Assessment of a Novel Position Verification Sensor to Identify and Isolate Robot Workcell Health Degradation

Manufacturing processes have become increasingly sophisticated leading to greater usage of robotics. Sustaining successful manufacturing robotic operations requires a strategic maintenance program. Without careful planning, maintenance can be very costly. To reduce maintenance costs, manufacturers are exploring how they can assess the health of their robot workcell operations to enhance their maintenance strategies. Effective health assessment relies upon capturing appropriate data and generating intelligence from the workcell. Multiple data streams relevant to a robot workcell may be available including robot controller data, a supervisory programmable logic controller data, maintenance logs, process and part quality data, and equipment and process fault and failure data. These data streams can be extremely informative, yet the massive volume and complexity of this data can be overwhelming, confusing, and sometimes paralyzing. Researchers at the National Institute of Standards and Technology have developed a test method and companion sensor to assess the health of robot workcells which will yield an additional and unique data stream. The intent is that this data stream can either serve as a surrogate for larger data volumes to reduce the data collection and analysis burden on the manufacturer, or add more intelligence to assessing robot workcell health. This article presents the most recent effort focused on verifying the companion sensor. Results of the verification test process are discussed along with preliminary results of the sensor's performance during verification testing. Lessons learned indicate that the test process can be an effective means of quantifying the sensor's measurement capability particularly after test process anomalies are addressed in future efforts.

Measurement and Evaluation for Prognostics and Health Management (PHM) for Manufacturing Operations - Summary of an Interactive Workshop Highlighting PHM Trends

Personnel from the National Institute of Standards and Technology (NIST) organized and led a Measurement and Evaluation for Prognostics and Health Management for Manufacturing Operations (ME4PHM) workshop at the 2019 Annual Conference of the Prognostics and Health Management Society held on September 23rd, 2019 in Scottsdale, Arizona. This event featured panel presentations and discussions from industry, government, and academic participants who are focused in advancing monitoring, diagnostic, and prognostic (collectively known as prognostic and health management (PHM)) capabilities within manufacturing operations. The participants represented a diverse cross-section of technology developers, integrators, end-users/manufacturers (from small to large), and researchers. These contributors discussed 1) what works well, 2) common challenges that need to be addressed, 3) where the community's priorities should be focused, and 4) how PHM technological adoption can be sped in a cost-effective manner. This report summarizes the workshop and offers lessons learned regarding the current state of PHM. Based upon the discussions, recommended next steps to advance this technological domain are also presented.

A review of diagnostic and prognostic capabilities and best practices for manufacturing

Prognostics and health management (PHM) technologies reduce time and costs for maintenance of products or processes through efficient and cost-effective diagnostic and prognostic activities. PHM systems use real-time and historical state information of subsystems and components to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. However, PHM is still an emerging field, and much of the published work has been either too exploratory or too limited in scope. Future smart manufacturing systems will require PHM capabilities that overcome current challenges, while meeting future needs based on best practices, for implementation of diagnostics and prognostics. This paper reviews the challenges, needs, methods, and best practices for PHM within manufacturing systems. This includes PHM system development of numerous areas highlighted by diagnostics, prognostics, dependability analysis, data management, and business. Based on current capabilities, PHM systems are shown to benefit from open-system architectures, cost-benefit analyses, method verification and validation, and standards.

Quick health assessment for industrial robot health degradation and the supporting advanced sensing development

Robotic technologies are becoming more integrated with complex manufacturing environments. The addition of greater complexity leads to more sources of faults and failures that impact a robot system's reliability. Industrial robot health degradation needs to be assessed and monitored to minimize unexpected shutdowns, improve maintenance techniques, and optimize control strategies. A quick health assessment methodology is developed at the U.S. National Institute of Standards and Technology (NIST) to quickly assess a robot's tool center position and orientation accuracy degradation. An advanced sensing development approach to support the quick health assessment methodology is also presented in this paper. The advanced sensing development approach includes a seven-dimensional (7-D) measurement instrument (time, X, Y, Z, roll, pitch, and yaw) and a smart target to facilitate the quick measurement of a robot's tool center accuracy.

