1. Introduction
2. Past Innovations
3. Technical Description
4. Design, Time, Cost, & Materials 
5. Works Cited

INTRODUCTION

Modern machinery along with technology has provided a great opportunity for society to gain the most effective results with minimum effort. These machines have elevated the simple innovations to ensure they work in their best form, without having exerting people and by saving time. Example of such machinery is scissors. The scissor is a simple machine that has been popular since the 16th century (Britannica). Regular scissors consist of two blades that meet at the handle. It can be used to cut through papers, clothes, food, and other light products. The uses might be undermined however, school students use them for their class projects, chefs use them to cut food in kitchen, and larger end companies also use scissors in their factories to make clothing and fashion items. The large variety of uses in our daily lives make scissors one of the most practical tools today.  

Despite the high demand for scissors in our daily lives, they still require a lot of time and energy that can prove to be tiring, especially if used repeatedly. Although there are significant innovations seen in other machines around us, the scissor and its design have remained relatively the same. Robotic vacuums, such as the Roomba, are an example of significant innovations among practical machines. Likewise, to amend the current versions of the scissor, our innovation, the SciBot, is an automatic scissor that limits human interaction little to no energy spent using it. It consists of two parts: the actual hardware that performs the task of cutting and an app that consists of the software that assigns the task. With modern scissors including laser and electric scissors, the user still has to manually carry out the task while the machine aids in accuracy. The SciBot targets this action and performs the task itself. Much like a Roomba, the SciBot moves by itself to achieve the task at hand, the material that needs to be cut, as it receives information from the app about the material and the path it is designated to follow. 

Since scissors are a common practical tool, they are used in a variety of environments. Clothing factories are a specific example of where the SciBot would be most beneficial. Today, one of the most profitable parts of the fashion industry is fast fashion. The idea behind fast fashion is to provide the most trending styles to consumers as fast as possible at affordable prices (Good On You). This strategy requires extensive hours from factory workers to complete as many garments as possible within a single day, leading workers to work excessively past their limits as found in investigations in foreign factories (Business Insider). Though it is a matter of human rights and requires legal action for significant change, implementing devices such as the SciBot would effectively change workers’ routine. Instead of wasting so much energy manually cutting fabrics and other materials, they would instead use the software to inform the device of how to cut and the device would perform the task, saving energy and time. 

PAST INNOVATIONS

There have been two recent innovations in assisted scissors that are similar to what we are trying to implement but have some weaknesses. For example, the company Nueby invented the Lazer Scissors, which have a laser-guided sensor on top to guide you and ensure you always cut a straight line. However, this design makes a person still do manual work, which makes it time consuming as well as an inefficient use of resources. Another design is from SliceProducts, which invented Rotary Scissors, a bladeless tool that reduces the risk of repetitive strain injuries.  

However, this design does not guarantee that you cut straight, and it also is an inefficient use of resources as you still need to manually cut. The SciBot allows you to draw out the design you want on an app and then it automatically cuts that design out. This saves you time and money as the design is perfectly shaped. Additionally, you do not have to monitor the scissor because it automatically stops cutting after the design has been finished. Overall, the SciBot is significantly better than both the Lazer Scissors and Rotary Scissors because of the software and efficiency. 

TECHNICAL DESCRIPTION

Introduction 

The SciBot consists of two major components, the product itself and a supporting app. The first part, the product, is hand-sized scissors which will have the blades as well as the software inside. This new technology has been inspired by previously existing robots in the form of automatic vacuums and coffee machines. Figure 1.0 demonstrates the robotic vacuum sensors which inspired the design and concept of SciBot. Similar to those robots, the goal of this innovation is to simplify everyday tasks as much as possible with the help of technology. As the SciBot scissors are autonomous, they do not require much human intervention but instead function through functioning hardware and the detailed software compromised with mobile. The overall concept involves the use of software to scan the desired material onto the SciBot app, allowing the machine to gather data such as the dimensions and type of material. The app also includes features to give the user the choice of shape and design they expect to be cut on the material. The software is connected and installed in the machine which holds other hardware materials like the blade, and blade holder which cuts the required material. 

