CAll Us: +1(212)-966-0410

Author Archives: Dmitriy

The PIPE, old-school real-time image processing

The PIPE, old-school real-time image processing

The PIPE, old-school real-time image processing

Randy Luck
revised 31-December-2019

Aspex Incorporated developed the PIPE system (Pipelined Image Processing Engine) in collaboration with the National Institute of Standards and Technology (NIST) in the mid-1980’s. This system was well ahead of its time but also a part of the times. The hardware was developed by Randy Luck, BJ Henrici, and Jim Herriman in conjunction with software by Jim Knapp, Shoshi Biro, and several others, all of Aspex in consultation with Ernie Kent, Mike Schneier, Tom Wheatley and others at NIST. An early version was described in Kent’s US patent [1] and in Kent, Shneier, and Lumia [2]. The actual PIPE implementation is better described in Luck [3] and [4].

PIPE was designed to process video images at real-time video frame rates. It consisted of modular processing stages (MPS) that could be flexibly connected under program control in many series or parallel combinations. Each MPS was implemented on two large 15” x 13” circuit boards. Each board was populated with about 200 ICs – 74ALS, 74F, and 74AHCT logic, GALs (small FPGA precursors), dynamic RAM, static RAM, and PROMs.

PIPE MPS circuit boards

MPA board

MPB board

The architecture was optimized for real-time point, spatial, and temporal image processing. The design made heavy use of SRAM look up tables and ALUs to perform arbitrary pixel point processing such as scaling, summing, thresholding or non-linear operations. Each MPS had two frame buffers and could perform two real-time 3 x 3 arithmetic or Boolean convolutions. The MPS had what was called a TVF, a two valued function look up table. With two 8 bit data inputs, a 64K x 8 SRAM could perform any operation on two values. Thus with one convolution performing the X gradient, and the other the Y gradient, the TVF could then be pre-programmed with a table of the square root of the sum of the squares of the X and Y gradients and therefore perform things like the Sobel operator in real-time. The frame buffers gave the PIPE temporal processing capabilities. Images were written into the frame buffers while the previous image could be simultaneously read out. The timing system allowed all frame buffers to run synchronously with each other. An entire PIPE consisted of up to 8 such MPS board sets. Each MPS had local image flow connections forward from the previous MPS, recursively from itself, and backwards from the next MPS in the pipeline. This made the system perfect for experimenting with various frame to frame and optic flow algorithms. In addition to the MPS, the PIPE had a video A/D front end and D/A back end for B&W or RGB cameras and other video sources and video outputs to monitors, a set of input frame buffers, a set of output frame buffers, a control stage that orchestrated all the control and handled host computer interface, and finally a processor called ISMAP which performed several kinds of histogram and cumulative histogram functions at frame rate. The system also had 6 video buses that allowed images to be broadcast from anywhere to anywhere in the system beyond the local connections. Other features included region of interest processing and host controlled literal bytes. An AT/386 PC running MS-DOS functioned as a host control and programming computer. PIPE could also connect to a VME or Multibus interface for higher level image understanding applications on high speed computers.

8 stage PIPE system block diagram

MPS block diagram

In 1989 I created a PIPE demo video tape for use as a marketing tool. It included implementations of algorithms that I developed along with example video sequences that some of our customers had developed. Unfortunately the original 3/4” U-Matic master tape has disappeared in the mists of time and only a VHS copy of the demo remains. So the video quality you see on the YouTube video is not great. . Here is the link to the 1989 PIPE demo tape on YouTube.

