Proactive Verification of Strip Y-Index to Mitigate Gross Misaligned Cut due to Mismatched Unit Pitching

Shopfloor practices that when the first cut line was aligned with the hairline, actual blade cut, and saw street of the strip, the succeeding cut lines will automatically follow with the same alignment. Considering various factors that affect the condition of the strip, it was recommended to verify the succeeding cut lines of the strip to project if the hairline will still be aligned with the saw street as cutting goes on. Unfortunately, verification of succeeding cut lines was usually skipped and refer only with the first cut alignment as reference. Thus, end up risking the units for possible cutting misalignment. Cutting misalignment can be encountered when the programmed unit pitching measurement was mismatched with the actual unit pitch of the strip. However, mismatching of the unit pitch can be anticipated through y-indexing where the saw street of the strip will be verified for alignment with the hairline along the succeeding cut lines. Frequent occurrence of mismatched unit pitching was brought about by the strip condition after series of assembly processes that expands and retracts the strip. With the mentioned scenario which has been encountered from different semiconductor assembly plants, it was best to verify the y-indexing of the strip on top of verification on the first cut line alignment. Application of y-indexing verification is essential for the entrapment and correction of mismatched unit pitching. Rejection of units due to misaligned cuts can also be prevented. Assistance of operators to adjust and monitor the hairline to compensate the actual pitching was also avoided as early as first cut line verification.

Supervised Detection of Connector Lock Events with Optical Microphone Data

In manufacturing industry, one of the main targets is to increase automation and ultimately to avoid failures under all circumstances. The plugging and locking of connectors is a class of tasks which is yet hard to be automatized with sufficiently high process stability. Due to the variation of plugging positions and external disturbances, e.g. occlusion due to cables, the quality assessment of plugging processes has emerged as a challenging task for image-based systems. For this reason, the proposed approach analyzes the inherent acoustic connector locking properties in combination with different neural network architectures in order to correctly identify connector locking signals and further to distinguish them from other machining events occurring in assembly plants. For this specific task, highly sensitive optical microphones have been applied for data acquisition. The proposed experiments are carried out under laboratory conditions as well as for the more complex situation in a real manufacturing environment. In this context, the usage of multimodal neural network architectures achieved highest levels in classification performance with accuracy levels close to 90%.

COMPLEX TOOLS FOR INCREASING THE EFFICIENCY OF THE PRODUCTION SYSTEM OF A MACHINE-BUILDING ENTERPRISE

The article presents the results of the development and implementation of comprehensive tools for increasing efficiency in modern production systems of car assembly plants.

The Practice of Solving Product Quality Problems Obtained through the Use of Design Activities at Car Assembly Plants

The paper presents the results of the development and implementation of design approach tools for solving quality problems of new cars in operation. The generalization of the experience of the project teams in solving problems in the field of the quality of new vehicles in operation.

Supply Chain Proximity and Product Quality

We estimate the effect of supply chain proximity on product quality. Merging four automotive data sets, we create a supply chain sample that reports the failure rate of 27,807 auto components, the location of 529 upstream component factories, and the location of 275 downstream assembly plants. We find that defect rates are higher when upstream and downstream factories are farther apart. Specifically, we estimate that increasing the distance between an upstream component factory and a downstream assembly plant by an order of magnitude increases the component’s expected defect rate by 3.9%. We find that quality improves more slowly across geographically dispersed supply chains. We also find that supply chain distance is more detrimental to quality when automakers produce early-generation models or high-end products, when they buy components with more complex configurations, or when they source from suppliers who invest relatively little in research and development. This paper was accepted by Vishal Gaur, operations management.