Hardware

The first part of the SciBot is the hardware. There are many subparts within the hardware, including the aluminum outer case, round blade, blade holder, hinge as well the rechargeable battery connected to the power button. The presence of small black wheels at the bottom of the machine allows the machine to move around the material without cutting unnecessary parts. In other words, the different subparts can be categorized as the aluminum materials, the blade, battery, and power, and lastly the wheels. 

1. Aluminum

The majority of the hardware of the SciBot is based on Aluminum 6061. Figure 3 shows an image of a sheet of aluminum which will be used to make the outer layer of the SciBot. As previously mentioned, this design will be a portable size, they would not be larger than the average palm size. Furthermore, this aluminum material will be used to make the blade cover which will store the blade when it is not being used. Lastly, the aluminum hinge is responsible for pulling the blade to the blade holder and rolling it down depending on the commands given by the software. 

2. The Blade 

The SciBot will have a round blade, as shown in Figure 5.0, which will be used to cut the material designated. The blade will be handled by the software, as it will be activated by the motor only when agreed by the software. This blade will be not as sharp as the material will be placed on a regular hard object; however, the motor will bring the speed which will allow to pace the cutting speed. 

3. Battery and the Power Button 

The rechargeable battery allows the user to keep on using SciBot, which contributes to the long shelf life. Figure 6.0 shows a sample battery which could be used for the SciBot product. The battery will have the same charging points as a laptop, that is lightening to USB. Although the charger will be an accessory to be purchased, users do have the opportunity to connect it to their laptop chargers based on its availability. The power button works as said and turns SciBot on and off.  

4. Wheels 

The wheels in the SciBot will be very small in size and will allow the product to move around and when informed by the software. The wheels share the purpose to avoid manual interference and let the SciBot make cuts whenever and wherever required by the software. 

Software 

As evident, the functionality of the majority of the hardware highly depends on the software, which will be placed inside the machine. There will be a second yet very significant component of the SciBot, which is the SciBot application. The purpose of this app is to allow the users to be able to make selections about the design and the location of the cut needed. The software itself is divided into two major sections: the scanner and the designer.  

The scanner will be the first thing which the user will experience as they open the SciBot app. The scanner will use the camera feature of the mobile/supported device to scan the material, which is expected to be placed on a flat and hard surface. The scanner will identify the dimensions of the material. In case of failure of identification, users have an opportunity to enter the dimensions on their own. Once this step is completed, the next step requires the users to identify the location the cut is desired. This step can be completed by either using the pre-designed shape or using a free-hand pen to make marks on the software. As the marks are confirmed, the SciBot product should be turned on and placed on the material for cutting. Based on the user-gained information, the SciBot will move around with blades out, whenever needed to cut, and will place the blades in the blade holder when needed to move, without making cuts. Lastly, the SciBot will be turned off after all the assigned cutting is completed. 

Conclusion 

The SciBot is a significant development to a current tool. It allows for maximum efficiency through its advanced software and hardware. Though the SciBot itself can be considered a developing idea, it has the foundation and purpose to be further advanced. It is needed not only in households for practicality but also in manual labor forces where workers heavily depend on tools like modern scissors. The SciBot is a prime example of modern innovations, prioritizing efficiency and practicality, made for the modern consumer.

Design, Time, Cost, & Materials 

The design of the SciBot is made to ensure that the scissors are easy to carry as well as easy to use. The automatic scissors have a dimension of 3.5 x 3.5 x 2 inches and are expected to weigh about 6.5 oz or 184.2 grams. This weight can be referenced in comparison to an iPhone 13 which weighs 6.1 oz or 174 grams (Apple). The outer or visible portion of the product will be made of Aluminum 6061 steel. This choice was made as 6061 Aluminum is easy to bend, which we will require to shape the outer side to a square and is water resistant (Jon, 2021). It is significant to ensure that the steel is water resistant as there are software and sensors which will be inside the SciBot, and water interference can damage the machine. It is expected that the steel will cost about $5 for each scissor when purchased in a bulk of 100+ pieces.  