Developing a hierarchical decomposition methodology to increase manufacturing process and equipment health awareness

Manufacturing systems are becoming increasingly complex as more advanced and emerging technologies are integrated into the factory floor to yield new processes or increase the efficiency of existing processes. As greater complexity is formed across the factory, new relationships are often generated that can lead to advanced capabilities, yet produce unforeseen faults and failures. Industrial robot arm work cells within the manufacturing environment present increasing complexity, emergent technologies, new relationships, and unpredicted faults/failures. To maintain required levels of productivity, process quality, and asset availability, manufacturers must reconcile this complexity to understand how the health degradation of constituent physical elements and functional tasks impact one another through the monitoring of critical informative measures and metrics. This article presents the initial efforts in developing a novel hierarchical decomposition methodology. The innovation in this method is that it provides the manufacturer with sufficient discretion to physically deconstruct their system and functionally decompose their process to user-defined levels based upon desired monitoring, maintenance, and control levels. This enables the manufacturer to specify relationships within and across the physical, functional, and information domains to identify impactful health degradations without having to know all possible failure modes. The hierarchical decomposition methodology will advance the state of the art in terms of improving machine health by highlighting how health degradations propagate through the relationship network prior to a piece of equipment compromising the productivity or quality of a process. The first two steps of the methodology, physical decomposition and functional decomposition, are defined in detail and applied to a multi-robot work cell use case.

Hierarchical Modeling of a Manufacturing Work Cell to Promote Contextualized PHM Information Across Multiple Levels

With the ever-increasing demand for reconfigurability and modularity in manufacturing, industrial work cells are increasingly integrating newer and more diverse technologies to not only support the production of a wider range of parts, but also ease the repair or replacement of faulty systems and components. Complex relationships between different elements of a work cell originate from the integration of multiple layers of hardware and software needed to successfully execute the complicated manufacturing processes. Much work within the science of PHM (prognostics and health management) has been dedicated towards the management of some of this complexity via monitoring, diagnostic, and prognostic technologies. The strategic application of PHM technologies has been shown to effectively reduce equipment/process downtime and lower maintenance costs. Part of the challenge of PHM, particularly for manufacturers, is to know exactly where, and how to apply PHM within their work cell operations to gain the maximum actionable information. This problem is further compounded for small to medium-sized manufacturers (SMMs) who are typically limited in their resources and investment capital. Effectively designing and implementing PHM requires a fundamental understanding of the overall work cell and its constituent physical components and sub-components. Likewise, understanding the relationships between these physical elements and how these elements relate to one another is critical to determining how the degradation of one element will impact the degradation of another. The National Institute of Standards and Technology (NIST) is researching various PHM technologies that aim to enhance decision-making at the factory floor to promote smarter maintenance and control strategies. Part of NIST's research focuses on the decomposition of a work cell into a hierarchical structure to understand the physical and functional relationships among the overall system's critical elements. This physical and functional decomposition is a necessity to promote a meaningful rollup of diagnostic and prognostic information from the lower levels to the higher levels of the hierarchy. The hierarchy seeks to encapsulate how the overall system, and its subsequent components, will behave when an element within the system is compromised or begins to fail. Neighboring components and sub-components could be subject to the 'domino effect' or the 'ripple effect', making diagnosing the root cause of a cascade alarms difficult without some reflective model of the system. This paper presents NIST's efforts to develop a hierarchical decomposition methodology that will support PHM design and implementation within a complex work cell.

Present Status and Future Growth of Advanced Maintenance Technology and Strategy in US Manufacturing

The goals of this paper are to 1) examine the current practices of diagnostics, prognostics, and maintenance employed by United States (U.S.) manufacturers to achieve productivity and quality targets and 2) to understand the present level of maintenance technologies and strategies that are being incorporated into these practices. A study is performed to contrast the impact of various industry-specific factors on the effectiveness and profitability of the implementation of prognostics and health management technologies, and maintenance strategies using both surveys and case studies on a sample of U.S. manufacturing firms ranging from small to mid-sized enterprises (SMEs) to large-sized manufacturing enterprises in various industries. The results obtained provide important insights on the different impacts of specific factors on the successful adoption of these technologies between SMEs and large manufacturing enterprises. The varying degrees of success with respect to current maintenance programs highlight the opportunity for larger manufacturers to improve maintenance practices and consider the use of advanced prognostics and health management (PHM) technology. This paper also provides the existing gaps, barriers, future trends, and roadmaps for manufacturing PHM technology and maintenance strategy.

