On-road eye movement tracking-glance analysis of dynamic scenes

Results from an on-road study of drivers eye-movements collected using a head-mounted eye-tracker are presented. Glance patterns for normal and distracted driving shall be identified. Problems in data collection and analysis will also be reported. © 2005 Optical Society of America

Models for recycling electronics end-of-life products

Increasing environmental concerns about the disposal of mass produced products have resulted in efforts to take back end-of-life consumer products. Legislation aimed at forcing manufacturers to take back electronics products at the end of their useful lives has either been adopted or is impending in many countries. This, along with shrinking landfill capacity and the reluctance of communities to open new waste sinks underscores the importance of developing methods and models for the management of end-of-life materials and products. This paper reports a study of the reverse channels for recycling of electronics products. The economics of electronics recycling are modeled from the viewpoints of the generators, recyclers, and material processors separately. A variety of mathematical programming models, representative of the many ways in which the recycling industry currently operates, have been proposed along with numerical illustrations. Models integrating disassembly and material recovery decisions are also presented. These models can be used by recyclers and processors for optimizing recycling operations and thus contribute towards the economic sustainability of electronics recycling

Surface roughness monitoring using computer vision

In this paper, a comparative experimental surface roughness measurement method based on the speckle pattern caused by a laser beam on a rough surface is presented. Surfaces with known surface roughness are measured using this method to obtain a calibration curve. This information is used to measure surfaces produced by surface grinding, and the results compared with stylus measurements. The online use of this method for tracking the roughness of a workpiece being processed on a surface grinder and to monitor the condition of the grinding wheel is also reported. Copyright © 1996 Elsevier Science Ltd

Configuring flexible flowlines

Flexible machines are machines equipped to respond to a variety of jobs by the selecting appropriate tools, which are available on the machine, to match the tasks required for each incoming job. Computer Numerical Control machines (CNCs) and robots with multiple grippers are examples of such machines. However, the tool holding or tool magazine capacity usually limits the flexibility of these machines. A sequence of such machines, with directed flows, is a flexible flowline, and these are seen as a possible solution for producing large varieties of similar products. In this paper, problems related to configuring flexible flowlines are considered, with the goal of minimizing the line size without requiring any backtracking while processing parts. Several forms of the problem are considered. Scenarios where single part types are produced on identical, tool-magazine capacity limited machines are examined first. These are expanded to include restrictions on processing capabilities as constraints. Multiple-part type scenarios are then modelled, beginning with procedures addressing open magazines, finally followed by restricted machines, with tool magazine size limitations. A mathematical programming formulation for configuring such a line is detailed, and solutions for test cases presented

Minimizing the expected processing time on a flexible machine with random tool lives

We present a stochastic version of economic tool life models for machines with finite capacity tool magazines and a variable processing speed capability, where the tool life is a random variable. Using renewal theory to express the expected number of tool setups as a function of cutting speed and magazine capacity, we extend previously published deterministic mathematical programming models to the case of minimizing the expected total processing time. A numerical illustration with typical cutting tool data shows the deterministic model underestimates the optimal expected processing time by more than 8% when the coefficient of variation equals 0.3 (typical for carbide tools), and the difference exceeds 15% for single-injury tools having an exponentially distributed economic life (worst case). Copyright © IIE

Using tool life models to minimize processing time on a flexible machine

Economic tool life models are presented for machines with finite capacity tool magazines and variable processing speed capability. Single and multiple part models for minimizing the total throughput time are formulated as nonlinear, integer programs (NLIP). An algorithm is presented for the NLP relaxation and a marginal analysis approach for solving the NLIP is detailed, giving an optimal tool loading policy as well as the processing speeds for each of the part types so as to minimize the makespan. A numerical example illustrates the procedures. © 1997 Taylor & Francis Group, LLC

