Industrial Engineering
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Course Title Rating Hours Price

182-Feedback Control System Fundamentals

(4.2) 9 Reviews

4 $90.00

Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

This course discusses many fundamental concepts associated with classical feedback control theory. Feedback control measures the state of a physical system or device with a sensing system. The measured state is fed-back and compared to a desired state and the error used by a controller to reduce the difference between the actual and desired states. An example of a feedback control system is the central heating and air conditioning system for a home, or building. A thermostat or temperature sensor is the feedback sensor that measures the room temperature and compares it to the desired temperature or set point, calculating a difference or error. If the temperature is less than the set point, the error is used by the controller to force more heat into the room. When the set point is reached, the error is zero or below an error threshold and the controller will stop heating the room. Another example is the speed control in most of today's automobiles. The speed of the vehicle is measured and compared to a desired speed. Based on the difference between actual speed and the set point, acceleration or braking is applied to the automobile drive to null the error and maintain the desired speed.

Classical control deals directly with the differential equations that describe the dynamics of a plant or process. These equations are transformed into frequency dependent transfer functions. The transfer function is the ratio of two frequency dependent polynomials whose roots describe the response of the plant in a frequency domain. The controller or compensator shapes the closed feedback loop response, given the plant response, to achieve the control performance objectives. Classical feedback control design and analysis tends to require a good foundation in mathematics, however the purpose of this course is not to dwell on the math, although examples are provided, but to provide the basic design and analysis concepts.

The topics covered begin with a description of the basic block diagram in section 2. The relationships between time and frequency domain representations of the block diagram elements are discussed in section 3 followed by the key feedback relationships derived from the block diagram algebra in section 4. Control loop stability and methods to determine stability margins are described in section 5 followed by a discussion of specifying control loop performance in section 6. A couple of control loop design methods are provided in section 7. The basic theory is then applied to two examples; a home heating system in section 8 and motion control applications in section 9. Converting to a digital sample data controller is discussed in section 10; as related to the motion control example in section 9.

291-Proportional, Integral, and Derivative Controller Design Part 1

(4.4) 5 Reviews

4 $90.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

In this course, the design and application of Proportional plus Integral plus Derivative (PID) controller�s is discussed. Some familiarity with feedback control may help in providing a better understanding of the course material. PID control is a technique used extensively in feedback control systems. Its origins date back to the 19th century, being used for governor speed control, and since then in numerous applications with a wide variety of actuators and sensors. The controller is simple structure; being the sum of three terms as the name implies. The PID structure provides for a fairly wide range of tuning adjustment in a feedback control loop, especially for relatively simple processes. A PID uses the error, it�s integral and derivative to derive a control signal driving the error to a null state. The controller can be structured in many configurations; P-only, PI, PD, PID, plus others to be discussed. PID control is central to most process control systems; but can also be found in numerous applications other than process control ranging from positioning control loops to pointing, tracking and platform stabilization control loops. The PID can also be integrated with higher level control strategies such as model predictive control, adaptive controllers and fuzzy logic control described in Part 2 of the course.

Starting with an introduction in section 1.0, topics covered are a description of the basic feedback control loop block diagram in section 2 and how the PID relates to the control loop. The relationships between time and frequency domain representations of the block diagram elements are discussed in section 3 followed by the key feedback relationships derived from the block diagram algebra in section 4. The PID control algorithm is described in section 5 which includes the frequency domain characterization of the PID (5.1), the effect of each PID term has on response (5.2) and finally different forms of the PID used in actual applications. In section 6 a discussion of specifying control loop performance is presented. PID control loop design methods are provided in section 7. The basic theory is applied to an example; a home heating system, in section 8.

292-Proportional, Integral, and Derivative Controller Design Part 2

(4.5) 4 Reviews

4 $90.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

As discussed in Part 1, PID controllers are used in many control applications; possibly the most common form of feedback control compensation. The versatility of the PID may reside is a fairly simple control structure, easy to implement in software or hardware, offering loop gain adjustment, an integrator to reduce or null servo error, and the phase lead of a derivative improve loop stability or act as a predictive element. This PID structure provides for a fairly wide range of tuning adjustment in a feedback control loop, especially for relatively simple processes. The controller can be configured in many configurations; P-only, PI, PD, PID, plus others are discussed. This part of the course focusses on the digital implementation of the PID controller and its implementation with higher level control strategies; adaptive controllers and fuzzy logic control.