Advancing Measurement Science to Assess Monitoring, Diagnostics, and Prognostics for Manufacturing Robotics

Unexpected equipment downtime is a 'pain point' for manufacturers, especially in that this event usually translates to financial losses. To minimize this pain point, manufacturers are developing new health monitoring, diagnostic, prognostic, and maintenance (collectively known as prognostics and health management (PHM)) techniques to advance the state-of-the-art in their maintenance strategies. The manufacturing community has a wide-range of needs with respect to the advancement and integration of PHM technologies to enhance manufacturing robotic system capabilities. Numerous researchers, including personnel from the National Institute of Standards and Technology (NIST), have identified a broad landscape of barriers and challenges to advancing PHM technologies. One such challenge is the verification and validation of PHM technology through the development of performance metrics, test methods, reference datasets, and supporting tools. Besides documenting and presenting the research landscape, NIST personnel are actively researching PHM for robotics to promote the development of innovative sensing technology and prognostic decision algorithms and to produce a positional accuracy test method that emphasizes the identification of static and dynamic positional accuracy. The test method development will provide manufacturers with a methodology that will allow them to quickly assess the positional health of their robot systems along with supporting the verification and validation of PHM techniques for the robot system.

Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems

The Adaptive Multi-scale Prognostics and Health Management (AM-PHM) is a methodology designed to enable PHM in smart manufacturing systems. In application, PHM information is not yet fully utilized in higher-level decision-making in manufacturing systems. AM-PHM leverages and integrates lower-level PHM information such as from a machine or component with hierarchical relationships across the component, machine, work cell, and assembly line levels in a manufacturing system. The AM-PHM methodology enables the creation of actionable prognostic and diagnostic intelligence up and down the manufacturing process hierarchy. Decisions are then made with the knowledge of the current and projected health state of the system at decision points along the nodes of the hierarchical structure. To overcome the issue of exponential explosion of complexity associated with describing a large manufacturing system, the AM-PHM methodology takes a hierarchical Markov Decision Process (MDP) approach into describing the system and solving for an optimized policy. A description of the AM-PHM methodology is followed by a simulated industry-inspired example to demonstrate the effectiveness of AM-PHM.

The present status and future growth of maintenance in US manufacturing: results from a pilot survey

A research study was conducted (1) to examine the practices employed by US manufacturers to achieve productivity goals and (2) to understand what level of intelligent maintenance technologies and strategies are being incorporated into these practices. This study found that the effectiveness and choice of maintenance strategy were strongly correlated to the size of the manufacturing enterprise; there were large differences in adoption of advanced maintenance practices and diagnostics and prognostics technologies between small and medium-sized enterprises (SMEs). Despite their greater adoption of maintenance practices and technologies, large manufacturing organizations have had only modest success with respect to diagnostics and prognostics and preventive maintenance projects. The varying degrees of success with respect to preventative maintenance programs highlight the opportunity for larger manufacturers to improve their maintenance practices and use of advanced prognostics and health management (PHM) technology. The future outlook for manufacturing PHM technology among the manufacturing organizations considered in this study was overwhelmingly positive; many manufacturing organizations have current and planned projects in this area. Given the current modest state of implementation and positive outlook for this technology, gaps, future trends, and roadmaps for manufacturing PHM and maintenance strategy are presented.

Adaptive Multi-scale PHM for Robotic Assembly Processes

Adaptive multiscale prognostics and health management (AM-PHM) is a methodology designed to support PHM in smart manufacturing systems. As a rule, PHM information is not used in high-level decision-making in manufacturing systems. AM-PHM leverages and integrates component-level PHM information with hierarchical relationships across the component, machine, work cell, and production line levels in a manufacturing system. The AM-PHM methodology enables the creation of actionable prognostic and diagnostic intelligence up and down the manufacturing process hierarchy. Decisions are made with the knowledge of the current and projected health state of the system at decision points along the nodes of the hierarchical structure. A description of the AM-PHM methodology with a simulated canonical robotic assembly process is presented.