Additionally, the outside will have 2 mini wheels, which will allow the scissors to move across the desired area for cutting. Each pair is expected to cost about $7-9 depending on the quantity. Furthermore, the round blade to cut will have a 200mm diameter and will cost about $3-4 each in bulk. The blade will be attached to a hinge which will bull the blades up to its designated blade holder, when there is no cutting required. The pulling hinge and blade holder will be made of the same Aluminum steel as the outer and will cost $2 in bulk. Inside the SciBot, there will be rechargeable batteries, similar to what are used in smartphones and laptops and each battery will cost $20. The chargers would be an accessory not provided due to affordability, but it will be a Type C and USB charger, which are used usually in households for charging devices. Lastly, the software itself will be inserted into the machine, but will not have an individual cost as it will all be developed separately. The software production is expected to be the most expensive aspect of the design, however, with bulk quantity machines being manufactured at once, it will be about $10 for each scissor.  

The SciBot will be built to a portable design that is accessible for the users to use daily and travel to different places when required. To ensure that the product is affordable to a larger population, the cost was minimized as much as possible. Inclusive of labor, manufacturing, and materials, the unit price for each SciBot will be $75. At this cost, users will have access to the SciBot scissors and the accompanying app for free. It is expected that about 100 SciBot Scissors will be made in a month, and will last for years and years, due to its rechargeable battery. 

At the cost of $75, SciBot hopes to provide a new generation of scissors for school, work, college, and factories with minimum effort and human interaction while providing the most error-proof cutting software. 

Works Cited 

Apple – iPhone 13 . Apple. (n.d.). Retrieved November 28, 2022, from https://www.apple.com/shop/buy-iphone/iphone-13  

Gaur, A. et al (n.d.). Scissors. Encyclopædia Britannica. Retrieved November 28, 2022, from https://www.britannica.com/technology/scissors  

Goyal, Saanavi. (2022). SciBot [Drawing]. 

Jackson, S. (2022, October 16). Shein factory employees are working 18-hour days for pennies per garment and washing their hair on lunch breaks because they have so little time off, new report finds. Business Insider. Retrieved November 28, 2022, from https://www.businessinsider.com/shein-factory-workers-18-hour-shifts-paid-low-wages-report-2022-10  

Jon. (2021, August 16). The main advantages of 6061 Aluminum. Thin Metal Sales. Retrieved November 28, 2022, from https://www.thinmetalsales.com/blog/the-main-advantages-of-6061-aluminum/  

Laser Scissors. Amazon. (n.d.). Retrieved November 28, 2022, from https://www.amazon.com/Stalwart-75-PT1022-Cordless-Cardboard-Rechargeable/dp/B0733HNKRZ  

Layton, J. (2005). Roomba Red []. How Stuff Works. https://electronics.howstuffworks.com/gadgets/home/robotic-vaccum 

Rauturier, S. (2022, June 23). What is fast fashion and why is it so bad? Good On You. Retrieved November 28, 2022, from https://goodonyou.eco/what-is-fast-fashion/  

Rotary Scissors with ergonomic bladeless design. slice. (n.d.). Retrieved November 28, 2022, from https://www.sliceproducts.com/products/rotary-scissors?utm_source=google&utm_medium=cpc&utm_term=&utm_content=&utm_campaign=17304683660&matchtype=&device=c&gclid=Cj0KCQiA1NebBhDDARIsAANiDD1rQRyxZMWgIvl_1SbxJx2L1Mf-TvQ6dPzoESnOm–gDFtP4e6WSCYaArHBEALw_wcB  

6061 aluminum sheet. Midwest Steel & Aluminum. (n.d.). Retrieved November 28, 2022, from https://www.midweststeelsupply.com/store/6061aluminumsheet

INTRODUCTION

The Roomba is part of a line of consumer robots sold by the firm iRobot. It uses its sensors to clean and navigate in indoor spaces, making it an autonomous vacuum cleaner. The firm, iRobot, founded by the Massachusetts Institute of Technology in 1990 designated its goal to make robots a practical part in the everyday routine of consumers. It was invented by inventor Joe Jonas, who was then employed by the firm and then later put on the market in 2002. Jonas recalls his personal inconvenience of having to clean his room and incorporated the challenge into the task assigned by the firm. He recalls the firm’s “Robot Olympics” challenge, where they were given a set of parts and had to create a robot with them. Though the initial prototype did not fully function, the concept was still developed. Since then, Roomba has been designed and upgraded in a variety of ways. The Roomba Red is an upgraded model of the original, having arrived in the market in 2006. 