Description of the demo sequences on the video

1) A sorting application using pattern matching

This is a translation, rotation and scale invariant matching algorithm. It first computes the Sobel edge direction, thresholded by the magnitude. Then the histogram of that is found. With a black background, the object becomes dominant in the histogram. This histogram is simply a list containing a count of the number of pixels in each of 180 edge directions (2o increments were used). Notice that the concept of image structure is eliminated and the histogram becomes essentially a unique signature for the object. Since the structure is gone, it becomes translation invariant. As the object is rotated, the histogram pattern shape remains the same but it just rotates around the 180 possible directions. Object matching then just becomes a rote comparison of the known signature histogram with the current histogram at all 180 rotations. A few other optimizations needed to be applied. In an image with square pixels, the pixels are 1.414 longer in the diagonal direction which causes an error, so this correction must be applied to the histogram pattern. If the measured pattern is normalized, for example to the bin with the maximum count, then over a reasonable range of sizes, the matching becomes scale invariant as well. In the video, the PIPE is performing the image processing, the ISMAP is performing the real-time histogram, and a program in the attached PC is doing the histogram normalizing and pattern matching. Two signature patterns were tested, one for the front and one for the back of the VHS box. As I recall the image processing worked at about 10 to 15 Hz, but clearly from the video, it looks like the computer matching is slower, about 1 Hz. I gave a paper on this [5] at an Electronic Imaging East conference. Here’s a link to a PDF of this paper. We also demonstrated this algorithm using an attached neural network co-processor to perform the pattern matching at an SPIE conference in the late 1980’s. This algorithm is somewhat like a global version of the HOG algorithm and was independently invented at around the same time.

2) Object tracking applications

The first example is performing a frame difference followed by thresholding and Boolean dilation. Histograms from ISMAP are finding the center of mass of the lit up pixels and then the host is drawing a cross hair cursor on that spot.

The Boston University sequences were courtesy Allen Waxman and are described in [6] and [7].

3) Template matching for quality control

This is showing a Sobel magnitude image, followed by histogramming. The differences between the current live histogram and a stored known good histogram are shown as a number.

4) Various image re-mapping functions

The first part is performing a log-polar transformation similar to Weiman and Chaiken [8] using non-linear functions for the read addresses of the image in the TVF.

The second part shows terrain mapping sequences, courtesy Eamon Barrett at Lockheed. Essentially this is Google Earth, 1988 style.

The driving demo shows variable resolution, depending on where the cursor is located. The ideas is that if the user’s eye gaze point could be sent at a low data rate back to the remote vehicle, then a variable resolution low bandwidth image could be returned to the user. The image is high resolution where the user is looking and progressively lower resolution in the periphery. This variable resolution fovea idea was conceived around the same time that the data compression techniques in JPEG and MPEG were invented. In the video, the periphery pixels look like they could use some filtering.

5) Thinning using Boolean morphology

PIPE’s Boolean neighborhood is performing connectivity preserving thinning on binary images.

6) Blob analysis using connected components

The PIPE is just capturing and thresholding the image, the attached PC is running a connected components program finding areas and bounding boxes. We had plans to develop a real-time connected components processor (CONCOMP) board for the PIPE, but never finished it.

7) Dynamic image centering

An early example of image shake reduction. The PIPE and the attached PC are finding the centroid of the Space Shuttle in the image, then adjusting X and Y address offsets to keep the centroid in the center of the frame. Every video camcorder made now has a similar feature, some use tracking, some use accelerometers.

8) Model based vision

The first part shows the PIPE running a corner detector using Gaussian curvature. Shrinking reduces these curvature maxima to dots. The ISMAP histograms make finding the X, Y locations of the dots easy. The attached PC then performs the model match as shown on the PC’s screen.

The second part was courtesy of Chuck Dyer at the University of Wisconsin and is described in [9].

9) Motion flow computations for velocity and direction

The first part of this demo is performing the simple Horn and Schunk optical flow computation [10]:

Essentially this is just the frame difference (delta time) image divided by the Sobel magnitude (spatial gradient magnitude). As the frames flow through the PIPE, temporally, the delta time image is computed two frames apart using the first and third, and the gradient magnitude is computed from the middle, second frame.

The second part of this demo is based on van Santen and Sperling [11] and Adelson and Bergen [12]. It is computing “Reichardt” type quadrature detectors in (x, t) and (y, t), thresholding on strength, taking the atan2 of the (x, t) and (y, t), and finally color coding for direction. The PIPE implementations of these image flow algorithms are described in [13].

10) Edge detectors

The first part is showing the Sobel direction, thresholded by the magnitude, and then color coded for edge direction. One PIPE stage could perform all this at 60 Hz.