A Sensor-Based Method for Diagnostics of Machine Tool Linear Axes

A linear axis is a vital subsystem of machine tools, which are vital systems within many manufacturing operations. When installed and operating within a manufacturing facility, a machine tool needs to stay in good condition for parts production. All machine tools degrade during operations, yet knowledge of that degradation is illusive; specifically, accurately detecting degradation of linear axes is a manual and time-consuming process. Thus, manufacturers need automated and efficient methods to diagnose the condition of their machine tool linear axes without disruptions to production. The Prognostics and Health Management for Smart Manufacturing Systems (PHM4SMS) project at the National Institute of Standards and Technology (NIST) developed a sensor-based method to quickly estimate the performance degradation of linear axes. The multi-sensor-based method uses data collected from a 'sensor box' to identify changes in linear and angular errors due to axis degradation; the sensor box contains inclinometers, accelerometers, and rate gyroscopes to capture this data. The sensors are expected to be cost effective with respect to savings in production losses and scrapped parts for a machine tool. Numerical simulations, based on sensor bandwidth and noise specifications, show that changes in straightness and angular errors could be known with acceptable test uncertainty ratios. If a sensor box resides on a machine tool and data is collected periodically, then the degradation of the linear axes can be determined and used for diagnostics and prognostics to help optimize maintenance, production schedules, and ultimately part quality.

Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems: Key Findings from a Roadmapping Workshop

The National Institute of Standards and Technology (NIST) hosted the Roadmapping Workshop - Measurement Science for Prognostics and Health Management for Smart Manufacturing Systems (PHM4SMS) in Fall 2014 to discuss the needs and priorities of stakeholders in the PHM4SMS technology area. The workshop brought together over 70 members of the PHM community. The attendees included representatives from small, medium, and large manufacturers; technology developers and integrators; academic researchers; government organizations; trade associations; and standards bodies. The attendees discussed the current and anticipated measurement science challenges to advance PHM methods and techniques for smart manufacturing systems; the associated research and development needed to implement condition monitoring, diagnostic, and prognostic technologies within manufacturing environments; and the priorities to meet the needs of PHM in manufacturing. This paper will summarize the key findings of this workshop, and present some of the critical measurement science challenges and corresponding roadmaps, i.e., suggested courses of action, to advance PHM for manufacturing. Milestones and targeted capabilities will be presented for each roadmap across three areas: PHM Manufacturing Process Techniques; PHM Performance Assessment; and PHM Infrastructure - Hardware, Software, and Integration. An analysis of these roadmaps and crosscutting themes seen across the breakout sessions is also discussed.

Creatine supplementation does not impair the thermoregulatory response during a bout of exercise in the heat

AIM

The aim of this work was to examine the effects of creatine (Cr) supplementation on resting body water volumes and on core temperature and sweat loss during a bout of exercise in a warm environment.

METHODS

Twenty-four aerobically trained male subjects (age 22.93+/-3.01 years, height 179.52+/-7.28 cm, mass 82.06+/-14.32 kg) volunteered to participate in this study. Each subject was assessed for resting body water volumes and for body mass (BM), heart rate (HR), blood pressure (BP), and core temperature immediately before and following a 60-min bout of exercise in a warm environment. Core temperature, HR, and BP were also recorded at 10-min intervals during exercise. Subjects were then randomly assigned to either a Cr or placebo (P) group. Each subject returned following a 5-day supplementation period and was reassessed using identical testing procedures. BM was measured using a standard electronic scale. Body water volumes were assessed using a multi-frequency BIS (Xitron Technologies, San Diego, CA, USA). Core body temperature was measured using a CorTemp Disposable Temperature Sensor and a CT2000 Miniaturized Ambulatory Recorder (HTI Technologies, Inc., Palmetto, FL, USA).

RESULTS

The Cr group experienced a significant increase in all body water volumes. No changes were observed in the P group. No changes in core temperature or sweat loss were observed in either group following supplementation.

CONCLUSIONS

Cr loading did not impair the thermoregulatory response during a bout of exercise in the heat.

Bringing baby home: a comprehensive case management program for obstetrical services

Originally presented to the Society of Home Health Care Management of the American College of Physician Executives at its November 16, 1993, meeting in Tucson, Ariz., the program described in this article was the winner of the College's 1994 Innovations Award in Medical Quality Management, sponsored by Merck Sharp & Dohme. The program shows the potential of case management for both improvement in the quality of care and containment of costs for a managed care population with a substantial Medicaid segment. This article is part of a continuing series on innovative programs in home health care.

Psychology of computer use: XXVIII: Effect of computer use on depth perception

30 computer users were tested first after work and then second both before and after periods of computer use. Subjects' depth perception ability was unaffected.

