Engineering Design Process Overview

Engineering Design Process

• A series of steps engineers follow to create solutions to problems.
• The process is iterative, involving multiple redesigns.
• It's a decision-making process that involves science, math, and engineering.
• The goal is to convert resources into solutions based on identified opportunities, analysis, synthesis, and evaluations of various solutions.
• Designing a system, component, or process, meets specifications and constraints.

Defining the Problem

• Understanding the problem, who needs a solution, and why it's important.
• Determining why the issue needs to be addressed.
• Identifying users or customers, existing solutions, and necessary requirements.

Requirements

• Defining the characteristics that the solution must possess for success.
• Determining important characteristics the solution must meet.
• Listing design requirements.
• Brainstorming and evaluating various potential solutions.

Building a Prototype

• Creating a functional model of the solution.
• Building an operating version of a solution.

Testing and Redesign

• Testing the design and finding weaknesses.
• Conducting iterative testing and redesign, making improvements based on findings.
• Iterations and refinements to the design through repeated testing.

Communicating Results

• Effectively communicating final results through reports and presentations.
• Reporting and showcasing project results to others.

Constraints

• Conditions that must be met; for example, external factors such as schedules or limitations of technology.
• Constraints can be in the form of codes, standards, or technical regulations.

Interoperability

• A product or system’s ability to work with other products and systems.
• Products or systems that work together without limitations.

Standards

• Providing a framework for technical requirements.
• Standards help avoid incompatibilities in programs or projects, reducing implementation timeframes.

Codes

• Laws that define minimum safety and health standards (e.g., building codes).
• Codes specify requirements for product development.

Technical Regulations

• Government-defined product characteristics and production methods.
• Specifications with mandatory compliance.

Organizations

• Defining standards, codes, and regulations (e.g., ASTM, IEEE, ASME).
• Organizations providing guidelines and definitions for standards, codes, and regulatory frameworks.

Design Goals

• Defining the characteristics of a computer system (e.g., features, weight, reliability, expandability, and power consumption).

Cost

• Keeping costs consistent with commercial or device criteria.

Performance

• Evaluating overall device functionality.

Power Consumption

• Optimizing power efficiency while achieving results.

Hardware

• Microprocessors or micro-controllers are used in embedded systems.

Research

• Including literature and studies related to the current research.
• Critically analyzing literature to identify shortcomings and justify the need for the research.
• Gathering information from various (primary and secondary) sources.
• Using different approaches to draw conclusions – including analytical reading and critical evaluation.
• Understanding and applying literature review approaches systematically.

Quantitative Approaches

• Descriptive research: understanding.
• Correlational/comparative research: understanding relationships.
• Experimental research: testing.
• Developmental research: understanding change.

Statistical Treatment

• Considerations for using statistical treatments for testing, like independent observations, homogeneity of variance, and normality (Bell curve) of data.
• Treatments for comparing groups or assessing relationships among variables are defined, along with the considerations in those tests.

Types of Tests

• Parametric tests(e.g., t-test, ANOVA, MANOVA): assumptions about data distribution (normally distributed).
• Non-parametric tests (e.g., Chi-square test, etc.): no assumptions about data distribution are required.
• Correlation tests (e.g., Pearson’s r): identify correlations between variables.
• Regression tests (e.g., simple and multiple linear regression): identify cause-and-effect relationships.