The second part is showing thresholded zero crossings from the difference of Gaussians.

11) Hough transform for lines

The Hough transform based on histograms of Sobel direction images. You can see the input binary image in the lower left and the Hough space along the top.

12) PIPE’s menu driven, graphical software interface is both easy to use and versatile

PIPE had its own micro-coded programming system. Run-time programs could be downloaded, and it would run by itself. We devised a software tool called ASPIPE [14] that was written in C, ran on MS-DOS, and used the PC’s EGA text mode graphics characters. It was essentially a text mode pop up windowing system, developed before Windows existed. I based some of its visual design and organization on an article on the Smalltalk environment that I had read about in Byte magazine. The entire ASPIPE program was less than 640K bytes, though as I recall it did use overlays. The software used graphic representations of the signal flow in the PIPE hardware. It presented the entire system with the physical orientation of the processors along the horizontal axis and frame time on the vertical axis. To program it, you set up the image data flow through space and time. You could click on objects and menus would pop up to let you make selections on how that hardware object would behave at that moment in time. Click on one of the processors on this chart and a diagram of the MPS would pop up. Then click on, for example, a neighborhood operator and set up the mode and mask in a pop up. A second tool called LUTGEN let you enter equations to make the various look up tables. The LUT functions could be graphed out, created, saved as small files, and then could be re-used in any program. PIPE’s main control was via an attached PC. Due to limits in the state of the art at the time, the PC/AT bus interface was not fast. PIPE had two computer ports and some customers opted to connect the second port to a VME or Multibus interface to link higher speed computers like the Sun, Sequent, Apollo, Masscomp, and others for faster high level vision processing.

2019 retrospective, 30+ years on

The PIPE’s pixel, image and frame synchronous nature was both a significant benefit, but also in hindsight a limitation for some applications. Image size was fixed at 256 x 240, 8 bit precision, and 60 frames per second. This meant that the user did not need to deal with setting the frames up which was good, but limiting. After customer requests, extensions were added that allowed 512 x 480 image resolution at 15 Hz and 16 bit precision but this capability was not easy to use. For some kinds of algorithms like morphology, one might have wanted to cascade several erosions or dilations in sequence without incurring frame delays, but the architecture didn’t allow that. Everything that flowed into a MPS needed to pass through a frame delay. Pipelining up to 8 stages allowed the 60 Hz frame rate to remain at full speed, but there could be latency delays of up to 8 frames. If an algorithm needed more than 8 stages of processing, you could then add more time by slowing the processing down to 30, 20, 15, 10, 7.5, etc. Hz. Other contemporary vision processor architectures could avoid this issue. For example ERIM’s Cytocomputer [15] was intended to perform cascades of morphology operations within a frame time and it was really good for those kinds of inspection type algorithms, but it didn’t have the temporal optic flow capabilities that the PIPE had. Cal Tech’s PIFEX architecture [16] was in some ways more flexible because it used a more general cross bar architecture to connect its various processing hardware elements. I don’t think it was ever commercialized though. By the late 1980’s, we started to consider a next generation PIPE 2 architecture that combined the best features of the PIPE with cross bar connections similar to PIFEX. The PIPE was also contemporaneous with and in many respects, faster for many image processing applications than the more general purpose WARP systolic array processor from Carnegie Mellon University. An 8 stage PIPE system could run at up to 1.2 GOPS.

Aspex Incorporated ultimately sold 42 PIPE systems, a few with 1 MPS, but most either with 3 or 8. These went to university, government, and corporate research laboratories in the US and Asia. Some 10 systems were sold to Neuromedical Systems Inc. which used PIPE as the image processing front end for the first generation of their neural network based automated pap smear screening system, PAPNET described in their patents and this article [17].