INTERIOR

From a surface level analysis, the Roomba Red is 13 inches in diameter and 3.5 inches tall. It is charged by a nickel metal hybrid battery (NiMH) and lasts 3 hours on full charge, needing 7 hours to recharge. It should be noted that the newer models have been able to decrease the charging time to 4 hours. It moves with 2 wheels, the power alternating between wheels. To function, it uses 5 motors in total are used to drive. They include: one motor per wheel, one motor for the vacuum itself, a motor for the spinning side brush and one motor for the agitator assembly. 

NAVIGATION

The Roomba functions by first measuring the size of the room when it is turned on. It does so by using the infrared receiver which is shown on the top of the device. The device reportedly sends out an infrared signal and calculates the time it takes to bounce back to the receiver; this is then used to calculate how long it would take to clean the room. The device is also equipped with other sensors as a precaution for environmental changes. For example, the vacuum has four cliff sensors, which are used to avoid sudden changes in elevation, such as stairs. The device knows of this because as it is operating it is also constantly sending out signals and waiting for them to bounce back to the four receivers and if one does not get a response, all signals are lost, and the device knows to avoid those areas. Another factor of navigation are the bumpers and object sensors. Upon colliding into something in the environment, the bumpers on the sides of the device retract which informs the object sensors that there are obstacles. This is followed by simple movements of rotations and calculations until it finds a clear path again.

Another sensor accompanied by the bumpers is the wall sensor. This sensor is especially used by the device to develop a consistent layout of the room with the best performance. Meaning, the Roomba can follow and clean closely along walls and furniture without coming in contact with them. And by doing so continuously, the device creates a layout of the room that allows most coverage. The infrared receiver is also used to find the self-charging pod or station. When the vacuum is low on battery power, it uses the infrared receiver to find signals omitted by the self-charger, which the device follows and attaches to, to charge. 

CLEANING

The most important aspect of the device is its ability to clean effectively. The device consists of a three-part cleaning system: the spinning side brush, the agitator, and the vacuum. The two dirt sensors are crucial for this system.  

Firstly, the spinning side brush assists the device by reaching under places that the main hardware is not able to reach. It functions by spinning along the walls of objects pushing the dirt underneath the device to the vacuum. While it does so, another brush opposite the spinning side brush makes sure captured dirt does not leave out of reach and keeps it under the device for the vacuum.

The second part of the system focuses on the agitator. The agitator is located underneath the device and is made up of counter-rotating brushes that have the function of a broom. Instead of pushing dirt towards the vacuum, the agitator directs any debris it touches into the dirt bin.

Lastly, the vacuum functions as any vacuum are known to, by drawing up the dirt as the device moves along. Much like other vacuums, with this device the dirt bin must be emptied once it is full, there is no indicator that informs the bin is full. As for the aforementioned dirt sensors, they are located directly above the agitator. Unlike the other sensors which work in a cycle with infrared signals, the dirt sensors are acoustic and impact. Meaning, when the agitator contacts a sizable number of debris, the dirt sensors vibrate against the metal to tell the device to not move along to a new area and to instead clean the current area again. The purpose of this function is to clean as effectively as possible much like humans would when seeing an abundant amount of dirt in a certain area.  

CONCLUSION

The Roomba Red is an example of the subtle advancement of modern technology. It is not complicated to use but it is convenient and practical, and its components are also understandable rather than complex. Much like the prototype of the earliest model it has kept true to the concept of functionality while being small. A breakdown of the components is enough to depict details that make up the device because there are few components to begin with. This is a growing trend and goal in modern innovations, being able to significantly provide in small sizes so that these devices can be practical and not an inconvenience to the consumer. 

