I connect materials physics, designed experiments, and production data to explain defects and improve processes — from CNC programming to gigafactory quality engineering.
Muhammad Munajad
Process Quality Engineer · CATL Karawang
Start where it matters to you
The problem I work on
Manufacturing failures are rarely mysterious for lack of data. They are mysterious for lack of explanation.
A production line generates more measurements than any engineer can interpret, yet the causal chain from process parameter to microstructure to defect usually stays implicit. Classical designed experiments handle a handful of factors against an assumed response surface. Purely data-driven models need volumes of data that an expensive experimental campaign cannot produce, and they extrapolate unphysically the moment they leave the sampled region.
Battery and semiconductor manufacturing are scaling faster than process understanding. I spend my working days on that gap: high-volume lines where a deviation is measured in parts per million and the cost of an unexplained defect is counted in production days.
Three case studies: the problem, the decisions and the alternatives they beat, and what was measured — or explicitly withheld.
The same four steps whether the subject is a toolpath, a casting, or a gigafactory line. Tools change; the loop does not.
Establish what the process is actually doing before theorising about it — measurement systems first, opinions second.
Take the failure down to the material. A rejected part is a materials event before it is a process event.
Design the experiment so the answer is attributable. Factor screening before optimisation, and significance before conclusions.
An improvement that is not held is not an improvement. Control plans, limits, and audits are part of the engineering, not paperwork after it.
Each thread is labelled by maturity. Proposed means exactly that — a reasoned intention with no work behind it yet.
Can infiltrated stir casting distribute hybrid reinforcement uniformly enough to raise specific energy absorption without losing castability?
Lightweight crash structures are judged on energy absorbed per unit mass. Magnesium starts from the best possible density and the worst possible stiffness, so the question is whether reinforcement can be added by a route a foundry could reproduce.
At gigafactory volume, which defect modes are genuinely explainable from routine line data, and which require characterisation that production cannot afford to run?
High-volume battery manufacturing produces more process data than any team can interpret, and the cheapest measurements are rarely the ones that carry the causal signal. Knowing which is which decides where inspection ends and process control begins.
Can laser powder bed fusion process windows be qualified predictively, instead of by exhaustive build-and-inspect trials?
L-PBF makes microstructure a designed variable rather than an inherited one — and simultaneously makes it path-dependent, which is why qualification currently depends on post-hoc CT inspection and campaign after campaign of trial builds.
Can known process physics be embedded in a learned model well enough to give predictive power from the small, expensive datasets a real experimental campaign produces?
This is the methodological question behind the other three. Classical designed experiments assume a response surface and handle a handful of factors. Purely data-driven models need volumes of data that powder-bed or production characterisation campaigns cannot produce, and they extrapolate unphysically outside the sampled region. Constraining the model with conservation laws and known process behaviour is the route to prediction from small data — which is the only kind of data manufacturing research actually has.
I started at the machine, programming toolpaths where a wrong decision shows up in the surface finish within minutes. Tooling came next, then whole machines, then the materials those machines were failing to form predictably — which is what took me to a research group. Today I work at the other end of that chain, on production lines where the same questions are asked in parts per million. Every step down that path was an attempt to reach the layer where the explanation actually lived.
Machining
Multi-axis CNC programming
Tooling
Precision injection molds
Machines
Industrial process machinery
Materials
Composites research, NTUST
Automation
Special-purpose machines, robot cells
Process at scale
Gigafactory process quality
Every externally verifiable signal, in one place, with the link to check it.
An academic CV and scholarly profile links will be added here. Until they exist they are not listed — an empty entry is worse than an absent one.
Open to doctoral study, engineering roles, and research collaborations
Pick the one that is yours. Each arrives with the right context already attached.