This paper presents the importance of Neutrosophy theory in order to find a method that could solve the uncertainties arising on process analysis. The aim of this pilot study is to find a procedure to diminish the uncertainties induced by manufacturing, maintenance, logistics, design, human resources. The study is intended to identify a method to answer uncertainties solving in order to support manufacturing managers, NLP specialists, artificial intelligence researchers and businessman in general.
This study is the first step of a research that points out the solving of uncertainties in
process analysis. The research is based on Neutrosophy Theory [
We believe that such as method would be useful for manufacturing managers, NLP specialists, artificial intelligence researchers, other scientists interested to find a method of uncertainties solving.
The paper is structured as follows: after a brief introduction, section 2 describes the background related to neutrosophy applicability; section 3 discusses the annotations regarding neutrosophy theory described in transposed in algebric structures, section 4 presents some indicators of process stability, section 5 introduces a sample of neutrosophic interpretation on manufacturing process, and finally section 6 depicts some conclusions and directions for the future.
According to the neutrosophy theory, the neutral (uncertainty) instances can be analysed and accordingly, reduced.
There are some spectacular results of applying netrosophy in practical application
such as artificial intelligence [
The real-time adaptive networked control of rescue robots is another project that used
neutrosophic logic to control the robot movement in a surface with uncertainties for it [
Starting from this point, we are confidence that neutrosophy theory can help to analysis, evaluate and make the right decision in the process analysis taking into account all sources that can generate uncertainty, from human being (not appropriate skill), logistics concept, lack of information, programming automation process according requirements, etc.
The specialty literature reveals that Zadeh introduced in 1965 the degree of membership/truth (t), the rest would be (1-t) equal to f/ false, their sum being 1, so it was defined the fuzzy set. tainty.
Logistics represents the department that supply the chain just in time (JIT) and just in place (JIP).
In case of delivering wrong parts (another code), in the wrong place, parts with defects, it is obvious that the operator induce at his turn confusion/uncertainty. In this situation it is a great concern who, what, how to intervene to diminish the confusions/uncertainties.
In automation systems equipment operate in cycles of time defined as sum of states: cycling time (machine is in cycling/operating), starved time (machine finished cycle tine but previous station cannot deliver part), blocked time (machine finished cycle time but cannot deliver the part to the next station because it is in cycle), waiting aux part time (machine process the part in addition with an auxiliary part that is not present), waiting attention time (machine is in fault and wait for operator to make decision), repair in progress (machine is in repairing), emergency stop (general stop for whole station), bypass (station is not operating, skip), tool change (machine needs to change tool), setup (time for parameters changes), break time (break for operators lunch time), no communications (network communication error) (see Fig. 2).
These statuses are defined in PLC (programmable logic controller) for process analysis and evaluation. Related on these statuses are proceeded also the maintenance indicators.
Availability is OEE Metric that represents the percentage of scheduled time that the operation is available to operate. Often is referred as Uptime. Performance is OEE Metric that represents the speed at which the Work Center runs as a percentage of its designed speed. Quality is OEE Metric that represents the Good Units produced as a percentage of the Total Units Started. Definition of a failure - failure is declared when the equipment does not meet its desired objectives. Therefore, we can consider any equipment that cannot meet minimum performance or availability requirements to be “failed”. Similarly, a return to normal operations signals the end of downtime or system failure, is considered to be “non-failed”.
Mean Time to Repair (MTTR) is the mean time of the facility in the status of “Repair”, and it is calculated as:
MTTR = Repair in Progress Time (min)/ Repair in Progress Occurrences.
Mean Time Between Failures (MTBF) shows the amount of time the machine spends in production time as a percentage of all the states except Break and No Communications.
MTBF = (Time in Auto / Total Time) x 100, where: Time in auto = Cycling Time + Blocked Time + Starved Time + Waiting Auxiliary Time +
Bypass Time,
Total Time = Cycling Time + Blocked Time + Starved Time + Waiting Auxiliary Time + Bypass Time + Tool Change Time + Waiting Attention Time + Shutdown Time + Emergency Stop Time + Set Up Time.
Failure Metrics
Timebetweenfailures Timeto repair
Timeto failure
Process Systemfailure
Resume ProcessOperations
Systemfailure
A process is stable when there is no variability in the system, when the outcome is by
design, as expected [
The systems variation we are talking about in this study refers to uncertainty, confusion that can occur in various situations in the manufacturing process that, can lead to another product than expected one, or a scrap.
In a process, practically can occur such situations when we are put in a position of uncertainty that leads the process variation to instability, to errors.
Below are presented two methods of analysis, evaluation and correction of the process: the Ishikawa diagrams and Pareto chart.
Ishikawa diagrams (also called fishbone diagrams, cause-and-effect diagrams) are
causal diagrams created by [
Common uses of the Ishikawa diagram (see Fig.4) are product design and quality
defect prevention, to identify potential factors causing an overall effect. Each cause or reason for
imperfection is a source of process variation. Causes are usually grouped into major categories
to identify the sources of variation such as: people, methods, machines, materials,
measurements, environment [
Related to these categories can be extended to detailed items like anyone involved
with the process, how the process is performed and the specific requirements for doing it,
policies, procedures, rules, regulations and laws, any equipment, computers, tools, etc. required to
accomplish the job, raw materials, parts, pens, paper, etc. used to produce the final product,
data generated from the process that are used to evaluate its quality, the conditions, such as
location, time, temperature, and culture in which the process operates [
For a manufacturing process we identify some sources that influence effectiveness indicators. Using Pareto charts it was described the process (see Fig.5.)
30
In this example, there are few issues that appear in process analysis such as procedures errors, operator errors, bad parts, missing parts, equipment faults, etc. According to Pareto principle, examining “operator errors” we can make the decision that reducing this cause of errors, the parameters of the system can be improved. Refining the operator errors issue by IT application, automation system, operators training, it results reducing human decision on process. 100 Neutrosophic interpretation according to issues T I F 90 80
IT 70 Tralaincikng Autolmowation Application Leorgriosrtsic 60
poor 50 20 25 35 10 40 80 75 50 70 30 50 40 28 90 20 10 0 80 50 20 75 40 25 50
During the refining process procedure, we observed that operator errors issue, generated also the decrease of all others errors of the manufacturing process (see Fig.8.)
Procedures er ors Missing parts Bad parts Equipment fault Operators er ors Others Total
IT Logistic Training Automation Application errors 60 90 85 10 30 10 5 20 25 5 9 20
T Training 60 Automation 90 IT Application 85 Logistic errors 10 6. Conclusions and Future Work
We presented a way of correcting the uncertainties arising in process analysis applying neutrosophy theory.
This result can drive us to use the neutrosophy theory for solving the uncertainty, extended in IT applications, logistics, and human resources.
In the future work we will be oriented to find an algorithm to achieve the objectives to improve the percentage of stable statuses, to reduce the neutrality/uncertainty.