REFERENCES 

History | iRobot. (n.d.). Retrieved November 1, 2022, from https://about.irobot.com/History 

Layton, J. (2020, June 5). How Robotic Vacuums Work. HowStuffWorks. https://electronics.howstuffworks.com/gadgets/home/robotic-vacuum.htm 

Thomas Gounley, Springfield News-Leader. (2017, June 15). The inventor of the Roomba, a southwest Missouri native, is about to release a new robot. News-Leader. https://eu.news-leader.com/story/news/business/2017/06/09/inventor-roomba-southwest-missouri-native-release-new-robot/371868001/ 

Lab reports can serve as devices of communication that simplify the process of sharing and understanding different ideas. The reports, Database Oriented Big Data Analysis Engine Based On Deep Learning by Xiaoran Shang and Data Analysis of Educational Evaluation Using K-Means Clustering Method by Riu Liu, are examples of reports that follow a common structure. Shang’s report focuses on the use of a more efficient engine system to meet the needs of growing demand for cloud databases. Liu’s report focuses on improving systems in education by using an analysis model built by a specific method. Upon review, both reports show use of the eight elements of any lab report: abstract, introduction, materials and methods, results, discussion, conclusion, and references. These elements are incorporated into the reports in a way that best represents all the information the authors intend to include. Because of the difference in the scope of their research, the structures also show slight differences. Both reports include titles that are informative because they include the main subjects of the study and the authors’ ideas. The titles are effective because they are specific, Liu includes the subjects, data analysis, education, and clustering method, while Shang introduces databases and data analysis engine. They only include words necessary to depict their topics, straying from aesthetic. 

According to the textbook, the abstract aspect of a report is the summary of its major components, introduction, results, and conclusions as they answer the questions of the motivation behind the study, discoveries, and the implications of these findings (Stuart 2021, pg. 932). Both authors follow this sequence in their abstracts, however they also detail their method beyond stating their plans. Within the experimental design represented in the abstract, Liu breaks down the design by showing the main ideas of each stage of their plan, which includes construction of the model, calculating the weight of unit of measurement, the value of the weight and representing their data (2022, pg.1). Along with the summary of the introduction, results, and conclusions, this follows an informative abstract because the major results are presented and made clear after the detailed follow through of the experiment’s design. This contrasts with Shang’s abstract, which does not specify the major results of the study but only restates the purpose of the experiment. “… compared with the traditional data analysis engine system with character search as the core, the database oriented big data analysis engine system based on a deep learning model and wolf swarm greedy algorithm has faster response speed and intelligence” (2022, pg. 1). Shang’s discovery only states that their idea was correct, which follows a descriptive abstract, the less popular of the forms because it requires more time to examine the report. It is important to recognize the two different forms of abstract because of audience awareness. If the audience is not someone particularly invested in the study, a descriptive abstract would not prove to be satisfactory while an informative abstract displays all the major ideas and findings to the audience before requiring further reading. 

The introduction is required to show the purpose of the study and its potential contribution to the broader field. To build the validity of the research, the introduction is expected to include knowledge of existing research so that the audience is aware that what is being studied is significant and not redundant. Liu makes sure to build an understanding with the audience of the importance of the research by supplying background information on current systems used by educational institutions, presenting the clear problem that schools are not able to manage the growing and overwhelming range of information (2022, pg. 2). By supplying extensive background information, the research has meaning to audiences beyond experts in the field, including more people in support of the goal. This is supported with the background information of reasons as to why there is a continuing problem: current algorithms are not designed to handle the abundance of information; the use of the author’s method is supported here explaining it is appropriate because it is able to better algorithms significantly, “this paper introduces K-means clustering algorithm to analyze the education evaluation data…. address the current educational data’s long processing time, uncertain parameters, and low clustering quality…. approach is suitable for large-scale DM (Data Mining)” (Liu 2022, pg. 2). As well as defining important terms, the paper does not fail to provide detailed information ahead of the methods section and allows the audience to digest the necessary information beforehand.  

