
3 8
5 10
7 11
10 12
13 14
0 5
3 8
6 10
8 13
11 15
14 17
18 19
1 7
4 10
8 13
12 17
17 20
21 23
25 26
2 9
7 14
13 19
19 25
25 29
31 33
37 38
H First
Second
Third
Fourth
Fifth
Sixth
Seventh
13
13
13
13
13
13
13
13
26
39
52
65
78
91
# 2
# 2
0 2
0 3
1 3
1 3
2 3
# 2
0 3
0 3
1 4
2 4
3 5
4 5
# 3
0 3
1 4
2 5
3 6
4 6
6 7
# 4
1 5
2 6
3 7
5 8
7 9
9 10
0 4
1 6
3 8
5 10
7 11
10 12
13 14
0 5
3 8
6 10
8 13
11 15
14 17
18 19
1 7
4 10
8 13
12 17
17 20
21 23
25 26
2 9
7 14
13 19
19 25
25 29
31 33
37 38
J First
Second
Third
Fourth
Fifth
Sixth
Seventh
20
20
20
20
20
20
20
20
40
60
80
100
120
140
# 2
# 2
0 2
0 3
1 3
1 3
2 3
# 2
0 3
0 3
1 4
2 4
3 5
4 5
# 3
0 3
1 4
2 5
3 6
4 6
6 7
# 4
1 5
2 6
3 7
5 8
7 9
9 10
0 4
1 6
3 8
5 10
7 11
10 12
13 14
0 5
3 8
6 10
8 13
11 15
14 17
18 19
1 7
4 10
8 13
12 17
17 20
21 23
25 26
2 9
7 14
13 19
19 25
25 29
31 33
37 38
= Use first sampling plan below arrow (refer to continuation of table on following page, when necessary). If sample size equals or exceeds lot or batch size, do 100 percent inspection. = Use first sampling plan above arrow.
= Use corresponding single sampling plan (or alternatively, use double sampling plan below, where available). Ac = Acceptance number. Re = Rejection number.
++ = Use corresponding double sampling plan (or alternatively, use multiple sampling plan below, where available). # = Acceptance not permitted at this sample size.