Starting with an introduction in section 1.0, topics covered are a summary of the basic feedback control loop block diagram relationships in section 2. The PID control algorithm, as presented in Part 1, is summarized in section 3 followed by the digital implementation of the PID within the constraints of a sampled control system. The building temperature control example, used in Part 1, is analyzed again in section 4.0 but now using a digital PI controller. Section 5.0 provides another example for a motion control application using a digital PD controller. Finally section 6.0 describes implementation of the PID within a model reference adaptive control (MRAC) architecture and also configured with a fuzzy logic controller (FLC).

183-Quality Project Management

(4.6) 73 Reviews

4 $90.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

This course is about improving the quality for managing project work in an organization whether you are an engineer, senior manager or professional project manager. In the ideal project world, project managers are well-trained professionals and assigned to a project at the beginning of the project. In the real project world, many projects are small and assigned to engineers and managers with less than professional project management training at any time in the life of the project.

For the engineer, senior manager or professional project manager the quality for managing project work should improve significantly by combining seven key tools with a basic feature from statistical process control, the control chart. The seven key tools, called The Seven Icons©, are presented in this course and will demonstrate how they can be used to improve planning and controlling project work.

The Seven Icons© are organized and connected in a structure that is easy to remember. The icon terms serve as a common language between managers, team members, and their bosses. This feature becomes most important when considering that practically everyone in an organization is involved in some kind of project work. Having an effective way of remembering and applying key tools to project work will improve communications throughout the organization and ultimately improve the quality for managing project work.

At the end of the course is a set of questions that highlights the take-aways for the reader to remember and use for improving the quality for managing projects in their organization.

218-Managing Project Risk

(4.6) 23 Reviews

3 $67.50

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

This course is about managing project risk in an organization whether you are an engineer, senior manager or professional project manager. In the project world, managing risk is critical because every decision, every action taken contains some element of risk. Risk cannot be eliminated. Risk can only be controlled and accepted if the decision or action needs to be made. Understanding this concept becomes important when considering that practically everyone in an organization is involved in some kind of project work and makes decisions involving risk.

In the ideal project world, project managers are trained in project risk management. ¹In the real project world, many projects are small and assigned to engineers and managers with less than formal risk management training. Managing risks become critical to achieving project cost and schedule targets. This course presents three basic principles for managing project risk, namely, identify, quantify, and control. However, managing project risk still depends on experience and skill of the engineer or manager to identify, quantify and control the risk in order to manage it.

At the end of the course is a set of questions that highlights the take-aways for the reader to remember and use for managing project risk in their organization.

¹Project Management Institute, PMBOK — GUIDE Fifth Edition 2013 Project Risk Management, Chapter 11

356-Industrial and Systems Engineering - The Fundamentals

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4 $90.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

This course presents principles and practices of Industrial and Systems Engineering (IISE) . The focus of IISE is Operations, namely; Operations Analysis and Design, Operations Control, and Continuous Improvement. IISE practices use science, mathematics, and engineering methods to analyze, design, and improve complex systems and operations. And, because these systems are so large and complex, IISE principles involve knowledge and skills in a wide variety of disciplines; require a broad systems perspective and the ability to work well with people.

A course about Industrial Engineering would not be complete without a brief description of why and how the profession began. The origins of Industrial Engineering began in the early 1900 as part of the scientific management movement. The definition of Industrial Engineering is:

Industrial Engineering is concerned with the design, improvement, and installation of integrated systems of people, material, information, equipment, and energy. It draws upon specialized knowledge and skills in the mathematical, physical and social sciences together with the principles and methods of engineering analysis and design to specify, predict and evaluate the results to be obtained from such systems.

Accordingly, Industrial Engineering emerged as the foundation for connecting engineering methods and economics to set quality and cost standards for delivering goods and services in business and industry. Industrial Engineers apply their knowledge and skills to set operations process standards through the use of planning, design, statistical analysis, methods engineering, interpersonal communications, quality control, computer simulation, and problem solving. At the end of the course is a set of questions that highlights the take-aways for the reader to remember and use for solving operations and systems problems in their organization.