Robust flowline design for automotive body shops

As the role of automation increases in modern manufacturing enterprises, the need for robust operations of these systems also becomes more acute. Automotive companies employ highly automated assembly lines in their body shops. Robot failures result either in stoppages of the line or require manual backup of operations. Failures impair the systems\u27 productivity as well as product quality. Therefore, the consideration of robot failures in the design stage of an assembly line is important. In this contribution, we propose a robust approach to configure a robotic assembly line such that in the event of a robot failure working robots take over the tasks of the failed robots. The throughput loss in these backup situations depends on the level of redundancy designed into the system. An integer optimization approach is used for the system design, and the performance evaluation is done by using a simulation model. A numerical analysis compares the performance of this approach with existing robotic assembly line balancing methods, establishing the advantage of using this approach for flowline designs for automotive body shops

Tool planning for a lights-out machining system

The goal of most advanced manufacturing systems is to continuously improve uptime while reducing the amount of direct supervision required for operations. However, when there is randomness in system components, this improvement can be difficult to attain. For instance, when automating metal cutting operations, the randomness of tool life requires ensuring that there are sufficient cutting tools available on the machines to meet unsupervised production requirements, and variations in tool life can make planning challenging. This paper focuses on the problem of selecting the cutting speeds for processing a set of part types by an unsupervised metal cutting flexible machine in such a situation. The machine is set up to operate unsupervised for a specific known duration. The tool magazine of this machine is preloaded with tools commensurate with the processing requirements. The lifetime of each tool is random, with the coefficient of variation assumed to be constant, and a system for online monitoring of the tool condition is available. The objective in this situation is to ensure that disruption is minimized-in other words, that the machine operates while ensuring some minimum probability of completion. This is referred to as the required service level. This paper presents models for determining the optimal magazine loading and cutting speeds that will meet a required service level. Solutions obtained using commonly available nonlinear programming solvers are included for illustration, and differences when the tool life distributions are either normal or Erlang are contrasted. © 2008 The Society of Manufacturing Engineers

A hierarchical model for control of flexible manufacturing systems

Flexible Manufacturing Systems (FMSs) are usually composed of general purpose machines with automatic tool changing capability and integrated material handling. The complexity of FMSs requires sophisticated control. In this paper we present a four-level control hierarchy and outline computationally feasible control algorithms for each level. The top level is concerned with the choice of part types and volumes to be assigned to the FMS over the next several months. The second level plans daily or shift production. Production levels are set and tools are allocated to machines so as to minimize holding and shortage costs. Various FMS environments are presented. The third level determines process routes for each part type in order to minimize material handling. Additional tools are loaded on machines when possible to maximize alternate routeing. Routes are then assigned to parts to minimize workload assignment, and these are used by level four for actual routeing, sequencing and material handling path control. The level three model is formulated as a linear program, and heuristics are used for level four. An example is provided to illustrate the completeness of the decision hierarchy and the relationships between levels. © 1994 Operational Research Society Ltd

Simulation of random tool lives in metal cutting on a flexible machine

This paper describes some numerical experiments related to a tool management model for a flexible machine equipped with a tool magazine, variable cutting speed, and sensors to monitor tool wear, when tool life due to flank wear is stochastic. A computer simulation was performed where decisions about tool loading and cutting speed were based on a deterministic mathematical programming model in which tool setup times are added up to total processing time whenever a tool is required but absent from the tool magazine. Two types of sensor systems are presented: offline sensors and online sensors. It is assumed that the sensor only gives information about whether or not the tool is in good condition to continue processing. The simulation aims at answering six questions: (1) which statistical distributions should be used to simulate the life of a cutting tool? (2) How effective is a deterministic model if tool lives are stochastic? (3) Is the use of a stochastic model still justified when tool life variability decreases (i.e. tool quality and reliability increases)? (4) Does adjusting cutting speed while processing a given part type help improve productivity? (5) If the machine must be stopped to inspect tool conditions, what are the best parameters to use, i.e. inter-inspection times and threshold? (6) How effective are the two sensor systems