Intracellular Ca2+ and protein kinase C interact to regulate alpha 1-adrenergic- and bradykinin receptor-stimulated phospholipase A2 activation in Madin-Darby canine kidney cells

Alpha 1-Adrenergic receptors and bradykinin receptors are two distinct membrane receptors that stimulate phospholipid breakdown and arachidonic acid and arachidonic acid metabolite release. In the current studies, we have examined several mechanisms to assess their possible contribution to arachidonic acid release in the Madin-Darby canine kidney cell line by agonist stimulation of these receptors: 1) activation of phospholipase A2 (PLA2); 2) sequential activation of phospholipase C, diacylglycerol lipase, and monoacylglycerol lipase; and 3) inhibition of the sequential action of fatty acyl-CoA synthetase and lysophosphatide acyltransferase. Experiments were conducted to measure the stimulation of lysophospholipid production by epinephrine and bradykinin, the rate of incorporation of [3H]arachidonic acid into stimulated and unstimulated cells, and the effect on [3H]arachidonic acid release of treating cells with exogenous phospholipase C. The data indicate that stimulation of PLA2 activity is regulated by alpha 1-adrenergic and bradykinin receptors and that this stimulation is mediated, at least in part, by the activation of protein kinase C. We find that the role of diacylglycerol in arachidonic acid release is as an activator of protein kinase C and not as a substrate for a lipase. Moreover, the hormonal agonists do not appear to inhibit fatty acid reacylation. Experiments using the Ca2(+)-sensitive dye fura-2 and the intracellular Ca2+ chelator 1,2-bis(2-aminophenoxy)ethane-N,N,N',N'-tetraacetic acid suggest that bradykinin activates PLA2 by a transient elevation of intracellular Ca2+. This action appears to be less important for activation of PLA2 by epinephrine. Taken together, these data are consistent with the following conclusions. 1) Hormone-stimulated arachidonic acid release in Madin-Darby canine kidney-D1 cells occurs as a consequence of PLA2 activation. 2) The ability of an agonist both to mobilize Ca2+ and to activate protein kinase C contributes to its efficacy as a stimulator of PLA2-mediated arachidonic acid release.

Differential activation of protein kinase C alpha is associated with arachidonate release in Madin-Darby canine kidney cells

The heterogeneity of the protein kinase C (PKC) gene family strongly suggests that different isoforms may have distinct functions in mediating signal transduction. However, there is very little direct evidence for this. PKC has been implicated in arachidonate (AA) release in many cell types. We sought to investigate whether bradykinin- and phorbol ester-stimulated AA release in Madin-Darby canine kidney (MDCK) cells was correlated with differential activation of PKC isoforms. Using phorbol esters to (i) activate the enzyme and (ii) to down-regulate it, we report that differential activation (translocation) of PKC alpha is associated with AA release in MDCK cells and that specific down-regulation of PKC alpha is associated with a loss of AA release in response to stimulation with dioctanoylglycerol and phorbol ester. We also demonstrate that bradykinin-stimulated AA release was associated with differential activation of PKC alpha and was inhibited in PKC alpha down-regulated cells. Thus, we conclude that the PKC alpha isoform is likely to be responsible for mediating AA release in these cells.

Defining the role of protein kinase C in epinephrine- and bradykinin-stimulated arachidonic acid metabolism in Madin-Darby canine kidney cells

Madin-Darby canine kidney cells (MDCK) are known to release free arachidonic acid and arachidonic acid metabolites (AA) in response to tumor-promoting phorbol esters, such as tetradecanoyl phorbol-13-acetate, and to agonists active at alpha 1-adrenergic and bradykinin B2 receptors. These experiments were conducted to define the role of Ca2+/phospholipid-dependent protein kinase (protein kinase C) activation in the stimulation of AA release, in the clonal isolate cell line MDCK-D1, by use of three inhibitors of protein kinase C, sphingosine, 1-(5-isoquinolinesulfonyl)-2-methylpiperazine (H-7), and staurosporine. We found that alpha 1-adrenergic- and bradykinin-stimulated [3H]AA release can be distinguished by differential dependence on protein kinase C; epinephrine-stimulated release was more dependent on protein kinase C activation than was bradykinin-stimulated release. The inhibition of bradykinin-stimulated AA release by sphingosine (20.2 +/- 6.1%) was substantially less than the inhibition observed for tetradecanoyl phorbol-13-acetate- (67.2 +/- 5.5%) and epinephrine-stimulated release (50.2 +/- 9.2%). These findings were confirmed by results using H-7 and staurosporine. The relative independence of bradykinin-stimulated AA release of protein kinase C was also demonstrated by the inability of phorbol ester-induced down-regulation of protein kinase C to eliminate bradykinin-stimulated AA release. The inhibition of alpha 1-adrenergic receptor-mediated AA release by sphingosine, H-7, and staurosporine was not due to a change in receptor number or affinity. Analysis of the products comprising [3H]AA release indicated that treatment with sphingosine did not change the composition of the released AA (34-48% prostaglandin E2, 17-27% free arachidonic acid, and 25-51% unidentified metabolites). These results indicate that two different types of hormone receptors in the same cell type can promote AA release by mechanisms that differ in their dependence on protein kinase C. The protein kinase C-dependent mechanism may represent protein kinase C-mediated activation of phospholipase A2.