Comparing Shang’s report on analysis engines, the author also provides a significant amount of background information that contributes to the purpose of the study instead of being excessive. This is done by explaining the impact of cloud computing thus far as the author’s goal of developing more efficient algorithms for faster search engine responses relies on a strong cloud database (Shang 2022, pg. 2). The author supports the use of “deep learning strategy and wolf swarm greedy algorithm” with meaningful reasons from efficient structure to minimize cost. These atone to a wide range of audience as well, both experts, investors, and the general audience. As the textbook also mentions to include previous work in the introduction, the reports follow suit through a slightly different format where they include the information right after the introduction in a related works section. They develop upon the works of previous authors and discuss the findings and how they correlate with the current studies’ purpose. This goes beyond the textbooks teaching to make previous research known because this format is still effective and shows extensive knowledge and understanding. 

The two lab reports differ in their reported methods because they take different approaches. Whereas Liu develops upon processes and the correlation between the method and purpose, Shang’s approach is to examine and evaluate different approaches to show how their supported method is most appropriate. Shang’s evaluation of different methods is justified because the goal of the new algorithm is to relate and perform knowing as much human behavior as possible, which predecessors have not accomplished (2022, pg. 6). In contrast, Liu’s methodology sections consist of subsections that develop upon the relationship between the purpose and the technology before specifying the study’s methodology in full detail. Even though these aspects of the report are formatted differently from the textbook’s descriptions, they still achieve the goal of a methodology section. Liu’s report for example, describes in large detail the primary material used in the research, “The core function of DM technology is to discover potential rules from large-scale data. DM (data mining) is a specialized technology for mining extraordinary knowledge from large-scale data” (2022, pg. 6). With as much detail as they use, the primary reasons of the distinct aspects of a lab report are still respected and contribute to the organization of the authors’ ideas rather than serve as a distraction.

Whereas the textbook adheres to separating results and discussions, the reports both include the two sections together. In Liu’s report, the results are displayed and compared in a graph and simultaneously analyzed, “In terms of recall rate, this method also has certain advantages, and the recall rate of this method is better than the other two algorithms” (2022, pg. 14). The comparisons between the precisions of each algorithm add to the validity of the author’s argument for the method. Likewise, Shang’s results also support the purpose and methodology as the chosen algorithm showed comparative data that distinguished from other algorithms specifically in speed and accuracy (2022, pg. 12). 

The conclusion of a report serves as the final way of persuading the audience to validate the author’s ideas and experiment. It consists of a summary of the major aspects of the author’s argument and the author’s parting knowledge as now they know even more about the subject. Both authors follow the usual format of a conclusion. However, as aforementioned the reports once again exemplify going beyond the textbook example. In addition to analyzing and summarizing their experiments and background information, the reports also mention the limits of their findings. Liu’s report mentions factors such as time constraints and limited knowledge (2022, pg. 18) while Shang’s factors include limited abilities of the algorithm as well as a more detailed experimental design (2022, pg. 13). By including their own limitations, they deepen the integrity of the experiment and their findings as well as give direction for future studies. Both reports also credit all the works cited in their references in alphabetical order, which also adds to the integrity of their works. 

Analyzing lab reports, focusing primarily on the format is not a linear task. To understand the expectations of a valid report, the contents must be understood to a certain degree, especially because the format of a report relies on its content. Though the textbook format mentions the eight elements: title, abstract, introduction, materials and methods, results, discussion, conclusion, and references, the two reports are evident that there can be flexibility depending on the abundance of information. Although, the format should not be drastically different because the primary goal of the report itself is to serve as straightforward evidence of the author’s idea and communicate that to any audience. 

Bibliography 

1. Liu, R. (2022). Data Analysis of Educational Evaluation Using K-Means Clustering Method. Computational Intelligence and Neuroscience, 2022. https://link.gale.com/apps/doc/A712889874/CDB?u=cuny_ccny&sid=bookmark-CDB&xid=5cdf67bf 
2. Shang, X. (2022). Database Oriented Big Data Analysis Engine Based on Deep Learning. Computational Intelligence and Neuroscience, 2022. https://link.gale.com/apps/doc/A717005268/CDB?u=cuny_ccny&sid=bookmark-CDB&xid=bee83119