Random Bin Research Articles

Larger is the key word at the latest IMTS show

PurposeThe purpose of this paper is to review the International Manufacturing Technology Show in Chicago with emphasis on innovative robot applications on display.Design/methodology/approachIn‐depth interviews with exhibitors of robots as well as system integrators who apply robots to specific categories of applications.FindingsRobots are becoming smarter with more integrated capabilities such as vision and autonomous part picking from random bin locations. They are becoming more economical, faster and more application specific. Robot system integrators are creating more efficient solutions for customers to consider.Originality/valueThe paper is of value to users who investigated robot solutions in the past and found they did not meet applications requirements and who may need to revisit robotics. Robot builders and system integrators are providing more suitable solutions that can better address application needs in a more cost‐effective manner than ever before.

Braintech releases Random Bin Picking(tm) software

Braintech releases Random Bin Picking(tm) software

A photon-counting time-of-flight ranging technique developed for the avoidance of range ambiguity at gigahertz clock rates

This paper describes a rapid data acquisition photon-counting time-of-flight ranging technique that is designed for the avoidance of range ambiguity, an issue commonly found in high repetition frequency time-off-light systems. The technique transmits a non-periodic pulse train based on the random bin filling of a high frequency time clock. A received pattern is formed from the arrival times of the returning single photons and the correlation between the transmitted and received patterns was used to identify the unique target time-of-flight. The paper describes experiments in laboratory and in free space at over several hundred meters range at clock frequencies of 1GHz. Unambiguous photon-counting range-finding is demonstrated with centimeter accuracy.

Robots meet machine tools at the IMTS party

PurposeTo review the International Manufacturing Technology Show in Chicago with emphasis on innovative robot applications on display.Design/methodology/approachIn‐depth interviews were conducted with exhibitors of robots as well as system integrators who apply robots to specific categories of applications.FindingsRobots are becoming smarter with more integrated capabilities such as vision and autonomous part picking from random bin locations. They are becoming more economical, faster and more application specific. Robot system integrators are creating more efficient solutions for customers to consider.Originality/valueUsers who investigated robot solutions in the past and found they did not meet applications requirements should revisit robotics. Robot builders and system integrators are providing more suitable solutions that can better address application needs in a more cost‐effective manner than ever before.

Wafer bin map recognition using a neural network approach

Although the fabrication of modern integrated circuits uses highly automatic and precisely controlled operations, equipment malfunctions or process drifts are still inevitable owing to the high complexity involved in the hundreds of processing steps. To detect the existence of these problems at the earliest stage, some important analytical tools must be applied. Among them is wafer bin map analysis. When the bin map exhibits specific patterns, it is usually a clue that equipment problems or process variations have occurred. The aim was to develop an intelligent system that could automatically recognize wafer bin map patterns and aid in the diagnosis of failure causes. A neural network architecture named Adaptive Resonance Theory Network 1 was adopted for the purpose. Actual data collected from a semiconductor manufacturing company in Taiwan were used for system verification. Experimental results show that with an adequate parameter, the neural network can successfully recognize and distinguish random and systematic wafer bin map patterns.

Random search in the one-dimensional cutting stock problem

The paper reports on a randomized approach to the one-dimensional cutting stock problem. In a sequential heuristic the next pattern to be processed is chosen according to the best outcome of 200 trials of random bin packing. The quality of the solutions is discussed and the problem of multiple occurrence of equivalent solutions with different patterns is explained by the cross over technique known from genetic algorithms. For one data instance with 27 demand lengths, reported by Haessler (1975), four different optimal solutions are given.