267-Biomass Process Flow Calculations

(4.5) 2 Reviews

1 $22.50

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

Process flow calculations are an essential part of any biomass project. They provide an aid in firming-up the basic process, sizing equipment and estimating the project. The calculations however are complicated by the fact the certain variables such as daily operating hours, bulk density and moisture content vary as the material progresses through stages of the process. This course presents a methodical approach that can render the calculations relatively simple and minimize opportunities for errors in complex projects.

On completing this course, the student should be able to:

Understand the difference between block flow diagrams and process flow diagrams.
Understand the basic methodology for performing process flow calculations.
Understand the need for storage volume calculations as part of the process flow calculation procedure.
Understand how to predict fuel requirement to a dryer.
Understand how to calculate annual uptime rates.
Understand the difference between dry basis and wet basis moisture contents and how to convert between them.
Understand the advantage of working with "oven-dry" bulk density.
Understand the considerations involved in selecting design factors.

275-What Every Engineer Should Know About Engineering Probability and Statistics I

(3.9) 17 Reviews

5 List: $112.50
Sale: $35.95

Special Price Offer: expires at midnight on 9/25/2020.

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

The concept of probabilistic design is quite pervasive across the engineering disciplines because of its implication on engineering design decisions. Typically, assumptions and simplification of engineering and other related natural processes are often idealistic and do not consider uncertainties inherent in those processes and phenomenon (be they mechanical, chemical, electrical, biological, etc). There is also the tendency to assume that the situation is either deterministic or qualitative or both. Under certain circumstances such assumptions may suffice. However, in the realm of engineering design, such assumptions and simplifications are not acceptable as uncertainties are unavoidable in almost all engineering analyses and design activities. Thus, any recommendations that are formulated without proper identification and assessment of the inherent risks and uncertainties would not only be invalid but would paint a wrong picture of the situation under consideration.

The purpose of this course therefore is to present the fundamental concepts of probability and statistics from the perspective of engineering practice. As part of the learning objective, the course would demonstrate:

The role of probability and statistics in engineering design decisions, and
The concepts of variability

Additionally, the student will be able to:

Develop an appreciation of the notion of events, the sample space and the real line.
Understand the notion of enumeration and counting techniques that are applicable in probability and statistics analyses.
Explore the meaning of density and mass functions with respect to their relationship to random variables.
Discover some of the common discrete and continuous distributions that are employed in describing engineering problem situations and scenarios

276-What Every Engineer Should Know About Engineering Probability and Statistics II

(4.1) 7 Reviews

5 List: $112.50
Sale: $35.95

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

The concept of probabilistic design is quite pervasive across all engineering disciplines because of its implication on engineering design decisions. Quite often because of the complexity of the processes and the difficulty in explicating the inherent relationships, assumptions about engineering and other related natural processes are simplified and so do not consider uncertainties inherent in those processes and phenomenon. Safety factors and safety margins have often been employed to overcome the need for probabilistic designs. Under certain circumstances such assumptions may suffice. However, in the realm of engineering design, such assumptions and simplifications may not be acceptable as uncertainties are unavoidable in almost all engineering analyses and design activities. Therefore any recommendation developed without proper identification and assessment of the inherent risks and uncertainties would not only be invalid but would paint an unrealistic and unrepresentative picture and thus could jeopardize public safety.

This second course in the series focuses on an important area of engineering analyses and design, namely Statistical Inference. Statistical Inference is about how we analyze data and use the information to make decisions about a given engineering problem. The process of explicating the complexities of the data to yield information that would eventually be used to make design or mission decisions is known as inference or more appropriately Statistical Inference. If we examine the relationship between the population and the sample (as we did in the first course) we note that there is sort of a symbiotic (parent-population, offspring-sample) relationship between the two. Probability deals with the population with its parameters (parent values) while Statistical Inference deals with the sample and its statistic (values computed from the sample and used to estimate the population or universe parameters). The following areas would be covered in the course but not necessarily in the order shown:

The Point Estimates for the Mean and the variance.
The Central Limit Theorem (CLT) and its role in estimating parameters of a population.
Sampling distributions for means and variances with variance both known and unknown
Sampling distribution for two means & two variances with variance known and unknown
Point Estimator, Interval Estimators and Tests of Hypothesis
Error of estimation and the effect on sample size (n).
Type I and Type II errors and the effect on ample size n
Confidence Intervals for one means and one variances
Confidence Intervals for two means and two variances

Due to the nature of the materials, a significant number of numerical examples have been included to provide better insight into the materials presented. At the end, the engineer should feel well equipped to explore the important area of Statistical Inference and what it offers with regards to Engineering design decisions.