One of the reasons this PIPE 2 never got developed was because ultimately something else did in the PIPE and all the other contemporaneous dedicated processors. By the early 1990’s, Intel 486 and Pentium processors and the new PCI bus got fast enough to be realistic for machine vision use. Essentially Moore’s Law caught up. This enabled most commercial customers to be able to do their machine vision applications using a simple PCI frame grabber and software running on the PC in a much more cost effective system. While the spatio-temporal image flow processing of the PIPE was very interesting to some in the research community, most industrial applications didn’t need it. Today I can write many of the same applications seen on this video using OpenCV and C++ or Python. On my Intel I7-8700 motherboard, these apps can run at speeds close to what the PIPE was doing back then but with much higher resolution frames. My desktop PC was put together for a very reasonable sub $1K cost. Lesson learned, ignore Moore’s Law and its various corollaries at your peril! Another lesson learned, a major cost reduction will almost always win over the incumbent technology even when there is also a small performance reduction. I had a professor tell me that the PIPE was really cool, but that he could get 10 Sun-3 systems and have 10 students working at the same time for the cost of 1 PIPE. Even though the Sun-3’s were not real time, the increased utilization was worth it. While it might be interesting to contemplate putting all the PIPE’s functionality into a few FPGA’s, very little you can create in this way can easily compete with close to free.

I think a second reason PIPE 2 never got developed had to do with the fall of the Berlin Wall. Many of the PIPE customers got their funding from various agencies of the US Defense Department. During the Reagan era, there seemed to be lots of money available. After the Wall fell and the Soviet Union broke up, for all intents and purposes, the Cold War ended and these funding sources dried up.

It was a lot of blood, sweat, and heartache, but also fun while it lasted!

The PIPE had many attributes that were really useful for temporal computer vision. Today, most computer vision applications both for industrial machine vision and for Convolutional NN’s, and Deep NN’s do not use or take much advantage of the temporal domain. Hot contemporary applications like ADAS and vision for self driving cars could benefit a lot from using the rich information available in the temporal domain. Also I think that OpenCV could really benefit from making the temporal domain easier to set up and use.

Back in the 1980’s there was a lot of interest in special purpose attached processors. The PIPE was one, and there were many others with similar acronyms; PIFEX, Cytocomputer, and Warp (all mentioned above), PUMPS, PICAP, ZMOB, Vicom, Butterfly, FLIP, MPP, Pixar (yes that Pixar), HNC, and many others. These were all developed because general purpose computers were not fast enough to handle the massive amounts of high speed data involved in computer vision. Today, the latest desktop PCs and even the processors in smart phones are fast enough for many imaging tasks. However, now there is a lot of renewed interest in special purpose processors that can handle certain tasks at higher speeds than a general purpose CPU. At the Embedded Vision Summit 2019 conference, I heard that over the last 2 years, VC’s have invested more than $1.5B in special purpose vision, NN, and AI chip companies. The original backpropagation based neural networks typically had only 3 layers, an input layer, a hidden layer and an output layer. In the PAPNET cancer screening system [17] mentioned above, the NN operated on 32 x 32 chunks of monochrome pixels that were selected by the PIPE image processing as most likely to be containing the nucleus of a cell. The NN then determined if that chunk was most likely a cancer cell. The NN therefore had 32 x 32 = 1024 input neurons. The hidden layer was about 25% of the input or 256 neurons and it had a single output neuron since it was just classifying how likely the 32 x 32 area had a cancer cell. Originally they used an attached “neurocomputer”, but Moore’s Law caught up and later production versions used a standard computer.

In contrast, today’s Convolutional NN and Deep NN’s such as ResNet-50, AlexNet, and others are a different beast altogether. These NN’s can be applied to operate on high resolution and/or color images. AlexNet uses 256 x 256 RGB input images, has 8 layers, 60M parameters, and 660K neurons. It was programmed to run on two Nvidia GPUs. ResNet-50 has 50 layers. These NN’s are only realistic on attached GPU’s, FPGA’s, or custom ASIC processors. Today’s AI chip companies are quoting NN inference in the tera- and peta-ops range. So it seems that the battle between custom attached processors and the standard CPU has come full circle.


[1] Kent, US Patent 4,601,055, 1986.

[2] Kent, Shneier, and Lumia, “PIPE (Pipelined Image-Processing Engine)”, Journal of Parallel and Distributed Computing, V2, Issue 1, pp 50-78 (Feb. 1985).