Injectable cross-linked collagen with improved flow properties

Aqueous suspensions of glutaraldehyde cross-linked fibrillar collagen and non-cross-linked fibrillar collagen were examined by rheometry, particle size analysis, and microscopic techniques. Although cross-linked collagen suspensions were similar to non-cross-linked suspensions by microscopic and size analyses, they differed in rheometric properties. Concentric cylinder Couette flow, shear creep, uniaxial creep, and porous bed flow all revealed that cross-linked collagen was more resistant to deformation and flow than non-cross-linked collagen. These results were in agreement with in vivo dermal implantation studies, both in pig and human; i.e., compared to non-cross-linked collagen, the cross-linked formulation was more difficult to inject into tissue and did not spread uniformly, sometimes giving rise to palpable lumps or large masses evident in histological sections. When hyaluronic acid was blended with cross-linked collagen to achieve a final hyaluronate concentration of 5 mg/mL, there was a significant improvement in ease of injection into tissue. Rheometry on blends of hyaluronate and cross-linked collagen demonstrated that the blend required lower forces to achieve deformation and flow, compared to cross-linked collagen alone. Particle size analysis on the blend showed a reduction in fiber aggregate dimensions, compared to cross-linked collagen alone.

AIDS and confidentiality: legal concept and its application in psychotherapy

Persons with positive HIV appear to have the same right to confidentiality as other medical psychiatric patients. The ethical and legal duties of practitioners who learn that their HIV positive patients are endangering others is discussed. The essential policies of the CDC, AMA, and APA are reviewed along with the current legal situation. One conclusion reached is that applying the Tarasoff doctrine to warn/protect a third party, if that party may already be infected, is useful only when the third party is moral and sensible enough to cease behavior that would spread the disease to others.

Evidence that the colored shadow effect is retinal

Polarized lenses were employed to present the images of the colored shadows effect separately to each eye. The phenomenon was perceived only when both images were viewed on the same retina, leading to the conclusion that the effect is entirely retinal in origin and does not involve the central nervous system.

Evidence that line illusions originate in the central nervous system

Polarized lenses were employed to present the components of four line illusions separately to each eye. The phenomena were perceived even when both images were disparately presented, leading to the conclusion that the effect originates in the central nervous system and does not require the retina.

Auditory sensitivity in the bullhead catfish (Ictalurus nebulosis)

The auditory sensitivity of 4 specimens of the bullhead catfish (Ictalurus nebulosis) was determined by shock-avoidance training in an aquatic shuttle box. The range of hearing extended from 100 to 4000 cycles per second, with the maximum sensitivity around 600 to 700 cycles per second.

Inter-situational transfer in the ant Formica schaufussi as tested in a two-phase single choice-point maze

Abstract

Workers of the ant Formica schaufussi did not transfer a specific choice-point response of right- or left-turning (learned on entrance runs in which an opposed-turns alley sequence led to the choice-point) to an equivalent free-turns situation traversed on returns from the food-box to the nest. Variable behavior, however, was evident in both phases of the maze run after the choice-point alternatives had been reversed experimentally. This behavior is attributed to the general excitatory effects of the situational change. The outcome of this study supports previous results for these ants in complex mazes and in single choice-point U-approach mazes. The conclusion is confirmed that choice-point learning in these ants is bound to the situation in which it is established, evidently through a specificity of response to the sensory aspects and the organic conditions peculiar to action in that situation.