283-What Every Engineer Should Know About the Design and Analysis of Engineering Experiments I

(3.6) 7 Reviews

5 List: $112.50
Sale: $35.95

Special Price Offer: expires at midnight on 9/25/2020.

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

Design of experiment is an activity that every Engineer should take very seriously. Engineers are called upon every day to make decisions regarding programs, processes and systems that have significant implications on the safety and well-being of society, be they chemical processes, the environment, infrastructure, machinery and equipment, and others. And while Engineers are known for sound and fact based judgment, those laudable qualities and characteristics may not be enough and may not serve them well in certain circumstances. This is especially true when they are called upon to make decisions regarding variables and factors whose underlying distributions are stochastic and thus have uncertain, albeit questionable, predictability. Handling these situations requires an understanding of the formal schemes and structures necessary to deal with variability, bias, and randomness.

This is the first of a two-course sequence in this subject area. As the prerequisite to the second course, it provides the Engineer with the rudimentary, but necessary, toolkit needed to plan, design and analyze basic engineering experiments and to make recommendations about design and operational decisions. It sets the stage for the second course, where more robust and higher level designs are explored, including Factorial designs, Fractional designs, Nested designs, Confounding schemes and Regression Analysis. The second course also addresses a fundamental problem of design, namely cost and resource utilization, and also the all important issue of missing values. While the two courses are not strictly about mathematics and statistics, they do utilize those subject matters to further elucidate how to plan, design, and analyze engineering experiments. Some of the areas covered in this course include:

The Role of Experiments in the Engineering Design Process
The Role of Statistics and Probability in Engineering Design
Purpose and Nature of Planned Experiments
Important Issues in Planned Experiments
The Effects of Changes in the Independent Variables
The Effect of Noise in An Experiment
Restrictions on Randomization
Single Factor Experiments including Model Analysis
Randomized Block Designs
Latin and Other Designs
Incomplete Block Designs

285-What Every Engineer Should Know About the Design and Analysis of Engineering Experiments II

(2.5) 2 Reviews

5 List: $112.50
Sale: $35.95

Special Price Offer: expires at midnight on 9/25/2020.

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

Design of experiment is an activity that every Engineer should take very seriously. Engineers are called upon everyday to make decisions regarding programs, processes and systems that have significant implications on the safety and well being of society, be they chemical processes, the environment, infrastructure, machinery and equipment, and others. Engineers are known for sound and fact based judgment but while those qualities and characteristics are laudable, they may not be enough and may not serve them well. This is especially true when they are called upon to make decisions regarding variables and factors whose underlying distributions are stochastic and thus have uncertain and questionable predictability. Handling these situations requires an understanding of the formal schemes and structures necessary to deal with variability, bias, and randomness.

This second course, in the two-course sequence, focuses on some of the more practical issues that engineers encounter during the design and analysis of experiments. This course focuses on more robust and higher level designs such as Factorial designs, Confounding Schemes Fractional designs, Fixed and Random factors, Expected Mean Squares, Nested or Hierarchical designs, and Regression Analysis. The course also addresses, with realistic examples, some of the common problem in design of experiments, namely, missing data or missing values. It also provides practical justification for confounding, which arises due to the physical limitation as it relates to acquiring all the needed data. It addresses the issue of cost and resource utilization where fractional factorial designs are used because the cost to run full higher order designs is prohibitive. The course has a very practical bent and while there are theoretical foundations undergirding the material, the course itself utilizes basic arithmetic for computation and analysis. Some of the areas covered in the course include:

The Role of Experiments in the Engineering Design Process
Missing Values for Randomized Block and Latin Designs
Factorial Designs for 2^f and 3^f
Confounding Schemes for 2^f and 3^f
Fractional Factorial Designs for 2^f and 3^f
Modeling of Fixed and Random Effects and Expected Mean Square (EMS)
Nested/Hierarchical Designs
Regression Analysis

290-What Every Engineer Should Know About Reliability I

(2.2) 4 Reviews

4 $90.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

Reliability Engineering is concerned with the design, implementation, and prediction of the life profiles of a system or component using a disciplined analysis approach that has strong roots in statistics, mathematics and engineering. Given a system, subsystem or component, one of the major challenges of the discipline is to understand the inherent failure mechanisms that govern the system and the development of the appropriate analytical scheme to determine the system's life profiles. The problem becomes even more acute given the phenomenon of aging and related transient phenomenon as well as the practical realities of little or no data. Today, these challenges still persist especially as companies try to shorten the time to market in order to gain market share.