[3] Luck, “PIPE: A Parallel Processor for Dynamic Image Processing”, Proc. SPIE V.758, (1987).

[4] Luck, “An Overview of the PIPE System”, Third Int’l Conference on Supercomputing: Supercomputing ‘88, Vol III, Boston, MA, (1988).

[5] Luck, “Translation, Scale, and Rotation Invariant Pattern Recognition Using PIPE”, Proc. Electronic Imaging East ‘88, (1988).

[6] Waxman, Wong, Goldenberg, and Bayle, “Robotic eye-head-neck motions and visual-navigation reflex learning using adaptive linear neurons”, Neural Networks, V1, Supplement 1, page 365, (1988).

[7] Baloch and Waxman, “A neural system for behavioral conditioning of mobile robots”, International Joint Conference on Neural Networks, (1990).

[8] Weiman and Chaiken, “Logarithmic spiral grids for image processing and display”, Computer Graphics and Image Processing, 11, (1979).

[9] Verghese, Gale, and Dyer, “Real-time motion tracking of three-dimensional objects”, Proceedings, IEEE International Conference on Robotics and Automation, 1990, pages 1998-2003.

[10] Horn and Schunck, “Determining Optical Flow”, Artificial Intelligence 17, (1981).

[11] van Santen and Sperling, “Elaborated Reichardt detectors”, JOSA, Vol. 2, No. 2, (1985).

[12] Adelson and Bergen, “Spatiotemporal energy models for the perception of motion”, JOSA A, Vol. 2. Issue 2, (1985).

[13] Luck, “PIPE, a parallel processor for dynamic image processing”, Proc. SPIE V.758, (1987).

[14] Luck, “ASPIPE: A Graphical User Interface for the PIPE System”, Proc. SPIE V.1076, (1989).

[15] Sternberg, “Parallel architectures for image processing”, Proc. 3rd International IEEE COMPSAC, pp. 712-717, (1978), (and numerous other subsequent articles and patents by Sternberg, Lougheed, and/or McCubbrey).

[16] Gennery and Wilcox, US Patent 4,790,026, (1988).

[17] Luck, Tjon, Mango, Recht, Lin, Knapp, “PAPNET: An Automated Cytology Screener using Image Processing and Neural Networks:, Proc. SPIE 20th AIPR Workshop, V.1623, 161-171 (1991).

ASPEX Incorporated Participates in ITMA 2019 Barcelona show

ASPEX Incorporated Participates in ITMA 2019 Barcelona show

Update: 28/6/19

Aspex Inc. has recently participated at ITMA in Barcelona with resounding success. Customers have shown keen interest in the latest developments announced including their new multiscanning inspection. Multiple customers visited including some that have used the SpinTrak system for more than 20 years and indicated their satisfaction about it’s performance and usefulness to their process control.

ASPEX INCORPORATED is pleased to announce their participation in the upcoming ITMA show from 20 to 26 June, 2019 in Barcelona Spain. They will be participating in Hall 7 and Stand C 117 demonstrating their latest SpinTrak Spinneret Inspection technology for filament, fiber and nonwovens producers.

Founded in 1978, Aspex Incorporated is the original designer and manufacturer the of the SpinTrak™ Automatic Spinneret Inspection System. The SpinTrak™ family of Spinneret inspection systems are specifically designed to inspect all types of extrusion dies/spinnerets used in filament yarn, staple fiber, spunbond, meltblown and spunlace applications. Over 500 SpinTrak™ Spinneret Inspection Systems are operating in all major textile producing countries in the world. SpinTrak™ enjoys a very big marketshare in the fiber/filament industry and is also used by leading machinery and spinneret companies.

