L1 · PAI-150

Choosing a material and a process

For the sensor-mount part and a stated requirement (strength, cost, count), select a material+process pairing and justify it against at least two alternatives, using the twin's estimated cost, time, and feasibility.

01
Challenge

Try this first — before any explanation.

Pick a material and a process that meet all four requirement lines for 5,000 brackets (count 5,000; survive 40 N; ≤ $3.50/part; first parts within 3 weeks). You haven't been taught how the families differ — try a pairing, estimate, read what the twin says, and iterate to a combination marked feasible and under cost at this volume.

→
The Bench

The twin's cost/time/feasibility model is a deterministic function of (material, process, count, geometry); here it is in numpy. Edit your chosen pairing and run.

= REQ['load_N'] and m['temp_ok']\n return dict(unit=round(unit_cost,2), days=round(days,1),\n feasible=feasible, strength_ok=strength_ok, notes=notes)\n\nfor pair in [('Al6061','cnc'), ('PLA','fdm'), ('ABS','molding')]:\n print(pair, estimate(*pair))","label":"2 — Deterministic estimator"},{"code":"# EDIT: your chosen pairing, and >= 2 alternatives each with the axis it LOSES on.\nchoice = ('ABS', 'molding')\njustify = [ # (material, process, losing_axis in {'cost','strength','count','lead'})\n ('Al6061', 'cnc', 'cost'),\n ('PLA', 'fdm', 'strength'),\n]\ne = estimate(*choice)\nprint('chosen', choice, '->', e)","label":"3 — Your choice + justification"},{"code":"def meets(material, process):\n e = estimate(material, process)\n weeks = e['days']/5.0\n return (e['feasible'] and e['strength_ok'] and\n e['unit'] <= REQ['cost_target'] and weeks <= REQ['lead_weeks']), e, weeks\n\nok, e, weeks = meets(*choice)\nproblems = []\nif not ok:\n if not e['strength_ok']: problems.append(f'{choice[0]} fails strength/temp for the 40 N + outdoor requirement.')\n if e['unit'] > REQ['cost_target']: problems.append(f'unit ${e[\"unit\"]} > ${REQ[\"cost_target\"]} target at 5,000 count.')\n if weeks > REQ['lead_weeks']: problems.append(f'lead {weeks:.1f} wk > {REQ[\"lead_weeks\"]} wk.')\n# validate justification: >=2 alternatives, each truly losing on its named axis\naxis_val = {'cost': lambda x: x['unit'], 'strength': lambda x: 0 if x['strength_ok'] else 1}\njok = len(justify) >= 2\nfor mat, proc, axis in justify:\n a = estimate(mat, proc)\n if axis == 'cost' and not (a['unit'] > e['unit']): jok = False; problems.append(f'{mat}+{proc} does not actually lose on cost.')\n if axis == 'strength' and a['strength_ok']: jok = False; problems.append(f'{mat}+{proc} does not actually lose on strength.')\n\nif ok and jok and not problems:\n print(f'PASS - {choice[0]}+{choice[1]} meets all four lines (unit ${e[\"unit\"]}, strength OK, '\n f'lead {weeks:.1f} wk). You beat 2 alternatives on their losing axes. A defended pairing.')\nelse:\n print('FAIL - ' + ' '.join(problems[:2] if problems else ['Need >= 2 alternatives, each with a real losing axis.']))","label":"4 — Autograder (seed 1102)"}],"intro":"The twin's cost/time/feasibility model is a deterministic function of (material, process, count, geometry); here it is in numpy. Edit your chosen pairing and run.","key":"manufacturing/choosing-material-and-process","kind":"python","title":"Choosing a material and a process"}">
PYTHON · NUMPY · IN-BROWSER

Choosing a material and a process

The twin's cost/time/feasibility model is a deterministic function of (material, process, count, geometry); here it is in numpy. Edit your chosen pairing and run.

02
Model

The idea, built visually.

There are really only three ways to make a solid thing: cut it out of a block (subtractive), pour liquid into a mold and let it form (molding/casting), or grow it layer by layer (additive). The trick isn't geometry first — it's count. Cutting and growing barely care about volume; each part costs about the same at one or a thousand. Molding starts brutal — you pay for a steel mold up front — then every part is almost free.

Somewhere there's a break-even; for 5,000 brackets you're well past it. Count picks the neighborhood; then geometry and material pick the house. So you don't pick a process — you pick a pairing, and justify it by beating the alternatives on the requirement.

▣ Stage animation: Cost-per-part vs part-count: CNC and 3D-print curves stay flat-high while the injection-molding curve starts at a 'tooling $$$' spike then dives below them past a pulsing break-even line.

03
Guided practice

Build it up, step by step.

  1. Pass A (worked): Aluminium 6061 + CNC @ 5000 → ~$11.40/part, feasible but far over $3.50; CNC cost barely drops with volume → wrong family.
  2. Pass B (hint): for 5,000 you want the FORMED family — a moldable thermoplastic + injection molding. Check strength (40 N), temp, and unit cost < $3.50.
  3. Pass C (independent): lock a pairing AND fill the justification matrix — name ≥2 alternatives and the requirement axis each loses on.
04
Feedback

How the Bench grades your run.

PASS WHEN Chosen pairing is feasible and meets all four requirements (strength, cost, count, lead), and ≥2 alternatives are each beaten on a losing axis that matches the twin's estimate, on seed 1102.

  • Aluminium + CNC is feasible but unit cost ~$11.40 > $3.50 — CNC cost barely falls with volume; at 5,000 parts move to the FORMED family (thermoplastic + injection molding).
  • PLA + 3D printing is cheap on screen but fails strength: PLA can't meet 40 N + outdoor temp. Keep molding, switch to ABS or Nylon-6.
  • Your pairing passes but the justification names only 1 alternative (need ≥2) and no losing axis — for each alternative, name the requirement it fails.
  • You wrote Aluminium+CNC 'loses on strength' — it's actually stronger; it loses on cost ($11.40). Match the losing axis to the numbers.
05
Retrieve & space

Bring back what you've already mastered.

  • From Lesson 1.1: your process is injection molding — which tagged defect does a mold care about most? → no draft.
  • From Design course: in the part header, identify a functional dimension (fixed) vs a shape dimension (free) — pre-loads 1.3's function-preservation rule.
  • Spaced: drag the injection-molding break-even marker to roughly where molding overtakes CNC on the count axis.
06
Mastery gate

What you must demonstrate to advance.

In sim, select a pairing the twin marks feasible and meeting all four requirements, with a justification beating ≥2 alternatives whose losing axes match the twin's estimates, on seed 1102.

07
Project

How this feeds your build.

Commits the part to a process (ABS + injection molding), which decides which DFM rules 1.3 must apply, which process M2 generates toolpaths for, and the cost basis the M5 capstone optimizes.