Tradeoffs, Constraints, and Design Space in Engineering

Nobody Gets Everything They Want

A junior engineer presents a bracket design. It's strong enough. Fits the space. Costs $8 to manufacture. The project manager says "great, but can you make it lighter?" Sure—the engineer removes material, drops weight by 30%. Then manufacturing reviews it: "These thin sections are hard to machine reliably, and yield will be terrible." So the engineer thickens it back up. Now the weight is back where it started, but manufacturing is happy. Then purchasing sees the material cost went up. "Can we use a cheaper alloy?" Yes, but now it needs to be thicker to maintain strength. Weight goes up again. Cost is down, weight is up, manufacturability is worse.

Welcome to engineering: you can't win, you can only choose which battles to fight. Make it stronger, it gets heavier or more expensive. Make it lighter, it gets weaker or harder to manufacture. Make it cheaper, reliability drops or lead times increase. Every single decision you make improves one thing at the expense of something else. The skill isn't finding the perfect solution—it's figuring out which compromises matter least for your specific application.

Students search for "the right answer." Engineers search for "the answer that best balances the things that actually matter here." Titanium is objectively better than steel on a strength-to-weight basis. But if you're designing a stationary industrial machine that never moves, who cares about weight? Steel costs 1/10th as much. Use steel. Context determines what "better" means.

The Map of What's Possible

Think of the design space as a map. Some areas are off-limits entirely—those are your hard constraints. The part must fit in this envelope. It must carry this load without yielding. It must cost less than this budget. Cross any of those lines and your design is dead on arrival, doesn't matter how clever it is.

Inside those boundaries, you're navigating preferences: lighter is better than heavier, cheaper is better than expensive, easier to manufacture is better than complicated. But "better" isn't absolute. Sometimes you accept heavier to get cheaper. Sometimes you accept more expensive to get easier manufacturing. You're trading one advantage for another based on what matters most for this specific situation.

The design space shows you what's achievable. Within that space, there's no single "best" answer—there are multiple good answers depending on what you value most. Your job is to pick the answer that makes the most sense for the constraints and priorities you're actually working with.

When Everyone Wants Something Different

Here's what actually happens on projects: you design a part. Manufacturing looks at it and says "these internal corners are impossible to machine, we need larger radii." You increase the radii. Now the stress analyst says "those larger radii create stress concentrations we didn't account for, you need more material there." You thicken the part. Procurement sees the weight went up: "that's 200 grams more aluminum per unit, times 50,000 units per year, you just added $30,000 to our material cost." You switch to a cheaper alloy. Now reliability says "that alloy has lower fatigue strength, we need a bigger safety factor." Which means more material. Which brings cost back up. You're going in circles.

Nobody's wrong. Manufacturing legitimately can't machine tight internal corners without custom tooling. The stress analyst is right that geometry changes affect stress distribution. Procurement has real cost targets. Reliability has to guarantee the product lasts. They all have valid requirements that conflict with each other. Your job isn't to make everyone happy—it's to find the design that violates the fewest important requirements.

The trick: figure out which requirements are actually non-negotiable versus which ones are preferences someone stated as requirements. "Must be under 500 grams" might actually mean "we'd prefer lighter because shipping costs matter." Okay, how much do shipping costs matter? If saving 50 grams reduces shipping costs by $0.30 but increases manufacturing cost by $2.00, maybe weight isn't the constraint you thought it was.

Constraints Are Your Friends (Sort Of)

Students see constraints as obstacles. "I could design something amazing if I didn't have to worry about cost, or manufacturing, or the space envelope, or..." Stop. Constraints aren't what's preventing you from designing—they're what's making design possible. Without constraints, there are infinite solutions and no way to choose between them. With constraints, you can actually make progress.

Think about it: "Design the best bracket possible" is an impossible problem. Best for what? Lightest? Strongest? Cheapest? Most reliable? Easiest to install? You can't optimize all of them simultaneously. But "Design a bracket that fits in 200 × 100 mm, carries 5000 N, costs under $50, and can be machined in our facility" is solvable. The constraints turned an impossible optimization problem into an actual design task.

The trick is distinguishing between real constraints and assumptions disguised as constraints. "Must be stainless steel" might actually be "we're worried about corrosion." Okay, how much corrosion are we talking about? Indoor use with occasional water exposure? That might be fine with coated carbon steel at 1/3 the cost. Outdoor marine environment with salt spray? Yeah, you probably need stainless. Challenge every "must" by asking what problem it's actually solving.

Here's what constraints you're actually navigating:

Physics: Material properties don't negotiate. Steel yields at its yield strength whether you like it or not. Deflection follows the equations. You can't violate the laws of thermodynamics no matter how clever your design is.

Manufacturing: What equipment exists? What tolerances are achievable? Can your supplier actually make this geometry, or are you designing something that only works in CAD? Sometimes the "perfect" design can't be built at scale.

Money: Budget is real. Saying "we could use titanium instead of steel" is useless if titanium blows the cost target. Return on investment matters. Development cost matters. Tooling cost matters. Sometimes "good enough and cheap" beats "technically superior and expensive."

Time: The deadline isn't arbitrary—it's when the customer needs delivery, or when the trade show happens, or when your company runs out of cash. A perfect design that ships six months late is worse than a good design that ships on time.

Regulations: Safety codes exist for reasons written in blood. OSHA requirements, pressure vessel codes, electrical standards—you don't get to ignore these even if they're inconvenient. Compliance isn't optional.

Work within the constraints. Don't waste energy fighting the ones that are actually fixed. Focus your cleverness on the design space that exists, not the one you wish existed.

When to Stop Optimizing and Ship It

At some point you have to stop analyzing, pick a design, and move forward. The hardest skill in engineering isn't running better simulations or calculating tighter tolerances—it's knowing when good enough is actually good enough. Over-optimization is a real trap. You can spend three weeks shaving 5% off the weight when nobody cares about weight and you're delaying delivery for something that doesn't matter.

Before you start optimizing, ask: what actually matters here? If you're designing a stationary machine that never moves, optimizing for minimum weight is performance theater—you're doing it to feel like you're engineering, not because it creates value. If you're designing a satellite payload, every gram matters enormously and weight optimization is absolutely worth the effort. Context determines which variables deserve your optimization time.

Here's how to actually make design decisions without getting stuck in analysis paralysis:

First, define what "success" looks like before you start designing. Write it down. "Part must carry 5000 N with FOS ≥ 2, cost under $50, manufacturable in our facility." Now you have clear success criteria. You'll know when you're done—when those boxes are checked.

Second, identify the 2-3 variables that actually matter. Don't try to optimize 15 parameters. Most designs are dominated by a few key dimensions. If thickness drives strength and strength is the constraint, focus on thickness. If manufacturing cost is the constraint, focus on geometry simplification. Optimize the things that matter. Ignore the things that don't.

Third, document why you picked this design so future-you (or whoever inherits this project) understands the reasoning. "Chose steel over aluminum because stiffness drove the design, and aluminum would require 2× diameter to match deflection limits, killing the weight advantage while increasing cost." Now when someone questions your choice, you have an answer.

Fourth, validate critical assumptions early. If your whole design assumes "customer won't see loads above 5000 N," verify that before you finish detailed design. Don't build everything on assumptions you never checked. Quick tests or prototype runs can catch bad assumptions before they're expensive to fix.

The goal isn't perfection. The goal is a design that works, meets requirements, and ships on time at acceptable cost. Good enough, delivered on schedule, is infinitely better than perfect but never finished.