“Aspex Incorporated is a unique company because we were the first to introduce a reliable working system into the market several years back and considered the standard in the industry. Years of working closely with key machinery and spinneret companies has provided Aspex valuable know-how for optimizing the performance, reliability and accuracy in the SpinTrak™. Huge importance in the design of the SpinTrak™ is to be able to perform continuously and accurately in a harsh plant environment. SpinTrak™ systems have a reputation for reliable performance and have proven to work well for an extended number of years. Aspex customizes each SpinTrak™ to offer the most economic solution and maximum performance for their specific process application. Every plant has different processes, specialties, capacity, etc. », stated Gerald Henrici, the company President. Aspex Incorporated now operates three locations to better support their ever expanding market place. New features on the SpinTrak™ will be demonstrated including their latest multiscan technology and a new marking system.

Aspex Incorporated operates their Head Office and factory in New York, USA. They have regional offices in Mumbai and Shanghai to support service and sales worldwide along with a team of agents in most major markets.

See our stand Design here



1) Some new features which aids in finding deep dirt inside the capillary, wear inspection (subject to spinneret condition), and skewness inspection.

2) Sub-capillary inspection of complex capillaries (not round) shape is one of our strongest features. For example, if we want to inspect trilobal capillaries, then our field of view (FOV) is higher than round capillaries because manufacturing tolerances for non-round capillary shapes are more than round capillary shapes. This explains why it is important to inspect non-round capillaries more accurately. The larger the FOV (more no. of pixels) – the lower the pixel to micron ratio is which provides more accurate measurements.

2a) For example, in trilobal capillaries, there are 3 legs and each leg plays very important role. Therefore, the SpinTrak™ makes more than 30+ measurements (ie. leg length , leg width , angle , radius of every leg).  All geometric measurements are made in the same inspection time like a round capillary. In other systems, many do not make sub-capillary inspection.

2b) In hollow capillary shapse (2C,3C or 4C), we consider every C as separate capillary. 2c) SpinTrak™ can inspect 4 capillaries separately in 1 counterbore and can also inspect 4 different capillary shapes in single spinneret.

In the last 15 years, Aspex inc. gained experience inspecting many new shaped spinneret designs with new or unique capillary shapes.  Some of them are expensive spinnerets ($20,000 / piece). This experience and industry cooperation helped accelerate improvements and new inspection capabilities in the SpinTrak™ system.

3) Having sold more than 500 machines in 30+ countries, Aspex has a very large global presence and SpinTrak™ usage in a wide range of applications.

4)  An important reason the SpinTrak™ has gained wide acceptance and sold in 30 + countries is it operates on a platform of menu driven user friendly software which is available in many popular languages where the  It requires almost zero maintenance with very low spare parts consumption. There is no need to purchase extra spare parts with the machine.

5) At this moment, SpinTrak™ measurements are considered as the “Bench mark” and “trusted” spinneret inspection system by end users and major Spinneret suppliers. SpinTrak™ systems are used by these spinneret manufacturers just to check quality of their new spinnerets.

6) As a testimonial to being the leader in the industry competitors try to emulate, the US court has issued an injunction against one of the competitors who violated our secrets and confidential information. This means that they can also do the same with customer.   In contrast Aspex Incorporated respects confidentiality and secrecy with their customer base like a trusted partner (like spinneret design , their know-how of production ,etc.).

7) The  SpinTrak™ system motorized zoom is offered on the B40-HS model. This means that no operator presence is required for adjusting the zoom level.

7a) The camera for the B40-HS is @1.4 megapixels. This provides a platform for a highly accurate system.

8) It is possible to inspect different spinneret types at a time. For example, Both spinnerets can be different spinneret type (with different no. of capillaries , different capillary size). This is applicable for up to 10 locations.

9) They use very low quality calibration scale and I strongly feel that it’s just for show and they also do not do calibration before inspection. Whereas , Aspex uses very high standard calibration scale (reticle) which is fitted into glass so that temperature variation does not affect. Their calibration scale is open to environment. This means that tool which they use itself is not accurate.

10) In some plants (for example, in China and India) , the customer returned competitor demo systems after trying them and bought SpinTrak™ because of their performance experience. Some positive feedback about the SpinTrak™  compared to competitor trials included the reliability of the software and the accuracty (especially important for smaller capillaries).

11) The important inspection measurements of the SpinTrak™ are based on sub-pixels & sub-microns. This means that our accuracy & repeatability is much better.

