Recently, abnormalities have occurred intensively in multiple departments of the company. Problems have frequently emerged in the electronics, structure, and testing processes, and the pressure of abnormal situation handling has increased significantly. In cross - departmental collaboration, many initially determined "conclusions" have been overturned, which exposes the crucial role of technical root - cause tracing ability. It also gives me a clearer understanding of the value of my position and my career direction.
Electronic anomaly: Rehabilitation of the false accusation against the LM358 chip
The first typical case points to an abnormality in the LM358 chip in the electronics department. The engineering side initially determined it to be a chip quality issue introduced in the quality assurance process and directly ordered the return of this batch of LM358 chips. However, I have doubts about this conclusion - as a mature and general-purpose device, the probability of large-scale quality defects in the chips is relatively low. To verify my conjecture, I independently built a test circuit identical to that on the production line. By inputting standard signals and monitoring the output waveforms and parameters, I reproduced the original abnormal phenomenon. Subsequently, I replaced the chips with a new batch, and the results remained the same, ruling out problems with the chips themselves. Further investigation revealed that when the engineering team converted the schematic diagram into the actual wiring diagram, there was an error in pin correspondence: in the original design, the feedback pin and the ground pin of the chip were reversed during the conversion, resulting in abnormal circuit functions. Finally, the responsibility was corrected from quality assurance to an oversight in the engineering conversion, avoiding unnecessary supply chain cost losses.
Structural abnormality: Use design optimization to digest raw material fluctuations
The abnormalities in the structural department involve a 12% product defect rate, initially attributed to the sub - standard performance of raw materials. I didn't stop at the superficial conclusion but carried out an analysis by comparing two types of data: vertically sorting out the raw material inspection data of the past six months, I found that although the key indicators (such as toughness and dimensional tolerance) of this batch of materials were slightly lower than the historical average, they were still within the error range promised by the supplier; horizontally comparing the structural designs of similar products, I found that the "pre - deformation compensation structure" used by competitors could significantly offset the impact of minor material fluctuations. Based on this, I designed three groups of comparative experiments: retaining the original design + current raw materials, original design + high - quality raw materials (with a 30% higher cost), and optimized design (increasing the pre - deformation by 0.2mm) + current raw materials. The results showed that the defect rate of the optimized design group dropped below 1%, that is, more than 90% of the impact of raw material fluctuations could be masked through design changes. Moreover, the cost per set of the current - priced raw materials was 25% lower than that of high - quality materials, and nearly one million in procurement costs could be saved annually. This conclusion not only solved the defect problem but also verified the cost - control logic that "design optimization is superior to simply relying on high - priced materials".
Test exception: The technical blind spot behind the dispute over jigs
The contradictions in the testing phase have exposed the cognitive biases in departmental collaboration. The Engineering Department has long believed that the self - designed test fixtures have "the most accurate parameters and the most perfect logic". However, when the Quality Assurance (QA) team recently conducted incoming inspections on a batch of products, they got a "total failure" result with 100% defective products - the same batch of products passed all the tests on the production line using the Engineering Department's fixtures. Both sides held their ground, and the focus of the dispute centered on the "difference in fixture standards". After I intervened, I disassembled the core modules of the two sets of fixtures. By comparing the calibration parameters, I found that the reference voltage calibration value of the QA fixture was 0.1V lower than that of the production - line fixture (exceeding the allowable error range of the product). When checking the test logic, I found that in the threshold setting for determining "qualified" products, the QA fixture mistakenly wrote "
From exception handling to career transition: Professional skills are the foundation for standing firm
The process of continuously handling these exceptions has made me deeply realize that in technology-intensive enterprises, if QE (Quality Engineer) lacks the solid ability to conduct in-depth technical exploration, they are very likely to fall into the predicament of "passively taking the blame" or "being questioned by the department" - relying on empiricism for initial judgments and being unable to present data or experimental evidence when disputes occur, which naturally makes it difficult to convince others. In the above three cases, whether it is circuit reproduction, design simulation, or fixture disassembly, the essence is to break the information gap with technical means, which is exactly the value of professional skills.
That's exactly why I'm even more determined to switch to a technical position. When dealing with the LM358 and structural abnormalities, the R & D department supervisor took the initiative to communicate with me twice and mentioned that "your technical analysis skills are more suitable for front - end R & D", and hinted that they could internally recommend me for a position transfer. Currently, I plan to officially submit an application to the R & D department two months later, aiming to transform the problem - insight and technical verification abilities accumulated in anomaly handling into the risk prediction and solution optimization abilities during the product design stage.