This first in a two-course sequence has examined some of the basic issues related to reliability such as:

Understand the various viewpoints of reliability, especially the engineering design viewpoint.
The use of nonparametric approach to estimate the reliability and hazard function functions
Understand the performance measures used to characterize reliability.
Appropriate reliability based intervention strategies that lead to optimally maintained system.
Availability, Maintainability and related Performability measures.

Under these broad themes, the topics to be covered include:

Reliability Models
Static Reliability
Reliability Improvement
Reparable Systems-Availability Models
System Redesign
Maintenance

The second sequence will focus on the all important area of dependency analysis, interference theory, data analysis and testing.

293-What Every Engineer Should Know About Reliability II

(3) 1 Review

4 $90.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

Reliability Engineering is concerned with the design, implementation, and prediction of the life profiles of a system or component�using a disciplined analysis approach that has strong roots in statistics, mathematics and engineering. Given a system, subsystem or component, one of the major challenges of the discipline is to understand the inherent failure mechanisms that govern the system and the development of the appropriate analytical scheme to determine the system's life profiles. The problem becomes even more acute given the phenomenon of aging and related transient phenomenon, as well as the practical realities of little or no data. Today, these challenges still persist especially as companies try to shorten the time to market in order to gain market share.

This second in a two-course sequence has examined some more practical issues related testing and parameter estimation as well as some topology or configurations that are practical and realistic but have not received enough attention.

Some of the issues addressed include:

Understand the various viewpoints of reliability, especially the engineering design viewpoint.
The use of nonparametric approach to estimate the reliability and hazard function functions
Understand the performance measures used to characterize reliability.
Appropriate reliability based intervention strategies that lead to optimally maintained system.
Availability, Maintainability and related Performability measures.

Under these broad themes, the topics to be covered include:

Reliability Models
Static Reliability
Reliability Improvement
Reparable Systems-Availability Models
System Redesign
Maintenance

The second sequence will focus on the all important area of dependency analysis, interference theory, data analysis and testing.

297-What Every Engineer Should Know About Statistical Process/Quality Control I

(4.8) 4 Reviews

5 $112.50

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

The American National Standards Institute (ANSI) defines Quality Assurance (QA) as "All of those planned or systematic actions necessary to provide adequate confidence that an item will perform satisfactorily in service". A more operational definition of quality is the one that defines as: "Fitness for Use" This points to the inescapable fact that it is the customer rather the producer or manufacturer that determines what quality is or should be.

There is a tendency to think of quality as a recent development or phenomenon. However, the basic idea of making a quality product with high degree of uniformity has been around for as long as man has made a product the idea that statistics may be instrumental in assuring the quality of manufactured products goes as far back as the advent of modern production. The widespread use of statistical methods in problems of quality control is even more recent. Many problems encountered in the manufacturing or of product and services and the associated supply chains exhibit process characteristics and as such are amenable to statistical treatment or analysis. Statistical Process/Quality control refer to three special techniques:

Process/Qualitycontrol,
Acceptance control,
Parameter design and the establishment of tolerances.

The course places emphases on the significance of process control rather than inspection as a means of reducing rework and nonconformance. Many experts agree that inspection (especially human inspection) does not add value to quality and thus is a necessary but non-value adding activity.

This first in a two-course sequence will focus on Process/Quality control with emphasis on:

Historical review of Statistical Process/Quality Control
Cost of Quality (Cost of Poor Quality)
Quality Auditing Process
The difference between Quality of Design and Quality of conformance
Differences and similarities between SQC and SPC
Total Quality Management (TQM)
The Three Gurus of TQM
Lean Six Sigma
Off-line Control and On-Line Control
Shewhart Control Charts--Interpreting Shewhart Control Charts
Process Capability Evaluation

The second course will focus on acceptance sampling and will explore some of the Military and Commercial Standards that have been developed to aid acceptance control.