12) The first machine for inspecting a fiber spinneret was sold in 1992 & it is still working. This itself speaks about Aspex’s commitment to the product quality and industry support.

Aspex Incorporated  has become the standard known in the industry for Automatic Spinneret Inspection

Aspex Incorporated has become the standard known in the industry for Automatic Spinneret Inspection

Aspex Incorporated has become the standard known in the industry for Automatic Spinneret Inspection having achieved sales in more than 30 countries.  Aspex Incorporated has provided their systems in a number of types of manufacturing processes including synthetic fiber (virgin and/or recycled content), filament, BCF carpet extrusion, aramids (and high tenacity) applications, spunbond and meltblown nonwovens extrusion and other applications. Aspex Incorporated offers industry leading technology for inspecting the latest and most advanced capillary shapes. They offer advanced software solutions for inspecting the latest in spinneret designs for higher value added and special products. These include spinnerets with complex geometric layouts, (many times containing different non-round capillary shapes), higher numbers of capillaries and overall spinneret capillary density.

Testifying to our expertise, technology and industry recognition, our SpinTrak™ Spinneret inspection systems have been supplied to and delivered by major well known European machinery manufactures for new projects, and used for in-house inspection by several well known spinneret suppliers in the industry

To produce such a wide variety of advanced image processing systems, Aspex employs attentive sales people, experienced design and software engineers, and skilled production personnel. We are all committed to enhancing our reputation as the source for expertise and excellence in the design, development, and manufacture of image processing systems.  Most recently Aspex has launched their latest multiscan scanning option for certain model greatly increasing throughput capacity on certain models.

Aspex Incorporated  achieves a major order of 6 SpinTrak™ systems from Bhilosa Industries, India

Aspex Incorporated achieves a major order of 6 SpinTrak™ systems from Bhilosa Industries, India

Bhilosa Industries recent expansion project at Naroli Silvassa is one of the most prestigious projects that the industry has witnessed in recent times. The New setup is planned to be state of the art and will deploy high quality machinery and equipment from around the globe. Aspex Incorporated is proud to be associated with Bhilosa Industries in this new growth venture.

Bhilosa Industries has been associated with Aspex Incorporated since its inception and has historically relied on the SpinTrak™ automatic spinneret inspection system as part of their standard and “must-have” instrumentation.

For this expansion project, Bhilosa Industries has placed orders with Aspex Incorporated for supplying 5 units of SpinTrak High speed automatic spinneret inspection for the POY production and 1 unit for the PSF production.

The new production units of Bhilosa are scheduled to be fully operational from fourth quarter of 2017.

Manual vs. Automated Spinneret inspection systems Which is right for my plant?

Manual vs. Automated Spinneret inspection systems Which is right for my plant?

By Shoshana Biro, Software Engineer, Aspex Incorporated

Although the Spinneret appears to be essentially a simple, precisely-made extrusion die, it is one of the most critical elements of fiber making. Since they are at the very heart of the fiber-making process, plugged, dirty or damaged Spinneret capillaries can have a large impact on overall plant profitability.

Cleaning failures, damage from handling, and wear from polymer flow are the primary causes of poor Spinneret quality. Since these are everyday problems, inspection is a necessity to prevent less-than-perfect Spinnerets from being installed. There are several possible choices of inspection systems, of varying levels of sophistication. How does one select the system most efficient to their plants needs?

Systems can be categorized in many ways, but that most directly related to cost and plant productivity issues is automation. Manual systems range from the purely optical (microscopes, optical comparators and hand-held magnifiers) to video based microscopes that display a magnified image on a television monitor. On these systems the operator generally moves the Spinneret from capillary to capillary using hand-operated positioning knobs while looking at a magnified image of the capillaries. These systems rely on the operator’s ability to find hard-to-see defects among the hundreds or thousands of capillaries viewed each day. The subjective judgment of the operator is the deciding factor as to how many capillaries are defective, and until which point it is acceptable to use the Spinneret in production. In these systems, small quantities of dirt will remain undetected. In addition, the inspections are often compromised by time pressure due to the Spinneret being urgently needed. As a result, random inspection of a few capillaries rather than a complete inspection may be done, or the inspection may be stopped before finishing. Since defective capillaries are easily missed, the entire process is highly fallible.

