In competitive mold-making and injection-molding markets, where tight pricing and narrow profit margins are the norm, end quality is the one thing that distinguishes a manufacturer from others. Molds have to be perfect, and the correct-specification parts made from them must be delivered on time and with no production delays.
It always sounds simple, but truly understanding all sources of underlying part variation is not. Managing variation, however, will always save time and money and boost customer confidence. F. & G. Hachtel in Aalen, Germany, uses Computed Tomography (CT) analysis to predict, measure, and control variation, distortions, and metallurgical faults. The software is used for in-process and final part inspection and production automation.
Passing Along Lessons
Hachtel operates a multi-service bureau for injection molding, mold-making, CT, and additive manufacturing. The injection-molding branch specializes in complicated processes and multi-component parts and materials. Here, tooling automation is important because the assembling tools put together the part directly as it leaves the mold. Customers are primarily in appliances, electronics, and automotive. Exacting part qualification is demanded – and achieved with the help of CT software analysis.
Other manufacturers facing similar customer demands and challenges may benefit from a supplier situation Hachtel encountered that allowed it to fully test its inspection solutions.
Taming The Spring
Recently, Hachtel received a large batch of defective tooling springs. Its inspection system, which relies on software from Volume Graphics, identified the problem immediately. Because of the software’s ability to automate inspection, it was decided that rather than reject the entire shipment, it would be better, and a test, to use the software’s adaptive measurement template function to identify and save any good parts that might remain in the production lot.
Adaptive measurement templates track the shape of distorted parts against a nominal CAD model, mesh, or ideal part profile derived from a CT scan. With adaptive measurement templates, a measurement plan can be created for even strongly deformed parts using these data sets or imported data via a product manufacturing information (PMI) file. Measurement points are placed at the optimal positions on the actual part and perfectly follow the distorted shape, allowing for analysis and then acceptance or rejection.
The springs are inlays for an injection molding process and receive a plastic tip on one end. Because Hachtel gets hundreds of thousands of these springs, its tool-production process is highly automated by necessity. A positioning system places each spring individually on a transfer plate from which a handling arm takes a set of springs and loads them into the molding tool. These steps are critical moments in the process – if a spring falls out of the handling or transfer plate, the machine halts, and production stops. In the best case, this event requires only a little human interaction to replace the spring. In the worst case, the mold requires cleaning because the plastic was pushed out of the designated shape and nest.
To reduce the rate of potentially expensive, periodic downtimes, Hachtel decided to control all the springs upon arrival and to use inspection as a decision aid for whether the springs get rejected or if they will work and can be processed. Receiving a shipment of mostly deformed springs put the CT inspection software and related handling functions to a ‘stress test,’ one the company felt it could benefit from if further studied.
Normal CT inspection typically uses a classic 3D design with defined dimensions as a basis for comparison. However, even routine springs rarely match the original CAD model perfectly, and spring shapes can fluctuate between individual batches. This made the first setup transfer of measurement templates from the CAD model, and even from proper pre-existing springs, difficult and time-consuming to manage.
Yet, the necessary pre-alignment was easy to automate, and all samples were pre-aligned per a macro. The transfer of the measurement template initially needed an individual inspection of each sample and a refitting of several geometry elements. Hachtel tried to use localized coordinate measuring machines (CMM) early on to help with correct fitting, but this increased the complexity of the template process and helped only a little with deformed parts. Some springs were too distorted to allow for an easy fit.
However, with the fully automated adaptive measurement template, Hachtel could ignore a pre-alignment step and didn’t have to perform any re-fitting of geometry elements. Indeed, after the application of the measurement template, the team could take target features and geometries and create a ‘registration’ of accurate part shapes. Now the registration, based on a saved datum system that included the distortion of the parts, was covered within the transfer. This allowed for slimmer and less complex measurement templates.
Time and Resource Savings
The entire non-automated CT inspection process per spring was previously a two-minute preparation stage. The pre-alignment calculation took another three minutes. The copying of the measurement template took only 15 seconds, and the manual re-fit of the elements took another five minutes. The classic approach meant 10 minutes per part, of which the second half was manual work with unnecessary labor costs.
Using the automated adaptive measurement template, the process still consists of two minutes of preparation. The transfer of the template takes about five minutes per part. This absolutely saves about three minutes per sample. The benefit, however, exceeds this three-minute time savings because the process runs automatically and doesn’t require further attention or adjustments. So, for tooling and production shops, this methodology effectively allows for the entire automation of measurement tasks, some of which previously needed to be performed manually.
In the case of the large quantity of distorted springs shipped to Hachtel, some springs were saved and filled a production need. The system proved itself not only for routine production inspection but, in this situation, very efficiently for unexpected crisis moments.
Automated CT inspection saves time and resources and preserves customer satisfaction. Furthermore, it provides unmatchable insight into sources of variation and metallurgical quality.
This article was contributed by Kamil David Szepanski, Head of Technological Development and Product Development CT&AM, F. & G. Hachtel