345-What Every Engineer Should Know about Statistical Process/Quality Control II

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5 $112.50

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

The focus of this course is to provide an understanding of the principles of Quality Assurance with a focus on Design for Robustness, Quality Loss, Loss Function Computation, and Acceptance control as well as current definitions, terminologies, inherent assumptions that are applicable in industry and as required by the US Government. It also introduces the student to the relevant Military Standards and other Government publications used in the industry. The course also further develops the concepts of system design, parameter design, and tolerance design, as the foundational elements of Robust Product Design. It also goes into a detailed analysis of the use of MIL-STAD-1916 and MIL-HDBK-1916 in establishing attribute-based Acceptance Sampling plans. The course is replete with numerical examples on the computation of the probability of acceptance (Pa) and other important parameters for single, double and multiple sampling plans including; the Average Outgoing Quality (AOQ), the Average Outgoing Quality Limit (AOQL), the Average Total Inspection (ATI), as well as the Average Sampling Number (ASN).

The course is concept based and uses basic arithmetic to develop the fundamental aspects of the techniques. The topics covered include:

Designing for Robustness

System Design, Parameter Design, Tolerance Design, Process Capability (Cp, Cpk) and Process Performance (Pp, Ppk), Process Errors, Quality Loss, and Loss Function

Acceptance Control

Lot Acceptance Sampling Plans (LASP):

Single sampling plans (SSP), Double sampling plans (DSP), Multiple sampling plans (MSP), Sequential Sampling Plans (SSP), Skip Lot Sampling Plans (SLSP), Operating Characteristics Curves (OC Curves), AOQ Curve

MIL- STD-1916 and MIL-HDBK-1916

Requirements and Applicability of MIL-STD-1916 and MIL-SHDBK-1916

Preferred sampling plans
Determination of sampling plan: Verification Level (VL), Code Letter (CL)
Sampling of lots or batches
Disposition of nonconforming product

385-Sustainability Comparisons for All Engineers

(4.7) 3 Reviews

3 $67.50

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

It is increasingly common for engineers in all fields to consider sustainability when designing a product, process, or facility. This course will cover recent trends in sustainability including the “triple bottom line”, life cycle assessment, lifecycle cost, renewable energy, the precautionary principle, and greenhouse gas emissions.

In can be challenging to quantify sustainability and to reduce subjectivity. This course will directly address these challenges and present a ten step framework for calculating and comparing the sustainability of alternatives. Two example comparisons are provided to guide you through the process of quantifying sustainability, comparing the alternatives, and picking a winner.

391-What Every Engineer Should Know About Regression Analyses

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5 $112.50

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

Modern computing technologies and Big data have significantly changed the discourse on data mining and data efficacy. In any system where quantities change, it is of interest to look at the effects if any, of the system variables. Indeed, there may be a relationship (in our case statistical relationship) which may be approximated by a simple mathematical relationship. At other times, the mathematical or functional relationship may be complicated. Still there may be situations where there does not seem to be meaningful relationships between the variables and yet we might want to express or relate those variables by some sort of mathematical equations Regression Analysis is one of the most important statistical techniques used for data mining applications. It is a statistical methodology that helps estimate the strength and direction of the relationship between two or more variables, more specifically regressor and response variables and provides detailed insight that can be applied to further improve system outcomes. The importance of regression analysis lies in its singular focus on data which means numbers and figures that ultimately define a business entity. In Regression Analyses, two types of variables are of major concern, namely the regressor or predictive variables also known as independent variables, and the response variable. The independent or predictor variable is one that is not random but is controlled (sometimes observed such as the amount of rainfall on a plot of land when the interest is on the effect of rainfall on crop yield) during an experiment. The dependent or response variable cannot be controlled but is rather measured as an outcome of the manipulation (or observation in the case of rainfall) of the independent variable and thus is a random variable. In this course, we will focus primarily on the following elements of Regression Analyses, namely: • Parameters & Estimates • Probability Distribution of the Parameters • Covariance between two variables • Simple hypothesis tests involving parameters including one- and two-sided t and F tests • Confidence Interval for the parameters • Orthogonal Columns, Diagonal and Symmetric Matrices • Estimation of model R2, Adjusted R2, (𝜌 or r) to assess data efficacy • Coefficient of Variation (CV) • Multicollinearity and Variance Inflation Factors

Industrial Engineering3 Easy Steps to Complete Your Course

Designing for Robustness

Acceptance Control

MIL- STD-1916 and MIL-HDBK-1916

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