While the initial cost of these systems is moderate, labor costs tend to be high, especially when there are many capillaries to inspect or production requires large numbers of Spinnerets to be inspected. These factors, when combined with lost productivity, can make the true cost very high.

Fully automated systems rely on computer-based machine-vision technology to reliably and objectively inspect every capillary. The capillary pattern for each Spinneret type is stored, as are pass/fail criteria that are used to judge a clean capillary. Using a motorized table the computer automatically moves the microscope from capillary to capillary while making inspections. The measured results are then compared by the software to the predefined standards and the Spinneret is determined to have passed or failed the inspection. The system also allows the operator to automatically go back to just the defective capillaries for re-cleaning and reinspection. During the inspection process itself the operator is free to do other jobs, minimizing labor costs.

Although the initial cost of these systems is significantly higher than manual systems, the advantages provided in having every capillary inspected in an objective and measurable way rapidly amortize the investment due to the improved efficiency of the plant, with higher first-quality product yield, longer spinpack life, fewer spinpack start-up failures, as well as savings on inspection labor and overtime. In addition, automatic systems can allow for the tracking of Spinneret history, which will point out problem Spinnerets, track Spinneret wear, and indicate the efficacy of the cleaning operation. By analyzing stored test results it is possible to create and evaluate “what-if” scenarios that can help to increase overall plant yield and quality, and reduce customer damage claims. Of course, buyers must be certain that the software they are receiving has all these capabilities, as some may not.

An example of this type of system is the SpinTrak™ Spinneret inspection system by Aspex. The system is fully automated, operates unattended, tests 100% of the capillaries, inspects as quickly as 3 capillaries per second and both provides several repeatable measurements of every capillary and stores them for either future comparisons or export to spreadsheets via the LAN. The benefits of these capabilities and the long lifespan of the SpinTrak™ system is the reason that the SpinTrak™ systems are now found in many plants around the world and why every SpinTrak™ owner with multiple plants has ordered additional units for their other plants.

When making the decision of which type of system to purchase you must consider all of the costs involved. Making the decision based only on initial price may cost much more in the end. You must try to evaluate all of the cost factors involved: labor, the risk of spinpack start-up failure, spinpack lifespan, cleaning expenses, transportation and product quality. While these expenses will vary from plant to plant, the total benefits offered by automated inspection usually far outweigh their higher initial cost.

Growth of the fiber market growing and production beginning to shift

Growth of the fiber market growing and production beginning to shift

By 2001, with the growth of the fiber market and production beginning to shift to countries with lower cost labor, Aspex Incorporated surpassed sales of more than 100 systems worldwide. Many of the earlier SpinTrak™ systems supplied in developing markets were transferred or purchased by larger companies from Western Countries who invested in new plants in lower cost developing countries. Additionally, SpinTrak™ systems were being recommended or supplied by well known machinery or engineering companies building the new plants.

Success and experience in the filament and fiber industries”  to “Aspex Incorporated  introduces a Floor Standing SpinTrak™ Model for Spunbond, Meltblown and Staple fiber Die Inspection

Success and experience in the filament and fiber industries” to “Aspex Incorporated introduces a Floor Standing SpinTrak™ Model for Spunbond, Meltblown and Staple fiber Die Inspection

Having gained success and experience in the filament and fiber industries, Aspex Incorporated developed a series of larger Floor Standing Models to accommodate the large Spinneret dies used in the nonwovens spunbond process.. The first unit was delivered to a major nonwovens producer in the USA in 1995. The SpinTrak™ Floor Standing Model was designed to accommodate both large spunbond dies and the smaller meltblown dies which most customers use on their lines. The SpinTrak™ has been supplied by 3 well known Spunbond machinery manufacturers and/or recommended to their customers. Aspex Incorporated enjoys a very high market share of new projects in this industry segment.